Cytopathology : official journal of the British Society for Clinical Cytology · 2024
Integration of AI-Assisted in Digital Cervical Cytology Training: A Comparative Study.
СтатьяСравнительное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Yang Yihui, Xian Dongyi, Yu Lihua, Kong Yanqing, Lv Huaisheng, Huang Liujing, Liu Kai, Zhang Hao, Wei Weiwei, Tang Hongping
Аннотация
OBJECTIVE: This study aimed to investigate the supporting role of artificial intelligence (AI) in digital cervical cytology training.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.