AIxMed, Inc., Santa Clara, California, USA.
Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
Cancer Cytopathol. 2024 Nov;132(11):686-695. doi: 10.1002/cncy.22884. Epub 2024 Jul 14.
This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence-based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time.
One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers.
AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%-30.6% to 63.9%), positive predictive value (PPV; from 21.6%-24.3% to 31.1%), and negative predictive value (NPV; from 91.3%-91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%-27.3% to 33.3%), PPV (from 31.3%-47.4% to 61.1%), and NPV (from 91.6%-92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%-82.2% to 90.0%) and NPV (from 91.7%-93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57-0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75-0.88). AIxURO significantly reduced screening time by 52.3%-83.2% from microscopy and 43.6%-86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers.
AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.
本研究评估了基于人工智能的 AIxURO 平台在支持膀胱癌管理中的尿细胞学诊断的有效性，该平台通常需要有经验的细胞学专家和大量的诊断时间。
一名细胞学专家和两名细胞技术专家审查了 116 例泌尿科患者的尿细胞学切片和对应的全玻片图像（WSI）。他们使用了三种诊断方式：显微镜检查、WSI 审查和 AIxURO，均符合巴黎尿细胞学报告系统（TPS）标准。比较了所有方法和观察者的性能指标，包括 TPS 指导的和二进制诊断、观察者间和观察者内的一致性以及筛查时间。
AIxURO 通过提高敏感性（从 25.0%-30.6%增加到 63.9%）、阳性预测值（PPV；从 21.6%-24.3%增加到 31.1%）和阴性预测值（NPV；从 91.3%-91.6%增加到 95.3%）来提高尿路上皮细胞不典型（AUC）病例的诊断准确性。对于可疑高级别尿路上皮癌（SHGUC）病例，它提高了敏感性（从 15.2%-27.3%增加到 33.3%）、PPV（从 31.3%-47.4%增加到 61.1%）和 NPV（从 91.6%-92.7%增加到 93.3%）。二进制诊断提高了敏感性（从 77.8%-82.2%增加到 90.0%）和 NPV（从 91.7%-93.4%增加到 95.8%）。所有方法的观察者间一致性显示出中度一致性（κ=0.57-0.61），细胞学专家在所有方法中的观察者内一致性均高于两名细胞技术专家（κ=0.75-0.88）。AIxURO 显著减少了显微镜检查和 WSI 审查的筛查时间，减少了 52.3%-83.2%和 43.6%-86.7%。所有方法和观察者的筛查阳性（AUC+）病例都需要比阴性病例更多的时间。
AIxURO 显示出通过尿细胞学诊断提高膀胱癌诊断的敏感性和效率的潜力。将其集成到细胞病理学筛查工作流程中可以显著减少筛查时间，从而改善整体诊断过程。
