Department of Pathology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zhengzhou Key Laboratory of Gynecological Disease's Early Diagnosis, Zhengzhou, China.
Cancer Cytopathol. 2024 Nov;132(11):723-730. doi: 10.1002/cncy.22896. Epub 2024 Aug 19.
AICyte has previously demonstrated a potential role in cervical cytology screening for reducing the workload by using a 50% negative cutoff value. The aim of the current study is to evaluate this hypothesis.
The authors used the Ruiqian WSI-2400 (with the registered trademark AICyte) to evaluate a collection of 163,848 original cervical cytology cases from 2018 to 2023 that were collected from four different hospital systems in China. A breakdown of cases included 46,060 from Shenzhen, 67,472 from Zhengzhou, 25,667 from Shijiazhuang, and 24,649 from Jinan. These collected cases were evaluated using the AICyte system, and the data collected were statistically compared with the original interpretative results.
In 98.80% of all artificial intelligence cases that were designated as not needing further review, the corresponding original diagnosis was also determined to be negative. For any cases that were designated atypical squamous cells, cannot exclude high-grade squamous intraepithelial lesion or higher, the sensitivity and negative predictive value were 90.77% and 98.80%, respectively. The sensitivity and negative predictive value were greater in cases designated as low-grade squamous intraepithelial lesion or higher at 98.92% and 99.94%, respectively. Of the 49 low-grade squamous intraepithelial lesion or higher that were designed by AICyte as not needing further review, the cytohistologic correlation revealed eight cases of cervical intraepithelial neoplasia 1 and 18 negative cases; and the remaining cases were without histologic follow-up. In practice, AICyte used at a 50% negative cutoff value could reduce the anticipated workload if a protocol were implemented to label cases that qualified within the negative cutoff value as not needing further review, thereby finalizing the case as negative for intraepithelial lesions and malignancy.
For pathologic practices that do not have cytotechnologists or in which the workflow is sought to be optimized, the artificial intelligence system AICyte alone to be an independent screening tool by using a 50% negative cutoff value, which is a potential assistive method for cervical cancer screening.
AICyte 先前已经证明，在使用 50%阴性截断值的情况下，其在减少工作量方面具有潜在作用，用于宫颈细胞学筛查。本研究旨在验证这一假设。
作者使用锐仟 WSI-2400（注册商称为 AICyte）评估了来自中国四个不同医院系统的 2018 年至 2023 年共 163848 例原始宫颈细胞学病例的集合。病例分类包括深圳 46060 例、郑州 67472 例、石家庄 25667 例和济南 24649 例。这些收集的病例使用 AICyte 系统进行评估，并对收集的数据与原始解释结果进行了统计学比较。
在人工智能系统中被指定为无需进一步复查的所有病例中，有 98.80%对应的原始诊断结果也为阴性。对于任何被指定为非典型鳞状细胞、不能排除高级别鳞状上皮内病变或更高级别病变的病例，其敏感性和阴性预测值分别为 90.77%和 98.80%。对于被指定为低级别鳞状上皮内病变或更高级别病变的病例，其敏感性和阴性预测值分别为 98.92%和 99.94%。在人工智能系统中被指定为无需进一步复查的 49 例低级别鳞状上皮内病变或更高级别病变中，细胞学组织学相关性显示有 8 例为宫颈上皮内瘤变 1 级和 18 例阴性病例，其余病例无组织学随访。实际上，如果实施一项方案，将符合阴性截断值的病例标记为无需进一步复查，并最终将病例确定为上皮内病变和恶性病变为阴性，那么使用 50%阴性截断值，AICyte 可以减少预期的工作量。
对于没有细胞技术人员的病理实践，或者希望优化工作流程的情况，人工智能系统 AICyte 可以单独作为一种独立的筛查工具，使用 50%的阴性截断值，这是宫颈癌筛查的一种潜在辅助方法。
