Esther Elishaev MD, Lakshmi Harinath MD, Yuhong Ye MD, PhD, Jonee Matsko SCT, MB, Amy Colaizzi SCT, Stephanie Wharton SCT, Rohit Bhargava MD, Liron Pantanowitz MD, PhD, MHA, Matthew G. Hanna MD, Sarah Harrington PhD, Chengquan Zhao MD, PhD
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引用次数: 0
Abstract
Background
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.
Methods
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.
Results
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%).
Conclusions
The HGDDS streamlined workflow, reduced manual workload, and functioned as a stand-alone system.
期刊介绍:
Cancer Cytopathology provides a unique forum for interaction and dissemination of original research and educational information relevant to the practice of cytopathology and its related oncologic disciplines. The journal strives to have a positive effect on cancer prevention, early detection, diagnosis, and cure by the publication of high-quality content. The mission of Cancer Cytopathology is to present and inform readers of new applications, technological advances, cutting-edge research, novel applications of molecular techniques, and relevant review articles related to cytopathology.