{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:23:30Z","timestamp":1786116210257,"version":"build-2736575974"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Male chronic pelvic pain syndrome (CPPS) is a heterogeneous condition involving overlapping urinary, psychosocial, organ-specific, infectious, neurological, myofascial, and sexual phenotypes. This complexity limits the effectiveness of routine symptom assessment and empirical treatment strategies. Artificial intelligence (AI) may support more reproducible interpretation, differential diagnosis, phenotyping, and personalized management. This up-to-date narrative mapping review aimed to identify AI-assisted approaches that are directly or indirectly relevant to the assessment of males with CPPS, classify them according to UPOINTS phenotypic domains and clinical functions, and critically discuss the extent to which current evidence is disease-specific or extrapolated from related conditions. Methods: A literature search was performed in PubMed\/MEDLINE, the Cochrane Library, and Google Scholar from database inception to May 2026 using terms related to male CPPS, UPOINTS domains, diagnosis, treatment, prognosis, digital solutions, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, and decision support. Studies were included if they described AI-based or AI-adjacent computational approaches relevant to male CPPS or to related conditions important for UPOINTS-based phenotyping, differential diagnosis, or phenotype-specific assessment. Results: Available evidence remains fragmented and is largely extrapolated from related urological, chronic pain, pelvic floor, infectious, neurological, and sexual medicine conditions. AI applications were most developed in urinary and organ-specific domains, including uroflowmetry analysis, bladder volume assessment, cystoscopy, prostate imaging, urinary biomarkers, and differential diagnosis of lower urinary tract disorders. AI tools also showed potential for infection detection, psychosocial screening, chronic pain monitoring, neuroimaging-based phenotyping, pelvic floor dysfunction assessment, and evaluation of sexual dysfunction. However, male-CPPS-specific validation remains limited. Conclusions: AI has promising potential to improve differential diagnosis, multidomain phenotyping, and individualized management in males with CPPS. Current evidence is mainly translational and hypothesis-generating. Future studies should focus on prospective male-CPPS-specific cohorts, external validation, explainable multimodal models, and integration of AI tools into clinically meaningful, patient-centered workflows.<\/jats:p>","DOI":"10.3390\/diagnostics16152479","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T08:08:02Z","timestamp":1786003682000},"page":"2479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in the Assessment of Males with Chronic Pelvic Pain Syndrome: An Up-to-Date UPOINTS-Based Narrative Mapping Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Ali","family":"Talyshinskii","sequence":"first","affiliation":[{"name":"Department of Urology and Andrology, Genome Clinic, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatima","family":"Kudakova","sequence":"additional","affiliation":[{"name":"Department of Functional Urology, Saint-Petersburg State University Hospital, Saint Petersburg 191023, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9512-3035","authenticated-orcid":false,"given":"Olga","family":"Staroseltseva","sequence":"additional","affiliation":[{"name":"Department of Functional Urology, Saint-Petersburg State University Hospital, Saint Petersburg 191023, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6255-0193","authenticated-orcid":false,"given":"Nariman","family":"Gadzhiev","sequence":"additional","affiliation":[{"name":"Department of Functional Urology, Saint-Petersburg State University Hospital, Saint Petersburg 191023, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6248-6478","authenticated-orcid":false,"given":"Bhaskar Kumar","family":"Somani","sequence":"additional","affiliation":[{"name":"Department of Urology, University Hospital Southampton NHS Trust, Southampton SO16 6YD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"ref_1","unstructured":"(2026). EAU Guidelines. Edn. Presented at the EAU Annual Congress; London, UK, EAU Guidelines Office."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.4103\/0970-1591.173105","article-title":"Male chronic pelvic pain: An update","volume":"32","author":"Smith","year":"2016","journal-title":"Indian J. Urol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1097\/JU.0000000000004565","article-title":"Male Chronic Pelvic Pain: AUA Guideline: Part II Treatment of Chronic Prostatitis\/Chronic Pelvic Pain Syndrome","volume":"214","author":"Lai","year":"2025","journal-title":"J. Urol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., and Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. npj Digit. Med., 5.","DOI":"10.1038\/s41746-022-00712-8"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hameed, B.M.Z., Dhavileswarapu, A.V.L.S., Raza, S.Z., Karimi, H., Khanuja, H.S., Shetty, D.K., Ibrahim, S., Shah, M., Naik, N., and Paul, R. (2021). Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature. J. Clin. Med., 10.","DOI":"10.3390\/jcm10091864"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1111\/iju.15149","article-title":"A review of current treatments for chronic prostatitis\/chronic pelvic pain syndrome under the UPOINTS system","volume":"30","author":"Maeda","year":"2023","journal-title":"Int. J. Urol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/2090598X.2021.1955546","article-title":"Using the UPOINT system to manage men with chronic pelvic pain syndrome","volume":"19","author":"Bryk","year":"2021","journal-title":"Arab J. Urol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/s41391-024-00847-7","article-title":"Can ChatGPT provide high-quality patient information on male lower urinary tract symptoms suggestive of benign prostate enlargement?","volume":"28","author":"Secco","year":"2025","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bae, H., Lee, G.M., Lee, J., Lee, J.H., Yoo, S., Cho, M.C., Jeong, H., and Son, H. (2026). Estimation of IPSS and OABSS scores using ChatGPT-4o: A comparative validation study in Korea. BMC Urol., 26.","DOI":"10.1186\/s12894-026-02054-z"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3343\/alm.2024.0304","article-title":"Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System","volume":"45","author":"Kim","year":"2025","journal-title":"Ann. Lab Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1002\/nau.25490","article-title":"Applications of machine learning in urodynamics: A narrative review","volume":"43","author":"Liu","year":"2024","journal-title":"Neurourol. Urodyn."