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Comparing Assisting Technologies for Proficiency in Cardiac Morphology: 3D Printing and Mixed Reality versus CT Slice Images for Morphological Understanding of Congenital Heart Defects by Medical Students

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Title: Comparing Assisting Technologies for Proficiency in Cardiac Morphology: 3D Printing and Mixed Reality versus CT Slice Images for Morphological Understanding of Congenital Heart Defects by Medical Students
Language: English
Authors: Henrik Brun (ORCID 0000-0002-0432-1907), Matthias Lippert (ORCID 0000-0003-0259-292X), Thomas Langø (ORCID 0000-0002-2824-6120), Juan Sanchez-Margallo (ORCID 0000-0002-0100-2695), Francisco Sanchez-Margallo (ORCID 0000-0003-2138-988X), Ole Jakob Elle (ORCID 0000-0003-2359-1272)
Source: Anatomical Sciences Education. 2025 18(1):68-76.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 9
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Anatomy, Human Body, Spatial Ability, Visual Aids, Medical Students, Medical Education, Heart Disorders, Printing, Clinical Diagnosis, Student Attitudes, Congenital Impairments, Medicine, Computer Simulation
DOI: 10.1002/ase.2530
ISSN: 1935-9772
1935-9780
Abstract: Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students' understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1454916
Database: ERIC
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  Value: <anid>AN0181847206;[8z8k]01jan.25;2024Dec27.02:24;v2.2.500</anid> <title id="AN0181847206-1">Comparing assisting technologies for proficiency in cardiac morphology: 3D printing and mixed reality versus CT slice images for morphological understanding of congenital heart defects by medical students </title> <p>Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students′ understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images.</p> <p>Keywords: 3D‐imaging techniques; 3D‐printing; cardiac morphology; congenital heart defects; digital morphology; extended reality; learning technology</p> <hd id="AN0181847206-2">INTRODUCTION</hd> <p>Learning human anatomy highly depends on spatial imagination abilities. By experience, understanding cardiac anatomy is among the more spatially challenging tasks for medical students. Most students never get to understanding the detailed morphology of complex congenital heart defects, and this remains a lifelong learning topic even for pediatric cardiologists. The ability of spatial anatomical understanding, based on two‐dimensional visual input, such as textbook drawings and CT or MRI slice images, varies between individuals, but it does not seem to influence choice between surgical or medical residency areas.[<reflink idref="bib1" id="ref1">1</reflink>] The standard methods of learning normal cardiac anatomy, reading books, supplied by the study of human cardiac specimens, are less limited by specimen availability, although cadaveric hearts frequently are worn and torn by years of study use. However, for teaching congenital heart defect morphology, the specimen libraries are in decay, and in many places they are not reproducible, due to ethical restrictions and privacy rules for the protection of sensitive medical material. Recent advances in 3D medical imaging and visualization technologies have opened new avenues for medical education that may compensate for both variations in spatial abilities and availability of cadaveric material. Further, in specialties such as pediatric cardiology, congenital heart surgery, and craniofacial surgery, patient specific 3D models are becoming a requested part of the preoperative imaging plan and treatment decision basis. They also seem to be highly efficient educational material. This study explores and compares the potential benefits of incorporating 3D prints and augmented reality visualization technology (in our case using the HoloLens), providing a more immersive and interactive learning experience for medical students, in tasks normally considered beyond medical student level, such as understanding complex congenital heart defect morphology.</p> <hd id="AN0181847206-3">BACKGROUND</hd> <p>Medical education and later practice, especially in surgical specialties, depends on detailed gross anatomy knowledge, both of normality and pathology. Cardiac anatomy is known as a challenging part of the medical curriculum. Congenital heart defect morphology is a small part of basic medical education, but it is known to be one of the most challenging topics, with high demands for spatial abilities (visualization and orientation) and may be an area with special benefits from 3D modeling and visualization technologies.[<reflink idref="bib2" id="ref2">2</reflink>] A shared library of 3D models, (https://3d.nih.gov/collections/heart‐library) created from patient images was even initiated by NIH as a great educational asset, but the uploading of new models seems to have stagnated.[<reflink idref="bib3" id="ref3">3</reflink>] Several studies have examined the use of 3D visualization technology to improve understanding of CHD, but few compared the different methods and modalities with respect to learning output.</p> <p>While Karsenty et al.[<reflink idref="bib4" id="ref4">4</reflink>] showed that medical students profit extra from studying 3D prints in understanding both simple atrial septal defect (ASD) and moderately complex Tetralogy of Fallot (ToF) heart defects, White et al. found that for residents, this effect is relevant first at a moderate CHD complexity level like ToF.[<reflink idref="bib5" id="ref5">5</reflink>] Somewhat contrary, Loke et al. found that a 3D‐printed ToF model did not provide better knowledge compared to schematic drawings in residents and suggested that future studies include more complex heart defects. In a study including a wide range of heart defect complexities, pediatric residents and nurse practitioners had a clear learning benefit of 3D prints added to traditional drawings based teaching.