Evidence map›Paper›PMID 42565162›Full record

ArticleCureus2026

Comparative Analysis of Bone Age Estimation Using the Manual Greulich and Pyle Method Versus Artificial Intelligence Algorithms in an Indian Pediatric Population Aged 1-18 Years.

Prashant Onkar, Anushika Maloo, Suresh Phatak, Sandip Dhote, Kajal Mitra

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Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Prashant OnkarDepartment of Radiodiagnosis, NKP Salve Institute of Medical Science & Research Centre and Lata Mangeshkar Hospital, Nagpur, IND.
Anushika MalooDepartment of Radiodiagnosis, NKP Salve Institute of Medical Science & Research Centre and Lata Mangeshkar Hospital, Nagpur, IND.
Suresh PhatakDepartment of Radiodiagnosis, Datta Meghe Institute of Medical Sciences, Wardha, IND.
Sandip DhoteDepartment of Radiology, NKP Salve Institute of Medical Science & Research Centre and Lata Mangeshkar Hospital, Nagpur, IND.
Kajal MitraDepartment of Radiodiagnosis, NKP Salve Institute of Medical Science & Research Centre and Lata Mangeshkar Hospital, Nagpur, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBone age (BA) estimation is an essential tool in pediatric growth assessment and endocrine evaluation. The study aimed to compare the performance and agreement of three artificial intelligence (AI)-based bone age assessment software platforms with the manual Greulich and Pyle (GP) atlas method in an Indian pediatric population. MATERIALS AND

methodsSeventy-four hand radiographs from children aged 1-18 years were analyzed retrospectively. BA was assessed using the manual GP atlas and three automated AI software platforms: Xrayhead (AI1), 16-Bit Physis (AI2), and FreeBoneAge (AI3). Statistical evaluation included Pearson correlation, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Bland-Altman analysis to determine agreement and systematic bias between methods.

resultsAll AI platforms showed significant correlation with the manual GP atlas (p < 0.001). Among the evaluated systems, 16-Bit Physis demonstrated the strongest agreement with GP assessment (ICC = 0.983, 95% CI: 0.973-0.989), the narrowest limits of agreement (3.53 years), and the lowest AI-related mean absolute error (1.24 years). FreeBoneAge showed good agreement (ICC = 0.903) but wider limits of agreement, whereas Xrayhead demonstrated only moderate agreement (ICC = 0.729) and produced unreliable outputs in children younger than six years. The manual GP method achieved the lowest overall mean absolute error relative to chronological age (0.76 years).

conclusion16-Bit Physis AI demonstrated the most reliable agreement with the GP atlas across all age groups and both sexes, with clinically acceptable bias and narrow limits of agreement. Xrayhead showed critical limitations in young children.

Indexed as

artificial intelligencebone age assessmentgreulich and pyle atlashand radiographypediatric radiologyskeletal maturation

Identifiers

PMID42565162
PMCPMC13446377

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