Pivotal Clinical Study to Evaluate the Efficacy and Safety of Assistive Artificial Intelligence-Based Software for Cervical Cancer Diagnosis
Seongmin Kim1, Hyonggin An2, Hyun-Woong Cho3, Kyung-Jin Min3, Jin-Hwa Hong3, Sanghoon Lee3, Jae-Yun Song3, Jae-Kwan Lee3, Nak-Woo Lee3
DOI : 10.3390/jcm12124024
Abstract
Colposcopy is the gold standard diagnostic tool for identifying cervical lesions. However, the accuracy of colposcopies depends on the proficiency of the colposcopist. Machine learning algorithms using an artificial intelligence (AI) system can quickly process large amounts of data and have been successfully applied in several clinical situations. This study evaluated the feasibility of an AI system as an assistive tool for diagnosing high-grade cervical intraepithelial neoplasia lesions compared to the human interpretation of cervical images. This two-centered, crossover, double-blind, randomized controlled trial included 886 randomly selected images. Four colposcopists (two proficient and two inexperienced) independently evaluated cervical images, once with and the other time without the aid of the Cerviray AI® system (AIDOT, Seoul, Republic of Korea). The AI aid demonstrated improved areas under the curve on the localization receiver-operating characteristic curve compared with the colposcopy impressions of colposcopists (difference 0.12, 95% confidence interval, 0.10–0.14, p < 0.001). Sensitivity and specificity also improved when using the AI system (89.18% vs. 71.33%; p < 0.001, 96.68% vs. 92.16%; p < 0.001, respectively). Additionally, the classification accuracy rate improved with the aid of AI (86.40% vs. 75.45%; p < 0.001). Overall, the AI system could be used as an assistive diagnostic tool for both proficient and inexperienced colposcopists in cervical cancer screenings to estimate the impression and location of pathologic lesions. Further utilization of this system could help inexperienced colposcopists confirm where to perform a biopsy to diagnose high-grade lesions.
Conclusions
An AI system could be used as an assistive diagnostic tool for both proficient and inexperienced colposcopists in cervical cancer screening to estimate not only the impression but also the exact location of pathologic lesions. AI interpretation of cervical images could be a beneficial assistive tool to be used in conjunction with human evaluation. Moreover, if additional supportive studies are conducted, it might be utilized as an alternative cost-effective diagnostic tool for evaluating high-grade cervical lesions, particularly in LMICs where proficient colposcopists are not fully available from lack of accessibility or cost. Therefore, further studies using various combinations of screening tools are warranted to determine the significance of AI systems in cervical cancer screening.