[<reflink idref="bib6" id="ref6">6</reflink>] In an even more complex task, understanding the anatomy of atrioventricular septal defects, however, 3D models did not add to the understanding,[<reflink idref="bib7" id="ref7">7</reflink>] perhaps because at this detailed level, a combination of schematic drawings and naturalistic models is necessary for conceptualization and understanding.</p> <p>For virtual models, Kim et al. underscored the importance of degree of immersion in group discussions of CHD, especially for more complex defects.[<reflink idref="bib8" id="ref8">8</reflink>] Awori et al. showed that virtual reality (VR) was preferred over 3D printed models by pediatric residents and nurse practitioners in understanding ToF anatomy and pathophysiology, but they did not measure learning output objectively.[<reflink idref="bib9" id="ref9">9</reflink>]</p> <p>To our knowledge, this is the first study comparing both objective morphology understanding and subjective learning experiences between 3D printed models (3DP), mixed reality (MR) 3D visualization (MR3D), and 2D CT images (CT2D) at medical student level.</p> <p>In the present study, we propose that stereoscopic visualization and 3D printed models based on segmented CT images from patients with CHD provide a faster and more correct morphological understanding than standard 2D CT image data in complex CHD. We further explore potential differences between the teaching properties of 3DP and holograms for specific tasks, such as understanding internal (e.g., ventricular septal defect) and external (e.g., great arteries) cardiac anatomy.</p> <hd id="AN0181847206-4">METHODS</hd> <p>After e‐mails, postings on social media groups, and verbal invitation in classes, 29 medical students volunteered for this study, taking place at their campuses (Universities of Oslo, Trondheim and Tromso in Norway). Time slots for participation were distributed in the order students registered. Experiences in cardiac imaging or cardiology or extensive previous MR experience were considered exclusion criteria.</p> <p>Participation time was estimated to be approximately 1 h. Prior to test start, students watched an introductory YouTube video on normal cardiac CT images, in effort to align their knowledge on cardiac anatomy as visualized by slice images (https://youtu.be/NSHm‐fl5Vsg). For the 3DP tasks, a short verbal introduction was given to explain the concept of hollowed blood pool 3D printed models, and for the MR tasks, an introduction to the applied MR tools: sizing, manipulation, and cutting was provided as well. Each student performed three consecutive diagnostic tasks, one for each visualization modality (Figure 1), studying three different out of the five selected cardiac defects (Table 1). The combination of model and modality to be studied per task followed a scheme that ensured an equal number of demonstrations of each model and also equal distribution of visualization modalities among the models. This scheme also took into account that the order of the visualization methods should have equal variation. This was achieved by matching model one to five with modality one to three in looping systems running in parallel, with the participants entering the system as they appeared.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jan25/ase2530-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ase2530-fig-0001.jpg" title="1 Photo of mixed reality, 3D printed models and 2D CT images as used in the study." /> </p> <p></p> <p>1 TABLE Heart model diagnoses.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Model no.</th><th align="left">Diagnoses</th><th align="left">Surgery/interventions</th></tr></thead><tbody valign="top"><tr><td align="left">1</td><td align="left">TOF (extreme RVOTO), RPA stenosis, four arch vessels</td><td align="left">Central shunt from aorta to right pulmonary artery</td></tr><tr><td align="left">2</td><td align="left">TGA, PmVSD, LVOTO, PDA</td><td align="left" /></tr><tr><td align="left">3</td><td align="left">DORV, aorta right posterior "posterior TGA"), Subpulmonary VSD, HAA, CoA, PDA, ASD</td><td align="left">Balloon atrial septostomy performed (iatrogenic ASD)</td></tr><tr><td align="left">4</td><td align="left">Multiple VSDs (Pm + muscular VSDs)</td><td align="left">Banding of pulmonary artery. ASD secundum closure with Amplatzer device</td></tr><tr><td align="left">5</td><td align="left">Malalignment VSD. HAA. PDA</td><td align="left" /></tr></tbody></table> </ephtml> </p> <p>1 Abbreviations: ASD, atrial septal defect; CoA, coarctation of the aorta; DORV, double outlet right ventricle; HAA, hypoplasia of the aortic arch; LVOTO, left ventricular outflow tract obstruction; PDA, persistent ductus arteriosus; Pm, perimembranous; RVOTO, right ventricular outflow tract obstruction; TGA, transposition of the great arteries; TOF, tetralogy of Fallot; VSD, ventricular septal defect.</p> <p>A set of five different complex congenital heart malformations was selected from a larger database of an ongoing clinical study, for which cardiac CT images were processed as previously described in detail.[<reflink idref="bib10" id="ref10">10</reflink>] The anatomic diagnoses of the five cases used for this study are described in Table 1. Briefly, the patient‐specific 3D models were made, using an in‐house deep learning algorithm for cardiac CT image segmentation.[<reflink idref="bib10" id="ref11">10</reflink>] Following the AI‐derived segmentation output, the masks were manually edited (by ML) to achieve an expert approved (HB) segmentation of the blood pool, using the open source software 3D Slicer (<ulink href="http://www.slicer.org">http://www.slicer.org</ulink>).[<reflink idref="bib11" id="ref12">11</reflink>] Models and corresponding CT images were anonymized, keeping only the 3D meshes as. <emph>stl</emph> files for 3D printing and. <emph>ply</emph> files for MR visualization in the Microsoft HoloLens 2, using a heart mesh viewing app (TruHeart App H.S.1.0.0, Holocare, Oslo, Norway, software developed for research purposes). The corresponding cardiac CT datasets were viewed as. <emph>nii</emph> (NIfTI) files, showing axial slices only, with MedSeg, a web based in‐house image processing software (by Oslo University Hospital). Three‐dimensional printing was performed after 2 mm outward hollowing of the blood pool based meshes, using an UltiMaker 3D printer and semi flexible (Shore 95A) TPU filament and Polymaker PolyDissolve S1 support material.