The Performance of Artificial Intelligence in Cervical Colposcopy: A Retrospective Data Analysis.

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+.

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC8754610 | biostudies-literature |

REPOSITORIES: biostudies-literature