Department of Pathology, Affiliated Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
AI Lab, Tencent, Shenzhen, China.
Cancer Cytopathol. 2021 Sep;129(9):693-700. doi: 10.1002/cncy.22425. Epub 2021 Apr 7.
Cervical cytology screening is usually laborious with a heavy workload and poor diagnostic consistency. The authors have developed an artificial intelligence (AI) microscope that can provide onsite diagnostic assistance for cervical cytology screening in real time.
A total of 2167 cervical cytology slides were selected from a cohort of 10,601 cases from Shenzhen Maternity and Child Healthcare Hospital, and the training data set consisted of 42,073 abnormal cervical epithelial cells. The recognition results of an AI technique were presented in a microscope eyepiece by an augmented reality technique. Potentially abnormal cells were highlighted with binary classification results in a 10× field of view (FOV) and with multiclassification results according to the Bethesda system in 20× and 40× FOVs. In addition, 486 slides were selected for the reader study to evaluate the performance of the AI microscope.
In the reader study, which compared manual reading with AI assistance, the sensitivities for the detection of low-grade squamous intraepithelial lesions and high-grade squamous intraepithelial lesions were significantly improved from 0.837 to 0.923 (P < .001) and from 0.830 to 0.917 (P < .01), respectively; the κ score for atypical squamous cells of undetermined significance (ASCUS) was improved from 0.581 to 0.637; the averaged pairwise κ of consistency for multiclassification was improved from 0.649 to 0.706; the averaged pairwise κ of consistency for binary classification was improved from 0.720 to 0.798; and the averaged pairwise κ of ASCUS was improved from 0.557 to 0.639.
The results of this study show that an AI microscope can provide real-time assistance for cervical cytology screening and improve the efficiency and accuracy of cervical cytology diagnosis.
宫颈细胞学筛查通常工作量大且费力，诊断一致性也较差。作者开发了一种人工智能（AI）显微镜，可实时为宫颈细胞学筛查提供现场诊断辅助。
从深圳市妇幼保健院的 10601 例患者中选择了 2167 例宫颈细胞学涂片，训练数据集由 42073 例异常宫颈上皮细胞组成。AI 技术的识别结果通过增强现实技术呈现在显微镜目镜中。在 10×视场（FOV）中，通过二进制分类结果突出显示可疑异常细胞；在 20×和 40×FOV 中，根据巴氏系统进行多分类结果。此外，选择了 486 张涂片进行读者研究，以评估 AI 显微镜的性能。
在读者研究中，将手动阅读与 AI 辅助进行比较，低级别鳞状上皮内病变和高级别鳞状上皮内病变的检测灵敏度分别从 0.837 显著提高到 0.923（P<0.001）和从 0.830 提高到 0.917（P<0.01）；非典型鳞状细胞不能明确意义（ASCUS）的κ评分从 0.581 提高到 0.637；多分类平均成对κ一致性从 0.649 提高到 0.706；二进制分类平均成对κ一致性从 0.720 提高到 0.798；ASCUS 的平均成对κ从 0.557 提高到 0.639。
该研究结果表明，AI 显微镜可为宫颈细胞学筛查提供实时辅助，并提高宫颈细胞学诊断的效率和准确性。
