External validation of deep learning-based bone-age software: a preliminary study with real world data.
Sci Rep
; 12(1): 1232, 2022 01 24.
Article
em En
| MEDLINE
| ID: mdl-35075207
ABSTRACT
Artificial intelligence (AI) is increasingly being used in bone-age (BA) assessment due to its complicated and lengthy nature. We aimed to evaluate the clinical performance of a commercially available deep learning (DL)-based software for BA assessment using a real-world data. From Nov. 2018 to Feb. 2019, 474 children (35 boys, 439 girls, age 4-17 years) were enrolled. We compared the BA estimated by DL software (DL-BA) with that independently estimated by 3 reviewers (R1 Musculoskeletal radiologist, R2 Radiology resident, R3 Pediatric endocrinologist) using the traditional Greulich-Pyle atlas, then to his/her chronological age (CA). A paired t-test, Pearson's correlation coefficient, Bland-Altman plot, mean absolute error (MAE) and root mean square error (RMSE) were used for the statistical analysis. The intraclass correlation coefficient (ICC) was used for inter-rater variation. There were significant differences between DL-BA and each reviewer's BA (P < 0.025), but the correlation was good with one another (r = 0.983, P < 0.025). RMSE (MAE) values were 10.09 (7.21), 10.76 (7.88) and 13.06 (10.06) months between DL-BA and R1, R2, R3 BA. Compared with the CA, RMSE (MAE) values were 13.54 (11.06), 15.18 (12.11), 16.19 (12.78) and 19.53 (17.71) months for DL-BA, R1, R2, R3 BA, respectively. Bland-Altman plots revealed the software and reviewers' tendency to overestimate the BA in general. ICC values between 3 reviewers were 0.97, 0.85 and 0.86, and the overall ICC value was 0.93. The BA estimated by DL-based software showed statistically similar, or even better performance than that of reviewers' compared to the chronological age in the real world clinic.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Determinação da Idade pelo Esqueleto
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
Limite:
Adolescent
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Child
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Child, preschool
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Female
/
Humans
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Male
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2022
Tipo de documento:
Article