External validation of deep learning-based bone-age software: a preliminary study with real world data
DC Field | Value | Language |
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dc.contributor.author | Lea, Winnah Wu-in | - |
dc.contributor.author | Hong, Suk-Joo | - |
dc.contributor.author | Nam, Hyo-Kyoung | - |
dc.contributor.author | Kang, Woo-Young | - |
dc.contributor.author | Yang, Ze-Pa | - |
dc.contributor.author | Noh, Eun-Jin | - |
dc.date.accessioned | 2022-02-22T15:42:24Z | - |
dc.date.available | 2022-02-22T15:42:24Z | - |
dc.date.created | 2022-02-15 | - |
dc.date.issued | 2022-01-24 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/136518 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | External validation of deep learning-based bone-age software: a preliminary study with real world data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Suk-Joo | - |
dc.identifier.doi | 10.1038/s41598-022-05282-z | - |
dc.identifier.scopusid | 2-s2.0-85123496124 | - |
dc.identifier.wosid | 000746700700020 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.12, no.1 | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
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