Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs.
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| Title: | Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs. |
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| Authors: | Hendrix, Nils1,2 (AUTHOR) nils.hendrix@radboudumc.nl, Hendrix, Ward1,3 (AUTHOR), Maresch, Bas4 (AUTHOR), van Amersfoort, Job5 (AUTHOR), Oosterveld-Bonsma, Tineke4 (AUTHOR), Kolderman, Stephanie4 (AUTHOR), Vestering, Myrthe4 (AUTHOR), Zielinski, Stephanie5 (AUTHOR), Rutten, Karlijn1 (AUTHOR), Dammeier, Jan1 (AUTHOR), Ong, Lee-Ling Sharon2,6 (AUTHOR), van Ginneken, Bram1 (AUTHOR), Rutten, Matthieu1,3 (AUTHOR) |
| Source: | European Radiology. Oct2024, Vol. 34 Issue 10, p6600-6613. 14p. |
| Subjects: | Artificial intelligence, Radiographs, Radiography, Sensitivity & specificity (Statistics), Standard deviations |
| Abstract: | Objectives: To develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs. Materials and methods: Two case-control datasets of hand and wrist radiographs were retrospectively acquired at three hospitals (hospitals A, B, and C). Dataset 1 (2178 radiographs from 1993 patients, hospitals A and B, 2018–2019) was used for developing an AI system for measuring scapholunate (SL) joint distances, SL and capitolunate (CL) angles, and carpal arc interruptions. Dataset 2 (481 radiographs from 217 patients, hospital C, 2017–2021) was used for testing, and with a subsample (174 radiographs from 87 patients), an observer study was conducted to compare its performance to five clinicians. Evaluation metrics included mean absolute error (MAE), sensitivity, and specificity. Results: Dataset 2 included 258 SL distances, 189 SL angles, 191 CL angles, and 217 carpal arc labels obtained from 217 patients (mean age, 51 years ± 23 [standard deviation]; 133 women). The MAE in measuring SL distances, SL angles, and CL angles was respectively 0.65 mm (95%CI: 0.59, 0.72), 7.9 degrees (95%CI: 7.0, 8.9), and 5.9 degrees (95%CI: 5.2, 6.6). The sensitivity and specificity for detecting arc interruptions were 83% (95%CI: 74, 91) and 64% (95%CI: 56, 71). The measurements were largely comparable to those of the clinicians, while arc interruption detections were more accurate than those of most clinicians. Conclusion: This study demonstrates that a newly developed automated AI system accurately measures and detects signs of carpal instability on conventional radiographs. Clinical relevance statement: This system has the potential to improve detections of carpal arc interruptions and could be a promising tool for supporting clinicians in detecting carpal instability. Key Points: Artificial intelligence can accurately measure and detect signs of carpal instability on conventional radiographs. It measures scapholunate distances, scapholunate and capitolunate angles, and detects carpal arc interruptions. The measurement accuracy was largely comparable to five clinicians with different specialties, and the arc interruption detections were more accurate than most clinicians. All measurements and detections are explainable, and a new heat map technique was developed for visualizing the degree and location of carpal arc interruptions in the image. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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