Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) is a chronic condition marked by pelvic pain, urinary urgency, and frequency (1). Diagnosis remains difficult due to symptom overlap, lack of standardized criteria, and IC/BPS subtypes. As healthcare becomes more data-driven, artificial intelligence (AI) has emerged as a promising tool to enhance diagnostic precision, identify subtypes, and support treatment planning (2). This meta-analysis evaluates the methodologies, limitations, and clinical utility of AI tools used in IC/BPS research, aiming to identify where AI can be effectively integrated into patient care.

Articles were selected from PubMed, ScienceDirect, and Scopus (n=25) through an online search using the terms IC/BPS and AI, limited to studies published between 2003 and 2024.
Studies were categorized based on tissue type: urine-based (e.g., cytokines), bladder tissue (transcriptomics), imaging, microbiome (stool-based), and questionnaire-based subtyping. AI tools included classical machine learning (e.g., logistic regression) and deep learning models, while analysis methods included supervised and unsupervised machine learning, gene expression profiling, molecular docking, and validation via qPCR or clinical data.

Urine-based studies found that IC patients exhibit distinct molecular changes, including elevated IL-6, IL-8, MCP-1, and GRO, indicating inflammation and potential bladder dysfunction. Machine learning models analyzing urine biomarkers achieved 87-96% accuracy, offering a non-invasive alternative to bladder biopsies.

Stool-based AI models classified IC/BPS patients with high accuracy by analyzing microbiome shifts.
Bladder tissue-based AI models identified significant gene expression changes in KRT20, BATF, TP63, and the miR-320 family, distinguishing IC subtypes and predicting comorbidities like depression. Though invasive, AI-driven bladder tissue analysis outperformed expert urologists in diagnosis and provided molecular insights for targeted therapies.
Imaging-based AI models, particularly deep learning such as convoluted neural networks, achieved high precision in detecting Hunner’s lesions and outperformed urologists, AI-assisted imaging enhances diagnostic accuracy and consistency by reducing reliance on subjective interpretation.
Blood-based AI models identified dysregulation in immune markers, particularly CXCL8 and IL1B, implicating systemic involvement and supporting dual screening for IC/BPS and related comorbidities such as depression.
Lastly, symptom-based AI models used surveys (e.g., ICSI/ICPI) to classify IC/BPS into subtypes: bladder-specific pain, myofascial pain, and non-urologic pelvic pain. (3)

AI-based urine tests offer a faster, more accurate, and patient-friendly approach to IC/BPS diagnosis. Stool analysis serves as a non-invasive tool that complements urine testing. Though invasive, bladder tissue-based AI outperformed urologists and may guide targeted therapies. Imaging-based AI improves accuracy and reduces subjectivity. Blood-based models may support dual screening for IC/BPS and comorbidities. Lastly, symptom-based AI relies solely on patient-reported data; it holds potential for early IC/ BPS diagnosis and personalized care, even in settings with limited specialist access.
However, most studies relied on small, retrospective datasets with limited diversity and often overlooked comorbidities. Many models also lacked explainability, limiting clinical trust and real-world adoption.

Stool-based analysis may complement urine testing in non-invasive settings such as integrative medicine clinics, particularly for microbiome-associated IC/ BPS subtypes. Bladder tissue-based AI, though invasive, could enhance personalized treatment in specialized centers managing complex cases. Once validated, blood-based models may serve as practical tools in multidisciplinary clinics to assess both bladder-specific pathology and systemic comorbidities. Symptom-based AI shows strong potential for early screening and triage, especially in primary care, telemedicine, and underserved populations. 

The current literature offers limited clinical data. Since AI models rely on large, diverse datasets for accuracy and generalizability, the development of collaborative networks among clinicians, researchers, and patient organizations is critical. Establishing large-scale consortiums could bridge existing gaps and accelerate the path toward AI-powered diagnosis and precision treatment of IC/ BPS.

Offiah, I., & McMahon, S. B. (2013). Interstitial cystitis/bladder pain syndrome: Diagnosis and management. International Urogynecology Journal, 30(1), 3–12.
Ogawa, T., Homma, Y., Igawa, Y., & Seki, S. (2023). Deep learning model for cystoscopic recognition of Hunner lesions in interstitial cystitis: A diagnostic study. JACC: Basic to Translational Science, 4(1), 49–58.
Kim, J. H., Kim, M. E., & Lee, K. S. (2023).Artificial intelligence in interstitial cystitis/bladder pain syndrome: Diagnosis, prognosis, and treatment.International Neurourology Journal, 27(Suppl 1), S1–S9.