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A critical comparative study of the performance of three AI-assisted programs for bone age determination.

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Title: A critical comparative study of the performance of three AI-assisted programs for bone age determination.
Authors: Pape, Johanna1 (AUTHOR), Rosolowski, Maciej2 (AUTHOR), Pfäffle, Roland3 (AUTHOR), Beeskow, Anne B.4 (AUTHOR), Gräfe, Daniel1 (AUTHOR) Daniel.Graefe@medizin.uni-leipzig.de
Source: European Radiology. Mar2025, Vol. 35 Issue 3, p1190-1196. 7p.
Subjects: Artificial intelligence, Standard deviations, Artificial bones, Accounting exams, Age groups
Abstract: Objectives: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&P). Therefore, the current study aimed to compare the performance of three programs, namely BoneXpert, PANDA, and BoneView, on a single Central European population. Materials and methods: For this retrospective study, hand radiographs of 306 children aged 1–18 years, stratified by gender and age, were included. A subgroup consisting of the age group accounting for 90% of examinations in clinical practice was formed. The G&P BA was estimated by three human experts—as ground truth—and three AI-supported programs. The mean absolute deviation, the root mean squared error (RMSE), and dropouts by the AI were calculated. Results: The correlation between all programs and the ground truth was prominent (R2 ≥ 0.98). In the total group, BoneXpert had a lower RMSE than BoneView and PANDA (0.62 vs. 0.65 and 0.75 years) with a dropout rate of 2.3%, 20.3% and 0%, respectively. In the subgroup, there was less difference in RMSE (0.66 vs. 0.68 and 0.65 years, max. 4% dropouts). The standard deviation between the AI readers was lower than that between the human readers (0.54 vs. 0.62 years, p < 0.01). Conclusion: All three AI programs predict BA after G&P in the main age range with similar high reliability. Differences arise at the boundaries of childhood. Key Points: QuestionThere is a lack of comparative, independent validation for artificial intelligence-based bone age estimation in children. FindingsThree commercially available programs estimate bone age after Greulich and Pyle with similarly high reliability in a central European cohort. Clinical relevanceThe comparative study will help the reader choose a software for bone age estimation approved for the European market depending on the targeted age group and economic considerations. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A critical comparative study of the performance of three AI-assisted programs for bone age determination.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22European+Radiology%22&quot;&gt;European Radiology&lt;/searchLink&gt;. Mar2025, Vol. 35 Issue 3, p1190-1196. 7p.
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  Data: Objectives: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, according to Greulich and Pyle (G&amp;P). Therefore, the current study aimed to compare the performance of three programs, namely BoneXpert, PANDA, and BoneView, on a single Central European population. Materials and methods: For this retrospective study, hand radiographs of 306 children aged 1–18 years, stratified by gender and age, were included. A subgroup consisting of the age group accounting for 90% of examinations in clinical practice was formed. The G&amp;P BA was estimated by three human experts—as ground truth—and three AI-supported programs. The mean absolute deviation, the root mean squared error (RMSE), and dropouts by the AI were calculated. Results: The correlation between all programs and the ground truth was prominent (R2 ≥ 0.98). In the total group, BoneXpert had a lower RMSE than BoneView and PANDA (0.62 vs. 0.65 and 0.75 years) with a dropout rate of 2.3%, 20.3% and 0%, respectively. In the subgroup, there was less difference in RMSE (0.66 vs. 0.68 and 0.65 years, max. 4% dropouts). The standard deviation between the AI readers was lower than that between the human readers (0.54 vs. 0.62 years, p &lt; 0.01). Conclusion: All three AI programs predict BA after G&amp;P in the main age range with similar high reliability. Differences arise at the boundaries of childhood. Key Points: QuestionThere is a lack of comparative, independent validation for artificial intelligence-based bone age estimation in children. FindingsThree commercially available programs estimate bone age after Greulich and Pyle with similarly high reliability in a central European cohort. Clinical relevanceThe comparative study will help the reader choose a software for bone age estimation approved for the European market depending on the targeted age group and economic considerations. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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              Text: Mar2025
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