Advancing Bladder Health Diagnostics: The Potential of Optical Techniques for Noninvasive Assessment of Lower Urinary Tract Disorders

推进膀胱健康诊断:光学技术在无创评估下尿路疾病中的应用潜力

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Abstract

PURPOSE: This review evaluates the clinical utility of emerging optical techniques-specifically, near-infrared spectroscopy (NIRS), optical coherence tomography (OCT), photoacoustic imaging (PAI), and fiber-optic sensors (FOSs)-as noninvasive, patient-friendly modalities for diagnosing lower urinary tract dysfunction. We assess their potential integration into wearable systems for personalized urological care and propose a novel clinical pathway for their use. METHODS: We included published studies employing optical modalities to evaluate bladder function or pathology, focusing on diagnostic accuracy, feasibility, and patient-related outcomes. We also examined technical principles, diagnostic performance metrics (e.g., sensitivity, resolution, penetration), and clinical validation across optical modalities. A total of 40 articles met the final inclusion criteria. RESULTS: NIRS demonstrates >85% sensitivity for detecting detrusor overactivity in small-scale trials, with wearable devices enabling continuous bladder monitoring. OCT has been found to improve the detection of carcinoma in situ by up to 22% compared to white-light cystoscopy, although its shallow penetration (~2 mm) limits evaluation of deeper layers. PAI visualizes microvascular structures to depths of several centimeters, suggesting strong potential for noninvasive bladder tumor diagnosis. FOSs offer continuous intravesical pressure monitoring with reduced discomfort, although semi-invasive placement remains a limitation. CONCLUSION: Noninvasive optical diagnostics offer a safer, more patient-friendly alternative to conventional cystoscopy and urodynamic studies. However, larger multicenter trials, cost-effectiveness analyses, and regulatory alignment are needed. Integrating these emerging modalities with telemedicine and artificial intelligence could transform bladder care into a continuous, home-based model.

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