A Study to Evaluate Accuracy and Validity of the EFAI Computer-Aided Bone Age Diagnosis System Compared With Qualified Physicians - Cheng, Chi-Fung; Liao, Ken Ying-Kai; Lee, Kuan-Jung; Tsai, Fuu-Jen (Frontiers in Pediatrics, 2022)
Study Objectives: In previous research, we built a deep neural network model (EFAI-BAA) based on Inception-Resnet-v2 to predict bone age. 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 to 16 years old and female subjects aged 2 to 15 years old were collected from China Medical University (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 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.