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

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...

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Autores principales: Wu, Qingjie, Ma, Hailong, Sun, Jun, Liu, Chuanbin, Fang, Jihong, Xie, Hongtao, Zhang, Sicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891291/
https://www.ncbi.nlm.nih.gov/pubmed/36741093
http://dx.doi.org/10.3389/fped.2022.1049575
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author Wu, Qingjie
Ma, Hailong
Sun, Jun
Liu, Chuanbin
Fang, Jihong
Xie, Hongtao
Zhang, Sicheng
author_facet Wu, Qingjie
Ma, Hailong
Sun, Jun
Liu, Chuanbin
Fang, Jihong
Xie, Hongtao
Zhang, Sicheng
author_sort Wu, Qingjie
collection PubMed
description 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.
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spelling pubmed-98912912023-02-02 Application of deep-learning–based artificial intelligence in acetabular index measurement Wu, Qingjie Ma, Hailong Sun, Jun Liu, Chuanbin Fang, Jihong Xie, Hongtao Zhang, Sicheng Front Pediatr Pediatrics 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. Frontiers Media S.A. 2023-01-16 /pmc/articles/PMC9891291/ /pubmed/36741093 http://dx.doi.org/10.3389/fped.2022.1049575 Text en © 2023 Wu, Ma, Sun, Liu, Fang, Xie and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pediatrics
Wu, Qingjie
Ma, Hailong
Sun, Jun
Liu, Chuanbin
Fang, Jihong
Xie, Hongtao
Zhang, Sicheng
Application of deep-learning–based artificial intelligence in acetabular index measurement
title Application of deep-learning–based artificial intelligence in acetabular index measurement
title_full Application of deep-learning–based artificial intelligence in acetabular index measurement
title_fullStr Application of deep-learning–based artificial intelligence in acetabular index measurement
title_full_unstemmed Application of deep-learning–based artificial intelligence in acetabular index measurement
title_short Application of deep-learning–based artificial intelligence in acetabular index measurement
title_sort application of deep-learning–based artificial intelligence in acetabular index measurement
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891291/
https://www.ncbi.nlm.nih.gov/pubmed/36741093
http://dx.doi.org/10.3389/fped.2022.1049575
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