Elishaev Esther, Harinath Lakshmi, Ye Yuhong, Matsko Jonee, Colaizzi Amy, Wharton Stephanie, Bhargava Rohit, Pantanowitz Liron, Hanna Matthew G, Harrington Sarah, Zhao Chengquan
Department of Pathology, UPMC Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Cancer Cytopathol. 2025 Jul;133(7):e70022. doi: 10.1002/cncy.70022.
Medical technologies powered by artificial intelligence are quickly transforming into practical solutions by rapidly leveraging massive amounts of data processed via deep learning algorithms. There is a necessity to validate these innovative tools when integrated into clinical practice.
This study evaluated the performance of the Hologic Genius Digital Diagnostics System (HGDDS) with a cohort of 890 previously reviewed and diagnosed ThinPrep Papanicolaou (Pap) tests with the intent to deploy this system for routine clinical use. The study included all diagnostic categories of The Bethesda System, with follow-up tissue sampling performed within 6 months of abnormal Pap test results to serve as the ground truth.
The HGDDS demonstrated excellent performance in detecting significant Pap test findings, with close to 100% sensitivity (98.2%-100%) for cases classified as atypical squamous cells of undetermined significance and above within a 95% confidence interval and a high negative predictive value (92.4%-100%).
The HGDDS streamlined workflow, reduced manual workload, and functioned as a stand-alone system.
由人工智能驱动的医疗技术正通过快速利用深度学习算法处理的大量数据,迅速转化为实际解决方案。当这些创新工具集成到临床实践中时,有必要对其进行验证。
本研究评估了Hologic Genius数字诊断系统(HGDDS)的性能,该研究队列包括890例先前经过审查和诊断的薄层液基细胞学检测(ThinPrep Pap)样本,旨在将该系统用于常规临床应用。该研究涵盖了贝塞斯达系统的所有诊断类别,并在巴氏试验结果异常后的6个月内进行了后续组织采样,以作为金标准。
HGDDS在检测重要的巴氏试验结果方面表现出色,对于分类为意义不明确的非典型鳞状细胞及以上的病例,在95%置信区间内灵敏度接近100%(98.2%-100%),且具有较高的阴性预测值(92.4%-100%)。
HGDDS简化了工作流程,减少了人工工作量,并作为一个独立系统发挥作用。