Journal Article

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

Liu, Zhihua, Li, Qing, Chen, Jiawei, Wang, Wei, Zhao, Fanghui, Ng, Man Tat Alexander, Xue, Peng, Li, Yucong, Ma, Kai, Shen, Yu, Zhang, Xun, Zhao, Yuqian, Li, Yuexiang, Zheng, Yefeng, Qian, Tianyi, Tang, Chao, Li, Longyu, Wu, Jianrong, Zhang, Shaokai, Zhang, Wenhua, Qiao, Youlin, Cui, Xiaoli, Jiang, Yu

2020

Overview

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. Conclusions The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Adult / Aged / Agreements / Algorithms / Artificial Intelligence / Biomedicine / Biopsy / Biopsy - methods / Biopsy - statistics & numerical data / Cancer / Cancer screening / Carcinoma, Squamous Cell - diagnosis / Carcinoma, Squamous Cell - pathology / Carcinoma, Squamous Cell - prevention & control / Cellular biology / Cervical cancer / Cervical cancer prevention / Cervix / Cervix dysplasia / Cervix uteri / China - epidemiology / Colposcopy / Colposcopy - methods / Colposcopy - statistics & numerical data / Colposcopy diagnosis and biopsy / Data Accuracy / Datasets / Diagnosis / Diagnostic systems / Diagnostic Tests, Routine - methods / Early Detection of Cancer - methods / Early Detection of Cancer - statistics & numerical data / Female / Global elimination of cervical cancer / Health aspects / Human papillomavirus / Humans / Medical diagnosis / Medicine / Medicine & Public Health / Middle Aged / Neoplasm Grading - methods / Pathology / Patients / Performance evaluation / Predictive Value of Tests / Pregnancy / Quality control / Reproducibility of Results / Research Article / Technology application / Uterine Cervical Neoplasms - diagnosis / Uterine Cervical Neoplasms - pathology / Uterine Cervical Neoplasms - prevention & control / Womens health / Young Adult