Mahapatra Chitaranjan
Institut Hospitalo-Universitaire, University of Bordeaux, Pessac 33600, France.
Bladder (San Franc). 2025 Apr 10;12(2):e21200042. doi: 10.14440/bladder.2024.0054. eCollection 2025.
Bladder pathophysiology encompasses a wide array of disorders, including bladder cancer, interstitial cystitis, overactive and underactive bladder, and bladder outlet obstruction. It also involves conditions such as neurogenic bladder, bladder infections, trauma, and congenital anomalies. Each of these conditions presents unique challenges for diagnosis and treatment. Recent advancements in artificial intelligence (AI) have shown significant potential in revolutionizing diagnostic methodologies within this domain.
This review provides an updated and comprehensive examination of the integration of AI into the diagnosis of bladder pathophysiology. It highlights key AI techniques, including machine learning and deep learning, and their applications in identifying and classifying bladder conditions. The review also assesses current AI-driven diagnostic tools, their accuracy, and clinical utility. Furthermore, it explores the challenges and limitations confronted in the implementation of AI technologies, such as data quality, interpretability, and integration into clinical workflows, among others. Finally, the paper discusses future directions and advancements, proposing pathways for enhancing AI applications in bladder pathophysiology diagnosis. This review aims to provide a valuable resource for clinicians, researchers, and technologists, fostering an in-depth understanding of AI's roles and potential in transforming bladder disease diagnosis.
While AI demonstrates considerable promise in enhancing the diagnosis of bladder pathophysiology, ongoing progresses in data quality, algorithm interpretability, and clinical integration are essential for maximizing its potential. The future of AI in bladder disease diagnosis holds great promise, with continued innovation and collaboration opening the possibility of more accurate, efficient, and personalized care for patients.
膀胱病理生理学涵盖了广泛的疾病，包括膀胱癌、间质性膀胱炎、膀胱过度活动症和膀胱活动低下症以及膀胱出口梗阻。它还涉及诸如神经源性膀胱、膀胱感染、创伤和先天性异常等情况。这些病症中的每一种在诊断和治疗方面都带来了独特的挑战。人工智能（AI）的最新进展已显示出在彻底改变该领域诊断方法方面的巨大潜力。
本综述对人工智能在膀胱病理生理学诊断中的整合进行了更新且全面的考察。它突出了关键的人工智能技术，包括机器学习和深度学习，以及它们在识别和分类膀胱病症中的应用。该综述还评估了当前由人工智能驱动的诊断工具、它们的准确性和临床实用性。此外，它探讨了在实施人工智能技术过程中面临的挑战和限制，例如数据质量、可解释性以及融入临床工作流程等。最后，本文讨论了未来的方向和进展，提出了增强人工智能在膀胱病理生理学诊断中应用的途径。本综述旨在为临床医生、研究人员和技术专家提供有价值的资源，促进对人工智能在改变膀胱疾病诊断中的作用和潜力的深入理解。
虽然人工智能在增强膀胱病理生理学诊断方面显示出相当大的前景，但在数据质量、算法可解释性和临床整合方面的持续进展对于最大限度发挥其潜力至关重要。人工智能在膀胱疾病诊断中的未来前景广阔，持续的创新与合作开启了为患者提供更准确、高效和个性化护理的可能性。
