Abstract
Objective We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions. Methods A retrospective and diagnostic study was conducted in Chongqing Cancer Hospital. Anonymized medical records with cytology, HPV testing, colposcopy findings with images, and the histopathological results were selected. The sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AI-assisted colposcopy, and the human colposcopists, respectively. Results Anonymized medical records from 346 women were obtained. The images captured under colposcopy of 194 women were found positive by the AI system; 245 women were found positive either by human colposcopists or the AI system. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs. 88.8%, p=0.016). The specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, p < 0.001) or the AI system (57.6%, p < 0.001). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.741, 0.765, and 0.674, respectively. In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs. 92.6%, p=0.13). The specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, p < 0.001) or compared with the AI system (56.6%, p < 0.001). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.759, 0.674, and 0.771, respectively. Conclusions The AI system provided equally matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. The AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+.
Highlights
Cervical cancer is a common malignant tumor among women
183 (52.89%) women were low-grade squamous intraepithelial lesion (LSIL) or worse; the images captured under colposcopy of 194 (56.07%) women were found positive by the artificial intelligence (AI) system; 245 women were found positive either by the human colposcopists or the AI system
In detecting CIN3+, the sensitivity of the human colposcopists, AI system, and AI-assisted colposcopy was 92.6%, 97.5%, and 97.5%. e sensitivity of the AI system and AI-assisted colposcopy was not significantly higher than human colposcopists (97.5% vs. 92.6%, p 0.13). e specificity of the human colposcopists, AI system, and AI-assisted colposcopy was 59.2%, 56.6%, and 37.4%, respectively. e specificity was significantly lower for AI-assisted colposcopy, compared with human colposcopists (37.4% vs. 59.2%, p < 0.001) or compared with the AI system (37.4% vs. 56.6%, p < 0.001)
Summary
We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions. E sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AIassisted colposcopy, and the human colposcopists, respectively. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs 88.8%, p 0.016). E specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, p < 0.001) or the AI system (57.6%, p < 0.001). In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs 92.6%, p 0.13). E specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, p < 0.001) or compared with the AI system (56.6%, p < 0.001). E AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+ Conclusions. e AI system provided matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. e AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+
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