</p> <p>The answers to seven selected morphological detail questions and four Likert scale user experience ratings were collected using a one page fill in yourself questionnaire in Norwegian (available as Supplementary Material Online). Time from start task to completion in minutes was self‐registered by the stopwatch function on each student's smartphone. User experiences were reported as Likert scale ratings of task difficulty from 1 to 5, 1 being very easy and 5 very difficult, except for VSD localization where the scale was inverted (1 very difficult to 5 very easy). The same questionnaire was used for all five cardiac morphology tasks and for all visualization methods. After all three tasks were completed, the overall preferred visualization modality was asked for.</p> <hd id="AN0181847206-6">RESULTS</hd> <p>In this study, 29 medical students, aged median 22 (range 19–30) years, of whom 25 were female, were included. Academic level was median 4th (range 2nd–6th) year of medical studies. Total time spent for each participant was approximately 1 h. One participant reported cyber‐sickness after the mixed reality study, albeit finished the task. No other unpleasant effects were reported.</p> <hd id="AN0181847206-7">Objective measurements</hd> <p>Mean (SD) time spent per task was 11.2 (4.3) min. There was no difference between time spent for task one, two, and three (<emph>p</emph> = 0.9 by one‐way ANOVA). Split on modality, mean 3D print task time was 10.4 (3.7) min, MR task time 11.6 (5.1) min and 2D axial CT time 11.8 (4.0). The slightly lower time spent for 3D print analysis as compared to the other modalities was not significant by one‐way ANOVA (<emph>p</emph> = 0.16).</p> <p>As shown in the violin plot (Figure 2), the percentage of morphology questions answered correctly ranged from mean 46% (SD 27%) for 3DP to 62.7% (SD 25.3%) for MR3D. MR3D resulted in significantly better results than 3DP (<emph>p</emph> = 0.009) and a trend was seen comparing MR3D to slice images (<emph>p</emph> = 0.06), whereas the difference between 3DP and CT2D was not significant (<emph>p</emph> = 0.2). Correction for potential intra‐participant dependencies in a mixed models analysis resulted in an estimated difference of 16.6% points (SE 6.5, <emph>p</emph> = 0.014) for the benefit of holograms as compared to 3D printed models. Students in early years of study (years 2 and 3) had a slightly lower percentage of correct answers with a mean (SD) of 50.5 (4.2) per cent, compared to late in study (years 4 to 6) with 56.7 (3.8) per cent, but this was not significant (<emph>p</emph> = 0.16, one‐sided <emph>t</emph>‐test). There was no difference in the percentage of correct answers between task one, two and three by one‐way ANOVA (<emph>p</emph> = 0.77).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jan25/ase2530-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ase2530-fig-0002.jpg" title="2 Percentages of correct answers for the three visualization methods. ** Significant by one‐way ANOVA." /> </p> <p></p> <p>Table 2 shows the separate morphology questions results split on the different visualization modalities. A significant benefit for MR was demonstrated for diagnosis of right ventricular outflow tract and pulmonary artery obstructions, and a weak trend for similar findings on the left side of the heart. No significant differences were found between modalities in understanding and reporting VSD number and location. In more detail, while there were 8/30 correct VSD locations with 3DP, an additional five students found one (out of two present) VSD's correctly located by 3DP, but still counted as false answers. Lastly, counted as incorrect answers, 27 of the 29 students answered "I don't know" at 54 of the 203 occasions altogether.</p> <p>2 TABLE Number of correct answers out of total for each morphology question.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">CT 2D axial</th><th align="left">3D print</th><th align="left">MR hologram</th><th align="left">Pearson Chi‐square</th></tr></thead><tbody valign="top"><tr><td align="left">Aortic origin</td><td align="left">15/28</td><td align="left">15/30</td><td align="left">21/29</td><td align="left">0.18</td></tr><tr><td align="left">LVOT/aortic narrowing</td><td align="left">21/28</td><td align="left">17/30</td><td align="left">23/29</td><td align="left">0.13</td></tr><tr><td align="left">Pulmonary artery origin</td><td align="left">20/28</td><td align="left">17/30</td><td align="left">22/29</td><td align="left">0.255</td></tr><tr><td align="left">RVOTO/pulmonary artery narrowing</td><td align="left">4/28</td><td align="left">9/30</td><td align="left">15/29</td><td align="left">0.01</td></tr><tr><td align="left">VSD number</td><td align="left">15/28</td><td align="left">19/30</td><td align="left">17/29</td><td align="left">0.75</td></tr><tr><td align="left">VSD location</td><td align="left">12/28</td><td align="left">8/30</td><td align="left">11/29</td><td align="left">0.416</td></tr><tr><td align="left">Great arteries relation</td><td align="left">14/25</td><td align="left">13/27</td><td align="left">17/26</td><td align="left">0.45</td></tr><tr><td align="left">Total correct % (SD)</td><td align="left">52.0 (25.9)</td><td align="left">46.0 (27.3)</td><td align="left">62.7 (25.3)</td><td align="left">See Figure 2</td></tr></tbody></table> </ephtml> </p> <p>2 Abbreviations: LVOTO, left ventricular outflow tract obstruction; RVOTO, right ventricular outflow tract obstruction; VSD, ventricular septal defect.</p> <hd id="AN0181847206-9">Subjective measurements</hd> <p>User experiences were reported as Likert scale ratings and overall modality preference. Table 3 shows that when asking for ease of morphological understanding, all over, MR3D was assessed as performing better than 2DCT and 3DP. Split on topics, holograms were found easiest for assessing great artery origin and presence of outflow stenosis and had a similar trend for VSD localization. However, only 3DP was experienced as significantly better than CT slice images for VSD localization.