{"title":"整合人工智能辅助宫颈细胞学数字化培训的比较研究。","authors":"Yihui Yang, Dongyi Xian, Lihua Yu, Yanqing Kong, Huaisheng Lv, Liujing Huang, Kai Liu, Hao Zhang, Weiwei Wei, Hongping Tang","doi":"10.1111/cyt.13461","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>This study aimed to investigate the supporting role of artificial intelligence (AI) in digital cervical cytology training.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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, <i>p</i> < 0.001), accompanied by a reduction in mean interpretation time (32.13 ± 11.740 min vs. 11.36 ± 4.782 min, <i>p</i> < 0.001). The proportion of trainees' results with complete consistence (Category O) were improved from 0.645 to 0.803 and the averaged pairwise <i>κ</i> 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 <i>κ</i> 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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":55187,"journal":{"name":"Cytopathology","volume":"36 2","pages":"156-164"},"PeriodicalIF":1.1000,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integration of AI-Assisted in Digital Cervical Cytology Training: A Comparative Study\",\"authors\":\"Yihui Yang, Dongyi Xian, Lihua Yu, Yanqing Kong, Huaisheng Lv, Liujing Huang, Kai Liu, Hao Zhang, Weiwei Wei, Hongping Tang\",\"doi\":\"10.1111/cyt.13461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>This study aimed to investigate the supporting role of artificial intelligence (AI) in digital cervical cytology training.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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, <i>p</i> < 0.001), accompanied by a reduction in mean interpretation time (32.13 ± 11.740 min vs. 11.36 ± 4.782 min, <i>p</i> < 0.001). The proportion of trainees' results with complete consistence (Category O) were improved from 0.645 to 0.803 and the averaged pairwise <i>κ</i> 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 <i>κ</i> scores of most class-specific diagnoses (NILM, Fungi, HSV, LSIL, HSIL, AIS, AC) also improved with AI assistance. 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引用次数: 0
摘要
目的：探讨人工智能（AI）在宫颈细胞学数字化培训中的辅助作用。方法：104名受训者在人工智能辅助的数字训练方案下完成了手动阅读和人工智能辅助阅读测试。比较各组口译成绩和测试时间。通过混淆矩阵和不一致评价进一步分析诊断的一致性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。结果：人工智能辅助组的平均口译分数明显高于手工阅读组（81.97±16.670 vs 67.98±21.469）。结论：人工智能辅助的数字宫颈细胞学培训平台的使用对实习生的表现有积极影响，临床医生对其有很高的满意度和接受度，表明其有潜力成为医学教育的有价值的辅助手段。
Integration of AI-Assisted in Digital Cervical Cytology Training: A Comparative Study
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.
期刊介绍：
The aim of Cytopathology is to publish articles relating to those aspects of cytology which will increase our knowledge and understanding of the aetiology, diagnosis and management of human disease. It contains original articles and critical reviews on all aspects of clinical cytology in its broadest sense, including: gynaecological and non-gynaecological cytology; fine needle aspiration and screening strategy.
Cytopathology welcomes papers and articles on: ultrastructural, histochemical and immunocytochemical studies of the cell; quantitative cytology and DNA hybridization as applied to cytological material.
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