Pediatric radiology · 2022
Validation of automated bone age analysis from hand radiographs in a North American pediatric population.
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Bowden Jonathan J, Bowden Sasigarn A, Ruess Lynne, Adler Brent H, Hu Houchun, Krishnamurthy Rajesh, Krishnamurthy Ramkumar
Аннотация
BACKGROUND: Radiographic bone age assessment by automated software is precise and instantaneous.
OBJECTIVE: The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
MATERIALS AND METHODS: We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R2).
RESULTS: Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R2=0.96; P<0.0001), with the mean bone age difference of 0.12±0.76 years. Bone age comparisons by the two methods remained strongly correlated (P<0.0001) when stratified by gender, common endocrine conditions including growth disorders and early/precocious puberty, and race. In the longitudinal analysis, we also found a strong correlation between the automated software and manual bone age over time (r=0.7852; R2=0.63; P<0.01).
CONCLUSION: Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.
OBJECTIVE: The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
MATERIALS AND METHODS: We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R2).
RESULTS: Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R2=0.96; P<0.0001), with the mean bone age difference of 0.12±0.76 years. Bone age comparisons by the two methods remained strongly correlated (P<0.0001) when stratified by gender, common endocrine conditions including growth disorders and early/precocious puberty, and race. In the longitudinal analysis, we also found a strong correlation between the automated software and manual bone age over time (r=0.7852; R2=0.63; P<0.01).
CONCLUSION: Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.