Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study

Abstract

BACKGROUND:The Paris System for Reporting Urinary Cytology (TPS) has been shown to improve bladder cancer diagnosis. Advances in artificial intelligence (AI) may assist and improve the clinical workflow by applying TPS in routine diagnostic services. METHODS: A deep-learning-based algorithm was developed to identify urothelial cancer candidate cells using whole-slide images (WSIs). In the testing cohort, 131 urine cytology slides were retrospectively retrieved and analyzed using this AI algorithm. The authors compared the performance of one cytopathologist and two cytotechnologists using AIassisted digital urine cytology. Then, the AI-assisted WSIs were evaluated in the clinical workflow. The cytopathologist first made a diagnosis by reviewing the AI-inferred WSIs and quantitative data (nuclear-to-cytoplasmic ratio and nuclear size) for each sample. After a washout period, the same cytopathologist made a diagnosis for the same samples using direct microscopy. All diagnosis results were compared with the expert panel consensus. RESULTS: The AI-assisted diagnosis by the two cytotechnologists and the one cytopathologist demonstrated performance results that were comparable to the expert panel consensus (sensitivity, 79.5% and 82.1% vs. 92.3%, respectively; specificity, 100% and 98.9% vs. 100%, respectively).Furthermore, the performance of the AI-assisted WSIs compared with the microscopic diagnosis by the cytopathologist demonstrated superior sensitivity (92.3% vs. 87.2%) and negative predictive value (96.8% vs. 94.8%). In addition, the AIassisted reporting demonstrated near perfect agreement with the expert panel consensus (κ = 0.944) and the microscopic diagnosis (κ = 0.862).

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

“…Using mixed-methods, Calisto et al 300 patients found that AI assistance increased detection of adenomas and serrated polyps during colonoscopy in comparison to historical controls, but the findings were not statistically significant. Ou et al [24] demonstrated that AI-assisted analysis of urine cytology outperformed the conventional method, with improved sensitivity (92% vs. 87%) and NPV (97% vs. 95%). Nasir-Moin et al [25] showed that AI for interpretation of 100 colorectal polyp samples significantly improved pathologists' classification accuracy compared with standard microscopic assessment (74% to 81%).…”

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology

“…Utilizing this approach, several studies were identified; Cochrane Library (47), PubMed® (26), Scopus™ (241), Google Scholar (3330) and Web of Science™ (2). Duplicate results, articles or audits not published in peer-reviewed journals were excluded.…”

Nuclear membrane irregularity in high‐grade urothelial carcinoma cells can be measured by using circularity and solidity as morphometric shape definitions in digital image analysis of urinary tract cytology specimens

“…There have been multiple efforts to digitize TPS by incorporating digital pathology methods into the analysis of urine cytology slides. [6][7][8][9][10][11][12][13][14] Many of these efforts hinge on whole-slide imaging to scan urine cytology slides at high resolution to allow for digital image analysis. Some early studies used the manual annotation of urothelial cells to better quantify TPS cytomorphologic criteria; the N/C ratio has been the most studied criterion using these methods.…”

“…There have been multiple efforts to digitize TPS by incorporating digital pathology methods into the analysis of urine cytology slides 6–14 . Many of these efforts hinge on whole‐slide imaging to scan urine cytology slides at high resolution to allow for digital image analysis.…”