Development of an artificial intelligence-based diagnostic system for the detection of abnormal colposcopic findings.

Abstract

82 Background: When performing colposcopic examinations in patients with abnormal cervical cytology, sufficient training is required to detect cervical intraepithelial neoplasia (CIN) with (1) high diagnostic accuracy and (2) minimizing time and reducing tissue biopsies. The aim of this study was to develop an artificial intelligence (AI)-based system that replicates expert colposcopic examination techniques to (1) identify CIN lesions with high accuracy and to (2) guide tissue sampling locations with high diagnostic performance, independent of examiner skill. Methods: A retrospective analysis was performed using 8341 colposcopic videos from 2013 to 2019, consisting of seven early-stage cervical cancer cases, 203 CIN3 cases, 276 CIN2 cases, and 456 CIN1 cases. An AI-based lesion detection model was constructed by annotating abnormal colposcopic findings after acetic acid processing in cervical cancer and CIN3 cases whose histologic diagnoses were confirmed by biopsies. The developed lesion detection model was applied to CIN1 and CIN2 cases, and the diagnostic accuracies of the lesions were evaluated. The accuracies of the lesions of CIN1 and CIN2 were evaluated according to the lesion area (sensitivity, specificity, area under the curve (AUC)) and the number of lesions identified. Results: The AI-based model was trained on 60 cases of cervical cancer and CIN3 and validated on 150 cases. The model was able to identify severe lesions with high accuracy, with a sensitivity of 85%, a specificity of 73%, an AUC of 0.89 for lesion area, and an accuracy of 95% for the number of lesions identified. The model also predicted abnormal colposcopic findings in CIN1 and CIN2 cases with high accuracy for detection of lesion area (sensitivity: 87% and 86%, specificity: 70% and 67%, AUC: 0.81 and 0.81, respectively) and identification of the number of lesions (97% and 93%, respectively). Furthermore, a heatmap display based on lesion prediction allowed visualization of the area with the highest acetic acid intensity corresponding to the actual biopsy locations. Conclusions: We have developed an AI-based diagnostic system for colposcopy that can identify CIN lesions with high accuracy and suggest appropriate biopsy sites. We are currently establishing an AI-based application for the visualization of abnormal lesions in colposcopy developed from the model, and anticipating its implementation in the clinical practice of colposcopic examinations.