</p> <p>3 TABLE Mean and SD of Likert values (1 very easy to 5 very difficult) for evaluation of task complexity (values for VSD localization are inverted: 1 very difficult to 5 very easy).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Task</th><th align="left">Modality</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left">SD</th><th align="left"><italic>p</italic> Value one‐way ANOVA (LSD)</th></tr></thead><tbody valign="top"><tr><td align="left">Great artery origin</td><td align="left">CT 2D axial</td><td align="char" char=".">27</td><td align="char" char=".">3.96</td><td align="char" char=".">1.02</td><td align="left" /></tr><tr><td align="left">3D print</td><td align="char" char=".">28</td><td align="char" char=".">3.21</td><td align="char" char=".">1.13</td><td align="char" char="(">0.13 (vs.CT)</td></tr><tr><td align="left">Hologram</td><td align="char" char=".">28</td><td align="char" char=".">2.11</td><td align="char" char=".">1.13</td><td align="char" char="("><0.01 (vs. 3DP)</td></tr><tr><td align="left">VSD localization</td><td align="left">CT 2D axial</td><td align="char" char=".">27</td><td align="char" char=".">2.59</td><td align="char" char=".">1.37</td><td align="left" /></tr><tr><td align="left">3D print</td><td align="char" char=".">28</td><td align="char" char=".">3.54</td><td align="char" char=".">1.17</td><td align="char" char="(">0.012 (vs. CT)</td></tr><tr><td align="left">Hologram</td><td align="char" char=".">28</td><td align="char" char=".">3.57</td><td align="char" char=".">1.53</td><td align="char" char="(">0.09 (vs. CT)</td></tr><tr><td align="left">Stenosis</td><td align="left">CT 2D axial</td><td align="char" char=".">27</td><td align="char" char=".">4.00</td><td align="char" char=".">1.00</td><td align="left" /></tr><tr><td align="left">3D print</td><td align="char" char=".">28</td><td align="char" char=".">3.86</td><td align="char" char=".">1.11</td><td align="char" char="(">0.63 (vs. CT)</td></tr><tr><td align="left">Hologram</td><td align="char" char=".">28</td><td align="char" char=".">2.61</td><td align="char" char=".">1.17</td><td align="char" char="("><0.01 (vs. CT and 3DP)</td></tr><tr><td align="left">Overall difficulty (see Figure 3)</td><td align="left">CT 2D axial</td><td align="char" char=".">28</td><td align="char" char=".">4.2</td><td align="char" char=".">0.63</td><td align="left" /></tr><tr><td align="left">3D print</td><td align="char" char=".">28</td><td align="char" char=".">3.7</td><td align="char" char=".">0.66</td><td align="char" char="(">0.07 (vs. CT)</td></tr><tr><td align="left">Hologram</td><td align="char" char=".">28</td><td align="char" char=".">3.2</td><td align="char" char=".">0.83</td><td align="char" char="(">0.019 (vs. 3DP)</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note</emph>: Bold <emph>p</emph> values indicate a statistically significant difference (<0.05).</p> <p>The overall preferred modality after completion of all three tasks was (number of students preferring this modality): CT scan: 1 (this participant experienced cyber‐sickness with the HoloLens), 3D‐printed model: 6, Holographic model: 22, as illustrated in the diagram below (Figure 3).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/8Z8K/01jan25/ase2530-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ase2530-fig-0003.jpg" title="3 Overall preferred modality." /> </p> <p></p> <p>As a final curiosum, the tasks performed by students at University of Oslo had a slight trend toward a higher mean (SD) percentage of correct answers at 56.1 (25.5) % versus 49.5 (27.8) %, although without reaching statistical significance (<emph>p</emph> = 0.128).</p> <hd id="AN0181847206-11">DISCUSSION</hd> <p>In this within‐group comparison of visualization tools, using mixed methods design, comparing new methods of 3D visualization of CHD morphology to standard 2D CT images, we found that medical students:</p> <p></p> <ulist> <item> Described CHD morphology more correctly after studying mixed reality 3D models (MR3D) as compared to 3D prints (3DP), with a similar trend when compared to 2D CT slice images (2DCT).</item> <p></p> <item> Found CHD morphology tasks easier with MR3D than 3DP and 2DCT.</item> <p></p> <item> Overall preferred MR3D to the other two modalities, for studying CHD morphology.</item> <p></p> <item> Benefited most from MR3D when studying outflow tract stenosis and great artery origin and less for VSD localization.</item> </ulist> <hd id="AN0181847206-12">General considerations—Choice of study group and methods</hd> <p>The study population was female dominated with 86% female participants. This partly reflects the female student share in Norwegian medical schools at present (around 70%), but apparently also some overrepresentation. This could create a gender bias, both for the measured morphological understanding and modality preferences. Care must be taken that findings may be less relevant for males, as spatial abilities may have a gender determined distribution.</p> <p>Looking at the rather low percentage of correct answers over all (46%–63%), the chosen morphologies and related tasks seem to have been quite challenging for the study group. The chosen task complexity level was based on published work assessing simpler defects, not being able to identify knowledge benefits of 3D visualization and concluding that more complex anatomies should be studied.[<reflink idref="bib7" id="ref13">7</reflink>] The idea was that 3D visualization could have larger benefits with higher anatomic complexity. However, with around half of the answers being wrong, one could speculate that in our study, the complexity level was a bit too high for the academic level, and that differences could have been more prominent with slightly easier tasks. This was indirectly supported by the somewhat higher scores for higher level students, although this did not reach significance. Due to the small study size, we have not analyzed in more detail for differences based on year of study but studied the group, knowing that all have passed their cardiac anatomy module, but that only a subset has read pediatrics. This was deemed acceptable as complex congenital heart defects are not part of the pediatrics curriculum of their corresponding universities.