Affiliation:
1. Department of Pathology University Hospital of Salerno Salerno Italy
2. Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori University of Milano‐Bicocca Milan Italy
3. Department of Public Health University of Naples “Federico II” Naples Italy
4. Pathology Laboratory Institute of Molecular Pathology and Immunology of University of Porto (IPATIMUP) Porto Portugal
5. Department of Pathology Gravina Hospital Italy
6. Department of Medicine and Surgery University of Salerno Baronissi Italy
Abstract
AbstractRecent advancements in computer‐assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes.