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基于深度学习的人工智能在髋臼指数测量中的应用。

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

作者信息

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

机构信息

Department of Pediatric Orthopedics, Anhui Provincial Children's Hospital, Hefei, China.

Fifth Clinical Medical College, Anhui Medical University, Hefei, China.

出版信息

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

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°,  < 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°,  = 0.126). The 95% LOA of the system was -0.93° to 2.86° (bias = -0.03°,  = 0.647). The 95% LOA of the clinician with the largest measurement error was -3.41° to 4.25° (bias = 0.42°,  < 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.

摘要

目的

构建一个用于测量髋臼指数并评估其临床应用准确性的人工智能系统。

方法

回顾性收集2014年4月至2018年12月我院共10219例标准骨盆前后位X线片。其中,随机选取9219例X线片用于训练和验证该系统。其余1000例X线片用于比较该系统与临床医生的测量结果。所有骨盆平片由专家委员会通过PACS系统按照统一标准进行标注以测量髋臼指数。随后,另外8名临床医生从测试X线片中随机选取200例独立测量髋臼指数。采用Bland-Altman检验对系统与临床医生测量结果进行一致性分析。

结果

测试集包括1000例(2000髋)。与专家委员会测量结果相比,该系统的95%一致性界限(95% LOA)为-4.02°至3.45°(偏差=-0.27°,<0.05)。根据Bland-Altman原理,该系统在所有年龄组(包括正常组和异常组)测量的髋臼指数也显示出良好的可信度。系统和8名临床医生与专家委员会测量评估结果的比较中,测量误差最小的临床医生的95% LOA为-2.76°至2.56°(偏差=-0.10°,=0.126)。该系统的95% LOA为-0.93°至2.86°(偏差=-0.03°,=0.647)。测量误差最大的临床医生的95% LOA为-3.41°至4.25°(偏差=0.42°,<0.05)。该系统的测量误差仅大于一名资深临床医生。

结论

新构建的人工智能系统能够快速、准确地测量标准骨盆前后位X线片的髋臼指数。该系统在测量标准骨盆前后位X线片时具有良好的数据一致性。该系统的准确性更接近资深临床医生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2bd/9891291/98b4365a39bf/fped-10-1049575-g001.jpg

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