</p> <p>One hour of challenging morphology study may be fatiguing, resulting in less effort put into the last task, possibly skewing data. However, the order of modalities was randomized to compensate for such effects, and there was no difference in the time spent or correctness percentage between task one, two and three. Lastly, the time spent per task, 11.3 min on average, seems enough, both to study the heart and answer the limited one‐page questionnaire, not asking for a complete morphological description, but for specific details, to enable clear distinctions between right and wrong answers. I do not know option was frequently used, perhaps as an expression of respect for the clinical importance of the decision.</p> <hd id="AN0181847206-13">Visualization modality—High‐level comparison/overall views</hd> <p>Both 3D printing and AR/VR technology have penetrated deeply into medical education and practice, starting at the turn of the millennium. Notably, CHD was an early use area. A clinical value of 3D printed models in facilitation of surgical planning for complex cases has been demonstrated.[<reflink idref="bib12" id="ref14">12</reflink>] The aim of our study was to find the best visualization tool for the understanding of complex cardiac defect morphology at medical student level. Previous studies have focused mainly on the students' subjective learning experience, and only a few included objective measurements of educational output and spatial conceptualization. In a controlled study of learning impact by 3DP in medical students, Su et al. found that 3DP helps medical students in understanding simple CHD (VSD).[<reflink idref="bib13" id="ref15">13</reflink>] In the present study, MR3D visualization outperformed 3DP as modality at an overall level. The higher objective performance seen in cardiac morphology understanding by MR3D was underscored by the students' own subjective impression, stating that MR3D was the easiest learning tool in general. A modality evaluation based on subjective preference only could easily tend toward favoring the most spectacular, that is, mixed reality, but when supported by hard data, the result appears more credible. Additionally, this finding points to the value of students′ feedback on teaching experiences with new approaches in learning anatomy.</p> <p>A big difference between MR3D and 3DP is the option for cutting into the region of interest repeatedly and interacting with the image data in real 3D. Although not systematically registered, the students used the 3D models much in the same manner as in surgical planning, by cutting into the 3D volume repeatedly from different angles. This way of using the MR3D provides a combination of the 3D properties (the model) and the 2D data (the slice plane) to be processed simultaneously by the viewer. As repeated slicing in data is also possible by scrolling in the 2DCT, the higher scores for MR3D point to a specific benefit from the three dimensionality in combination with the dynamic fashion of use compared to the rather hard 3D printed models available from standard 3D printers such as the UltiMaker. Soft materials that may provide easier intracardiac examinations still require high‐end poly‐jet 3D printers but fortunately 3D‐printing technology develops rapidly, making soft models more affordable in few years' time.</p> <p>Standard medical teaching includes study of generic, simplified figures and heart models as an entry level for ease of understanding. We chose to use patient specific data for all three modalities to: (A) study the feasibility of patient data transformed by new technology in teaching medical students about complex anatomy, and (B) compare the educational value of the visualization modalities directly to each other. From a didactical point, this can be discussed, as standardized figures are easily available for students in books and online and simplification may facilitate understanding. In medical working life, however, real image data is what students meet and optimally understand based on their training, so an earlier introduction of patient‐based data facilitated by new visualization technology could be a step forward, preparing students better for clinical work. Further, as seen in our study, even the 2DCT images performed quite well, with a trend toward a better objective score than 3D prints in total. This somewhat intriguing finding may partly be related to that 2DCT was the only modality that was given a 10‐min separate video instruction before study start, and that as mentioned, the 3D print quality was determined by the affordability of the 3D printer.</p> <hd id="AN0181847206-14">Visualization modality—Task‐specific comparison</hd> <p>Determining what type of tasks benefit most from which modality of visualization can bring knowledge about the features that generate such benefits. To investigate differences in optimal modality for different anatomical task types, we therefore included intracardiac and extracardiac details to be assessed by the students with the different modalities.</p> <p>Few previous studies have assessed the effects of 3D visualization on objective scores of understanding of substructures of heart defects. Patel et al. indicated that atrioventricular septal defect morphology learning can be equally easily achieved on desktop computers as by studying 3D models.[<reflink idref="bib14" id="ref16">14</reflink>] AV‐valves are typically assessed by ultrasound and our way of segmenting CT images does not allow for a detailed analysis of AV‐valves, so this could not be confirmed. In our study, the benefit of mixed reality viewing was most pronounced for the students in the understanding of extracardiac structures such as great artery relationship and tubular narrowing, as the presence of outflow tract stenosis. For great artery origin determination, the user experience was rated higher than 2DCT for MR3D and with a trend also for 3DP with similar trends for correct answers on the topic. This is in contrast to typical clinical image interpretation challenges that are sought to be solved by 3D‐modeling, such as the intracardiac relation between ventricular septal defects and outflow tracts.