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. 2022 Apr 8:10:829372.
doi: 10.3389/fped.2022.829372. eCollection 2022.

A Study to Evaluate Accuracy and Validity of the EFAI Computer-Aided Bone Age Diagnosis System Compared With Qualified Physicians

Chi-Fung Cheng  1 , Ken Ying-Kai Liao  2 , Kuan-Jung Lee  2 , Fuu-Jen Tsai  3
Affiliations

A Study to Evaluate Accuracy and Validity of the EFAI Computer-Aided Bone Age Diagnosis System Compared With Qualified Physicians

Chi-Fung Cheng et al. Front Pediatr. .

Abstract

Study objectives: In previous research, we built a deep neural network model based on Inception-Resnet-v2 to predict bone age (EFAI-BAA). The primary objective of the study was to determine if the EFAI-BAA was substantially concordant with the qualified physicians in assessing bone ages. The secondary objective of the study was to determine if the EFAI-BAA was no different in the clinical rating (advanced, normal, or delayed) with the qualified physicians.

Method: This was a retrospective study. The left-hand X-ray images of male subjects aged 3-16 years old and female subjects aged 2-15 years old were collected from China Medical University Hospital (CMUH) and Asia University Hospital (AUH) retrospectively since the trial began until the included image amount reached 368. This was a blinded study. The qualified physicians who ran, read, and interpreted the tests were blinded to the values assessed by the other qualified physicians and the EFAI-BAA.

Results: The concordance correlation coefficient (CCC) between the EFAI-BAA (EFAI-BAA), the evaluation of bone age by physician in Kaohsiung Veterans General Hospital (KVGH), Taichung Veterans General Hospital (TVGH2), and in Taipei Tzu Chi Hospital (TZUCHI-TP) was 0.9828 (95% CI: 0.9790-0.9859, p-value = 0.6782), 0.9739 (95% CI: 0.9681-0.9786, p-value = 0.0202), and 0.9592 (95% CI: 0.9501-0.9666, p-value = 0.4855), respectively.

Conclusion: There was a consistency of bone age assessment between the EFAI-BAA and each one of the three qualified physicians (CCC = 0.9). As the significant difference in the clinical rating was only found between the EFAI-BAA and the qualified physician in TVGH2, the performance of the EFAI-BAA was considered similar to the qualified physicians.

Keywords: artificial intelligence; bone age assessment; clinical practice; concordance correlation coefficient (CCC); deep learning.

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Conflict of interest statement

KY-KL and K-JL were employed by Ever Fortune.AI Co., Ltd., Taichung, Taiwan. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of radiographs enrolled in the study.
See this image and copyright information in PMC

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