| 叶子晨,杨怡晖,徐炼,韦荣干,阮细玲,薛鹏,江宇,乔友林.人工智能辅助诊断系统在宫颈细胞病理检查中的诊断性能评价[J].中华流行病学杂志,2025,46(3):499-505 |
| 人工智能辅助诊断系统在宫颈细胞病理检查中的诊断性能评价 |
| Diagnostic performance evaluation of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination |
| 收稿日期:2024-07-11 出版日期:2025-03-14 |
| DOI：10.3760/cma.j.cn112338-20240711-00412 |
| 中文关键词: 人工智能 宫颈癌 细胞病理学 外部验证 人机辅助 |
| 英文关键词: Artificial intelligence Cervical cancer Cytopathology External validation Human-machine assistance |
| 基金项目:博士后研究人员计划（GZB20230076）；中国博士后科学基金（2024T170072）；中国医学科学院医学与健康科技创新工程（2021-I2M-1-004） |
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| 中文摘要: |
| 目的 评价人工智能辅助诊断系统在宫颈细胞病理检查中的诊断性能。方法 回顾性收集4家医院的宫颈细胞病理切片数据，对人工智能辅助诊断系统进行外部验证，然后利用前瞻性数据进行人机辅助研究。结果 在回顾性研究中，共收集了3 162名有效病例作为外部验证数据集，人工智能辅助诊断系统的曲线下面积（AUC）为0.890（95%CI：0.878~0.902），准确性为0.885（95%CI：0.873~0.896），灵敏度为0.928（95%CI：0.914~0.941），特异度为0.852（95%CI：0.834~0.867）。在前瞻性研究中，共收集了212名有效病例，5名低年资医生参与了人机辅助研究。医生独立诊断的AUC为0.686（95%CI：0.650~0.722），准确性为0.699（95%CI：0.671~0.727），灵敏度为0.653（95%CI：0.599~0.703），特异度为0.719（95%CI：0.685~0.750），Fleiss κ值为0.510，阅片时间为223 s。在人工智能辅助诊断系统辅助下，医生的AUC、准确性、灵敏度和特异度分别提高了0.166、0.143、0.225和0.107，Fleiss κ值为0.730，阅片时间减少了188 s，差异有统计学意义（均P<0.001）。结论 人工智能辅助诊断系统的诊断性能优异，具有良好的泛化能力，且能显著提高低年资医生的诊断准确性、一致性和工作效率，可作为低年资医生在临床实践中的辅助工具。 |
| 英文摘要: |
| Objective To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination. Methods Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies. Results In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95%CI: 0.878-0.902), accuracy of 0.885 (95%CI: 0.873-0.896), sensitivity of 0.928 (95%CI: 0.914-0.941), and specificity of 0.852 (95%CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95%CI: 0.650-0.722), the accuracy was 0.699 (95%CI: 0.671-0.727), the sensitivity was 0.653 (95%CI: 0.599-0.703), the specificity was 0.719 (95%CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice. |
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