[<reflink idref="bib2" id="ref17">2</reflink>] This may indicate that 3D visualization tools may have different strengths in education and clinical use.</p> <p>Somewhat intriguingly, students in the present study subjectively assessed 3DP (trending also for MR3D) as being a better tool than 2DCT for localizing VSD's, whereas by objective measurement, the results trended to be better with 2DCT images than 3DP (<emph>p</emph> = NS). Part of this can be related to that detecting VSD in semi‐flexible 3D prints as used in this study is easier for some locations (perimembranous and mid muscular VSDs) but more difficult for others (marginal, apical), obviously depending on how the 3D print is cut open. Our models were standard cut parallel to the diaphragmatic surface, cutting both AV‐valves in two halves making VSDs located dorsally on the septum toward the RVOT a bit harder to see. The hardness of the material used in our study may have introduced some limitations to discovering eccentric located VSDs when compared to available softer 3DP materials. The latter, however, frequently create problems in the printing process and have lower durability with use.</p> <p>Summarized, for the VSDs that the students discovered, probing the hole with an instrument may be a concrete and very satisfactory experience, but only a subset of the VSDs were easy to find and locate in this way, missing out on quite a few. For extracardiac and tubular structures, the 3D modalities seem to have the most pronounced benefits for medical student teaching, perhaps related to these structures being easier to understand in general, thus being the most adequate task for this academic level.</p> <hd id="AN0181847206-15">Implementation</hd> <p>Bringing new medical imaging technology into a teaching or clinical community can be challenging. Implementation success partly reflects the benefits of the technology, but other factors such as availability and ease of use play important roles. Urlings et al. identified obstacles to the integration of 3D visualization technologies in clinical medicine, finding that although many studies have proven their benefits in both teaching and planning, implementation is hampered by practicalities and inconveniences.[<reflink idref="bib15" id="ref18">15</reflink>] Interestingly, in a study of patient specific 3D models of atrioventricular septal defects, VR headset visualization was compared to flat screen rendering of the same models and no learning benefit was seen, apart from a better user experience. A discussion was raised whether immature technology with unstable software and complex user interface outweighs the benefits of true three dimensional perception.[<reflink idref="bib14" id="ref19">14</reflink>] In our experience, another main challenge in clinical implementation is medical device regulation rules that restrict use to research purposes for a long time until these extensive and highly expensive regulatory processes are funded and completed. For medical student and postgraduate teaching, the increasing availability of affordable 3D printers and VR/MR headsets with acceptable quality level should make 3D visualization an attractive tool for learning anatomy and studying typical clinical cases. This idea is supported by the overall positive attitude to MR3D among the participants in the present study. Experience with these new tools during training hopefully make future specialists more ready to exploit them optimally for patient treatment when clinically approved.</p> <hd id="AN0181847206-16">Limitations</hd> <p>Small sample size with possible selection bias toward students with special interest for imaging technology, but none reported special experiences with VR/MR. Most of the volunteers were female, possibly making the results of the study have less impact on men, acknowledging sex differences in spatial abilities. Applying patient‐specific imaging data in medical student study could make results less valid for residents and fellow academic levels. This was sought to be compensated by the video tutorial for cardiac CT data. 3D print model quality is dependent on availability and budget for obtaining models, which sets a limit for what we were able to produce for the study. Time measurement reliability may be reduced by the self‐timing protocol, but this should be equal for all tasks, thus not creating bias.</p> <hd id="AN0181847206-17">CONCLUSIONS</hd> <p>In this within‐group comparison of visualization tools, using mixed methods design, testing different ways of learning complex congenital heart defects morphology, mixed reality three‐dimensional visualization was found to be the overall preferred modality, and this also provided the best anatomical understanding. With tasks adapted to the level of education, new 3D visualization techniques may provide useful tools for medical education and training. However, it should be remembered that, MR3D may be an important complimentary, but not a mutually exclusive visualization modality, to the other existing modalities.</p> <hd id="AN0181847206-18">AUTHOR CONTRIBUTIONS</hd> <p> <bold>Henrik Brun:</bold> Conceptualization; data curation; formal analysis; investigation; methodology; visualization; writing – original draft; writing – review and editing. <bold>Matthias Lippert:</bold> Data curation; methodology; software; visualization; writing – original draft; writing – review and editing. <bold>Thomas Langø:</bold> Conceptualization; funding acquisition; investigation; methodology; project administration; writing – original draft; writing – review and editing. <bold>Juan Sanchez‐Margallo:</bold> Conceptualization; data curation; funding acquisition; investigation; methodology; project administration; resources; software; validation; visualization; writing – review and editing. <bold>Francisco Sanchez‐Margallo:</bold> Conceptualization; funding acquisition; investigation; methodology; project administration; resources; writing – review and editing. <bold>Ole Jakob Elle:</bold> Conceptualization; data curation; formal analysis; funding acquisition; methodology; project administration; resources; software; supervision; visualization; writing – review and editing.