Application of deep-learning-based artificial intelligence in acetabular index measurement

Qingjie Wu; Hailong Ma; Jun Sun; Chuanbin Liu; Jihong Fang; Hongtao Xie; Sicheng Zhang

doi:10.3389/fped.2022.1049575

Application of deep-learning-based artificial intelligence in acetabular index measurement

Front Pediatr. 2023 Jan 16:10:1049575. doi: 10.3389/fped.2022.1049575. eCollection 2022.

Authors

Qingjie Wu^{1

2}, Hailong Ma¹, Jun Sun^{1

2}, Chuanbin Liu³, Jihong Fang¹, Hongtao Xie³, Sicheng Zhang^{1

2}

Affiliations

¹ Department of Pediatric Orthopedics, Anhui Provincial Children's Hospital, Hefei, China.
² Fifth Clinical Medical College, Anhui Medical University, Hefei, China.
³ School of Information Science and Technology, University of Science and Technology of China, Hefei, China.

Abstract

Objective: To construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application.

Methods: A total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9,219 radiographs were randomly selected to train and verify the system. The remaining 1,000 radiographs were used to compare the system's and the clinicians' measurement results. All plain pelvic films were labeled by an expert committee through PACS system based on a uniform standard to measure acetabular index. Subsequently, eight other clinicians independently measured the acetabular index from 200 randomly selected radiographs from the test radiographs. Bland-Altman test was used for consistency analysis between the system and clinician measurements.

Results: The test set included 1,000 cases (2,000 hips). Compared with the expert committee measurement, the 95% limits of agreement (95% LOA) of the system was -4.02° to 3.45° (bias = -0.27°, P < 0.05). The acetabular index measured by the system within all age groups, including normal and abnormal groups, also showed good credibility according to the Bland-Altman principle. Comparison of the measurement evaluations by the system and eight clinicians vs. that of, the expert committee, the 95% LOA of the clinician with the smallest measurement error was -2.76° to 2.56° (bias = -0.10°, P = 0.126). The 95% LOA of the system was -0.93° to 2.86° (bias = -0.03°, P = 0.647). The 95% LOA of the clinician with the largest measurement error was -3.41° to 4.25° (bias = 0.42°, P < 0.05). The measurement error of the system was only greater than that of a senior clinician.

Conclusion: The newly constructed artificial intelligence system could quickly and accurately measure the acetabular index of standard anteroposterior pelvic radiographs. There is good data consistency between the system in measuring standard anteroposterior pelvic radiographs. The accuracy of the system is closer to that of senior clinicians.

Keywords: DDH; acetabular index; artificial intelligence - AI; child; deep learning.

Grants and funding

This work was supported by the National Natural Science Foundation of China (Reference number: 61976008), the Natural Science Foundation of Anhui Province (Reference numbers: 2208085QH241 and 1808085MH288), and Science and Technology Project of Anhui Province (Reference number: 202007d07050006).