Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies
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RESEARCH ARTICLE
Open Access
Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies Peng Xue1,2† , Chao Tang3†, Qing Li4†, Yuexiang Li5, Yu Shen6, Yuqian Zhao7, Jiawei Chen5, Jianrong Wu8, Longyu Li9, Wei Wang10, Yucong Li11, Xiaoli Cui12, Shaokai Zhang13, Wenhua Zhang2, Xun Zhang14, Kai Ma5, Yefeng Zheng5, Tianyi Qian8, Man Tat Alexander Ng8, Zhihua Liu15, Youlin Qiao1,2 , Yu Jiang1* and Fanghui Zhao2*
Abstract Background: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. Methods: Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. Results: The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p < 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9– 62.9%; all p < 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; all p > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. (Continued on next page)
* Correspondence: [email protected]; [email protected] † Peng Xue, Chao Tang and Qing Li contributed equally to this work. 1 Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China 2 Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative C
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