</p> <hd id="AN0181847206-19">ACKNOWLEDGMENTS</hd> <p>This study was funded by the Erasmus+ program of the European Union (MIREIA: Mixed Reality in medical Education based on Interactive Applications Project; Reference: 621668‐EPP‐1‐2020‐1‐ES‐EPPKA2‐KA). Thanks to Celine Krefting, Oslo University Hospital for 3D printing the models.</p> <p>GRAPH: Data S1.</p> <ref id="AN0181847206-20"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Langlois J, Wells GA, Lecourtois M, Bergeron G, Yetisir E, Martin M. Spatial abilities of medical graduates and choice of residency programs. Anat Sci Educ. 2015 ; 8 (2): 111 – 119.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Yoo S‐J, Thabit O, Kim EK, Ide H, Yim D, Dragulescu A, et al. 3D printing in medicine of congenital heart diseases. 3D Print Med. 2016 ; 2 (1): 3.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">3</bibl> <bibtext> Bramlet M, Olivieri L, Farooqi K, Ripley B, Coakley M. Impact of three‐dimensional printing on the study and treatment of congenital heart disease. Circ Res. 2017 ; 120 (6): 904 – 907.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref4" type="bt">4</bibl> <bibtext> Karsenty C, Guitarte A, Dulac Y, Briot J, Hascoet S, Vincent R, et al. The usefulness of 3D printed heart models for medical student education in congenital heart disease. BMC Med Educ. 2021 ; 21 (1): 480.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref5" type="bt">5</bibl> <bibtext> White SC, Sedler J, Jones TW, Seckeler M. Utility of three‐dimensional models in resident education on simple and complex intracardiac congenital heart defects. Congenit Heart Dis. 2018 ; 13 (6): 1045 – 1049.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref6" type="bt">6</bibl> <bibtext> Tarca A, Woo N, Bain S, Crouchley D, McNulty E, Yim D. 3D printed cardiac models as an adjunct to traditional teaching of anatomy in congenital heart disease—a randomised controlled study. Heart Lung Circ. 2023 ; 32 (12): 1443 – 1450.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref7" type="bt">7</bibl> <bibtext> Loke YH, Harahsheh AS, Krieger A, Olivieri LJ. Usage of 3D models of tetralogy of Fallot for medical education: impact on learning congenital heart disease. BMC Med Educ. 2017 ; 17 (1): 54.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref8" type="bt">8</bibl> <bibtext> Kim B, Loke Y‐H, Mass P, Irwin MR, Capeland C, Olivieri L, et al. A novel virtual reality medical image display system for group discussions of congenital heart disease: development and usability testing. JMIR Cardio. 2020 ; 4 (1): e20633.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref9" type="bt">9</bibl> <bibtext> Awori J, Friedman SD, Howard C, Kronmal R, Buddhe S. Comparative effectiveness of virtual reality (VR) vs 3D printed models of congenital heart disease in resident and nurse practitioner educational experience. 3D Print Med. 2023 ; 9 (1): 2.</bibtext> </blist> <blist> <bibtext> Nainamalai V, Lippert M, Brun H, Elle OJ, Kumar RP. Local integration of deep learning for advanced visualization in congenital heart disease surgical planning. Intell Based Med. 2022 ; 6 : 100055.</bibtext> </blist> <blist> <bibtext> Gering DT, Nabavi A, Kikinis R, Hata N, O'Donnell LJ, Grimson WEL, et al. An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging. 2001 ; 13 (6): 967 – 975.</bibtext> </blist> <blist> <bibtext> Valverde I, Gomez‐Ciriza G, Hussain T, Suarez‐Mejias C, Velasco‐Forte MN, Byrne N, et al. Three‐dimensional printed models for surgical planning of complex congenital heart defects: an international multicentre study. Eur J Cardiothorac Surg. 2017 ; 52 (6): 1139 – 1148.</bibtext> </blist> <blist> <bibtext> Su W, Xiao Y, He S, Huang P, Deng X. Three‐dimensional printing models in congenital heart disease education for medical students: a controlled comparative study. BMC Med Educ. 2018 ; 18 (1): 178.</bibtext> </blist> <blist> <bibtext> Patel N, Costa A, Sanders SP, Ezon D. Stereoscopic virtual reality does not improve knowledge acquisition of congenital heart disease. Int J Cardiovasc Imaging. 2021 ; 37 (7): 2283 – 2290.</bibtext> </blist> <blist> <bibtext> Urlings J, de Jong G, Maal T, Henssen D. Views on augmented reality, virtual reality, and 3D printing in modern medicine and education: a qualitative exploration of expert opinion. J Digit Imaging. 2023 ; 36 (4): 1930 – 1939.</bibtext> </blist> </ref> <aug> <p>By Henrik Brun; Matthias Lippert; Thomas Langø; Juan Sanchez‐Margallo; Francisco Sanchez‐Margallo and Ole Jakob Elle</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> <p></p> <p>Henrik Brun, M.D, Ph.D., is pediatric cardiologist with subspecialty in pediatric echocardiography and head of the pediatric echo lab at the national center for pediatric cardiology and heart surgery in Oslo, Norway. His research topics are 3D modeling and holographic visualization in structural heart disease, digital twins and artificial intelligence in cardiology and cardic interventions, telemedicine and robotics.</p> <p>Matthias Lippert, M.D, Ph.D., is German specialist in cardiology and Norwegian cardiology resident. He is highly proficient in AI‐enabled cardiac image segmentation and is currently delivering his PhD in digital anatomic twins in surgical planning for structural heart disease at the University of Oslo.</p> <p>Thomas Langø, is Chief Scientist in medical technology at St. Olavs hospital and SINTEF Digital, Dept. Health Research, Trondheim, Norway. He leads several R&D projects at SINTEF and is the scientific leader for the Norwegian National Research Center for Minimally Invasive and Image‐Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital. Additionally, he is the scientific leader of NorTrials Medical Devices. He serves on the National Committee for Medical and Health Research Ethics of Norway and supervises multiple PhD candidates in medical technology.