Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

被引：13

作者：

Li, Hao ^{[1
]}

Tao, Xiang ^{[1
]}

Liang, Tuo ^{[1
]}

Jiang, Jie ^{[1
]}

Zhu, Jichong ^{[1
]}

Wu, Shaofeng ^{[1
]}

Chen, Liyi ^{[1
]}

Zhang, Zide ^{[1
]}

Zhou, Chenxing ^{[1
]}

Sun, Xuhua ^{[1
]}

Huang, Shengsheng ^{[1
]}

Chen, Jiarui ^{[1
]}

Chen, Tianyou ^{[1
]}

Ye, Zhen ^{[1
]}

Chen, Wuhua ^{[1
]}

Guo, Hao ^{[1
]}

Yao, Yuanlin ^{[1
]}

Liao, Shian ^{[1
]}

Yu, Chaojie ^{[1
]}

Fan, Binguang ^{[1
]}

Liu, Yihong ^{[2
]}

Lu, Chunai ^{[2
]}

Hu, Junnan ^{[2
]}

Xie, Qinghong ^{[2
]}

Wei, Xiao ^{[2
]}

Fang, Cairen ^{[2
]}

Liu, Huijiang ^{[3
]}

Huang, Chengqian ^{[4
]}

Pan, Shixin ^{[5
]}

Zhan, Xinli ^{[1
]}

Liu, Chong ^{[1
]}

机构：

[1] Guangxi Med Univ, Affiliated Hosp 1, Nanning, Guangxi, Peoples R China

[2] Guangxi Med Univ, Nanning, Guangxi, Peoples R China

[3] Orthopaed First Peoples Hosp Nanning, Nanning, Guangxi, Peoples R China

[4] Orthopaed Peoples Hosp Baise, Baise, Guangxi, Peoples R China

[5] Orthopaed Wuzhou Red Cross Hosp, Wuzhou, Guangxi, Peoples R China

来源：

FRONTIERS IN PUBLIC HEALTH | 2023年 / 11卷

关键词：

artificial intelligence; deep learning; machine learning; ankylosing spondylitis; pelvic radiograph; TOTAL HIP-ARTHROPLASTY;

D O I：

10.3389/fpubh.2023.1063633

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

IntroductionThe diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. MethodsIn this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. ResultsThe ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. DiscussionIn this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

引用

页数：16

共 32 条

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