Yang Yihui, Xian Dongyi, Yu Lihua, Kong Yanqing, Lv Huaisheng, Huang Liujing, Liu Kai, Zhang Hao, Wei Weiwei, Tang Hongping
Department of Pathology, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China.
Medical Affairs Department, Guangzhou Betrue Technology Co. Ltd., Guangzhou, China.
Cytopathology. 2025 Mar;36(2):156-164. doi: 10.1111/cyt.13461. Epub 2024 Dec 8.
This study aimed to investigate the supporting role of artificial intelligence (AI) in digital cervical cytology training.
A total of 104 trainees completed both manual reading and AI-assisted reading tests following the AI-assisted digital training regimen. The interpretation scores and the testing time in different groups were compared. Also, the consistency, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of diagnoses were further analysed through the confusion matrix and inconsistency evaluation.
The mean interpretation scores were significantly higher in the AI-assisted group compared with the manual reading group (81.97 ± 16.670 vs. 67.98 ± 21.469, p < 0.001), accompanied by a reduction in mean interpretation time (32.13 ± 11.740 min vs. 11.36 ± 4.782 min, p < 0.001). The proportion of trainees' results with complete consistence (Category O) were improved from 0.645 to 0.803 and the averaged pairwise κ scores were improved from 0.535 (moderate) to 0.731 (good) with AI assistance. The number of correct answers, accuracies, sensitivities, specificities, PPV, NPV and κ scores of most class-specific diagnoses (NILM, Fungi, HSV, LSIL, HSIL, AIS, AC) also improved with AI assistance. Moreover, 97.8% (89/91) of trainees reported substantial improvement in cervical cytology interpretation ability, and all participants (100%, 91/91) expressed a strong willingness to integrate AI-assisted diagnosis into their future practice.
The utilisation of an AI-assisted digital cervical cytology training platform positively impacted trainee performance and received high satisfaction and acceptance among clinicians, suggesting its potential as a valuable adjunct to medical education.
本研究旨在探讨人工智能（AI）在数字宫颈细胞学培训中的辅助作用。
共有104名学员按照人工智能辅助数字培训方案完成了手动阅片和人工智能辅助阅片测试。比较了不同组别的解读分数和测试时间。此外，通过混淆矩阵和不一致性评估进一步分析了诊断的一致性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。
与手动阅片组相比，人工智能辅助组的平均解读分数显著更高（81.97±16.670对67.98±21.469，p<0.001），同时平均解读时间减少（32.13±11.740分钟对11.36±4.782分钟，p<0.001）。在人工智能辅助下，学员结果完全一致（O类）的比例从0.645提高到0.803，平均成对κ分数从0.535（中等）提高到0.731（良好）。大多数类别特异性诊断（NILM、真菌、HSV、LSIL、HSIL、AIS、AC）的正确答案数量、准确率、敏感性、特异性、PPV、NPV和κ分数在人工智能辅助下也有所提高。此外，97.8%（89/91）的学员报告宫颈细胞学解读能力有显著提高，所有参与者（100%，91/91）都表示强烈愿意将人工智能辅助诊断纳入未来的实践中。
利用人工智能辅助数字宫颈细胞学培训平台对学员表现产生了积极影响，并在临床医生中获得了高度满意度和接受度，表明其作为医学教育有价值辅助手段的潜力。