</p> <p>Juan A. Sánchez‐Margallo, Ph.D., is a Senior Researcher at the Bioengineering and Health Technologies Unit of the Jesús Usón Minimally Invasive Surgery Center in Cáceres (Spain). His research interests include surgical robotics, artificial intelligence in the medical field, advanced technologies for surgical training, medical devices and surgical planning and assistance systems.</p> <p>Francisco M. Sánchez Margallo, is the Scientific Director of the Jesús Usón Minimally Invasive Surgery Centre ‐JUMISC‐ and Assistant Director of the Singular Scientific Spanish infrastructure named NANBIOSIS (<ulink href="http://www.nanbiosis.com">www.nanbiosis.com</ulink>). He is Project Manager for minimally invasive techniques with responsibility regarding biomaterials, medical devices, robotic‐assisted surgery, new techniques and imaging diagnosis in laparoscopic, endoscopic and other minimally invasive procedures and technologies.</p> <p>Ole Jakob Elle, is professor of robotics at Dept of Informatics, University of Oslo and head of Section for medical cybernetics and image processing at the Intervention Centre, Oslo University Hospital. He has led and leads large national and international research projects within AI‐based medical image processing. soft tissue navigation, mixed reality applications, robotics and telemedicine. He has supervised and supervises multiple PhD candidates in medical technology.</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref10"></nolink> <nolink nlid="nl2" bibid="bib11" firstref="ref12"></nolink> <nolink nlid="nl3" bibid="bib12" firstref="ref14"></nolink> <nolink nlid="nl4" bibid="bib13" firstref="ref15"></nolink> <nolink nlid="nl5" bibid="bib14" firstref="ref16"></nolink> <nolink nlid="nl6" bibid="bib15" firstref="ref18"></nolink>
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  Data: Comparing Assisting Technologies for Proficiency in Cardiac Morphology: 3D Printing and Mixed Reality versus CT Slice Images for Morphological Understanding of Congenital Heart Defects by Medical Students
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Henrik+Brun%22">Henrik Brun</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0432-1907">0000-0002-0432-1907</externalLink>)<br /><searchLink fieldCode="AR" term="%22Matthias+Lippert%22">Matthias Lippert</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0259-292X">0000-0003-0259-292X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Thomas+Langø%22">Thomas Langø</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2824-6120">0000-0002-2824-6120</externalLink>)<br /><searchLink fieldCode="AR" term="%22Juan+Sanchez-Margallo%22">Juan Sanchez-Margallo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0100-2695">0000-0002-0100-2695</externalLink>)<br /><searchLink fieldCode="AR" term="%22Francisco+Sanchez-Margallo%22">Francisco Sanchez-Margallo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2138-988X">0000-0003-2138-988X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ole+Jakob+Elle%22">Ole Jakob Elle</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2359-1272">0000-0003-2359-1272</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Anatomical+Sciences+Education%22"><i>Anatomical Sciences Education</i></searchLink>. 2025 18(1):68-76.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 9
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  Data: 2025
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Anatomy%22">Anatomy</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Body%22">Human Body</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+Ability%22">Spatial Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+Aids%22">Visual Aids</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+Students%22">Medical Students</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+Education%22">Medical Education</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+Disorders%22">Heart Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Printing%22">Printing</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Diagnosis%22">Clinical Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Congenital+Impairments%22">Congenital Impairments</searchLink><br /><searchLink fieldCode="DE" term="%22Medicine%22">Medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Simulation%22">Computer Simulation</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/ase.2530
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  Data: 1935-9772<br />1935-9780
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students' understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images.
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  Label: Entry Date
  Group: Date
  Data: 2024
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  Label: Accession Number
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  Data: EJ1454916
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        Value: 10.1002/ase.2530
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      – Text: English
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      Pagination:
        PageCount: 9
        StartPage: 68
    Subjects:
      – SubjectFull: Anatomy
        Type: general
      – SubjectFull: Human Body
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      – SubjectFull: Spatial Ability
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      – SubjectFull: Visual Aids
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      – SubjectFull: Printing
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      – SubjectFull: Clinical Diagnosis
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      – SubjectFull: Student Attitudes
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      – SubjectFull: Congenital Impairments
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      – TitleFull: Comparing Assisting Technologies for Proficiency in Cardiac Morphology: 3D Printing and Mixed Reality versus CT Slice Images for Morphological Understanding of Congenital Heart Defects by Medical Students
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