},{"key":"ref_12","first-page":"275","article-title":"Artificial intelligence-assisted uroflowmetry and automated evaluation of lower urinary system symptoms","volume":"93","author":"Duran","year":"2026","journal-title":"Urol. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1111\/iju.14661","article-title":"Development of an artificial intelligence diagnostic system for lower urinary tract dysfunction in men","volume":"28","author":"Matsukawa","year":"2021","journal-title":"Int. J. Urol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tsai, V.F.S., Tsai, Y.C., Yang, S.S.D., Li, M.W., Pong, Y.H., and Tsai, Y.T. (2025). An Emerging Trend of At-Home Uroflowmetry\u2014Designing a New Vibration-Based Uroflowmeter with Artificial Intelligence Pattern Recognition of Uroflow Curves and Comparing with Other Technologies. Diagnostics, 15.","DOI":"10.3390\/diagnostics15141832"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"296","DOI":"10.5213\/inj.2550190.095","article-title":"Accuracy of Portable Ultrasound Measurement of the Bladder Volume Using the Manual Fanning Scan and Traditional Motorized Scan Methods","volume":"29","author":"Jeong","year":"2025","journal-title":"Int. Neurourol. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1002\/nau.70057","article-title":"Portable Ultrasound Bladder Volume Measurement over Entire Volume Range Using a Deep Learning Artificial Intelligence Model in a Selected Cohort: A Proof of Principle Study","volume":"44","author":"Jeong","year":"2025","journal-title":"Neurourol. Urodyn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1097\/MOU.0000000000001135","article-title":"Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours","volume":"34","author":"Ikeda","year":"2024","journal-title":"Curr. Opin. Urol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"78","DOI":"10.5213\/inj.2244064.032","article-title":"Development of an Artificial Intelligence-Based Support Technology for Urethral and Ureteral Stricture Surgery","volume":"26","author":"Eun","year":"2022","journal-title":"Int. Neurourol. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shen, C., Ji, Y., Zhang, W., Fang, X., You, J., and Zheng, B. (2025). Risk model for depression in patients with chronic prostatitis\/chronic pelvic pain syndrome: A retrospective cross-sectional study. J. Int. Med. Res., 53.","DOI":"10.1177\/03000605251399460"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1001\/jamainternmed.2022.3178","article-title":"Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: A Randomized Comparative Effectiveness Trial","volume":"182","author":"Piette","year":"2022","journal-title":"JAMA Intern. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hauser-Ulrich, S., K\u00fcnzli, H., Meier-Peterhans, D., and Kowatsch, T. (2020). A Smartphone-Based Health Care Chatbot to Promote Self-Management of Chronic Pain (SELMA): Pilot Randomized Controlled Trial. JMIR MHealth UHealth, 8.","DOI":"10.2196\/15806"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, R., Lee, Y.C., Kraut, R.E., and Mohr, D.C. (2023). Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. npj Digit Med., 6.","DOI":"10.1038\/s41746-023-00979-5"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.jad.2024.04.057","article-title":"The therapeutic effectiveness of artificial intelligence-based chatbots in alleviation of depressive and anxiety symptoms in short-course treatments: A systematic review and meta-analysis","volume":"356","author":"Zhong","year":"2024","journal-title":"J. Affect Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.jpain.2018.09.005","article-title":"Determining Pain Catastrophizing from Daily Pain App Assessment Data: Role of Computer-Based Classification","volume":"20","author":"Jamison","year":"2019","journal-title":"J. Pain"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chang, T.C., Shkolyar, E., Del Giudice, F., Eminaga, O., Lee, T., Laurie, M., Seufert, C., Jia, X., E Mach, K., and Xing, L. (2023). Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: A Pilot Study. J. Endourol., Epub ahead of printing.","DOI":"10.1089\/end.2023.0056"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1093\/clinchem\/hvae156","article-title":"Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features","volume":"70","author":"Meng","year":"2024","journal-title":"Clin. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kaya, M., Durdag, O., Solak, M., Coskuncay, A., and Burakgazi, G. (2026). Lesion detection using artificial intelligence models in MR images of prostate cancer and prostatitis patients and comparison of model performance. Front. Urol., 5.","DOI":"10.3389\/fruro.2025.1726795"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Goh, H., Heo, T., Kim, J., Kim, Y., Lim, B., Woo, M.K., Choi, K.Y., Kim, C.-S., and Kim, J. (2025). Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO). Sci. Rep., 16.","DOI":"10.1038\/s41598-025-32334-x"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fang, F., Sun, Y., Huang, H., Huang, Y., Luo, X., Yao, W., Wei, L., Xie, G., Wu, Y., and Lu, Z. (2024). Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: A two-center study. J. Cancer Res. Clin. Oncol., 150.","DOI":"10.1007\/s00432-023-05549-6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1007\/s11255-024-04137-y","article-title":"Optimizing anterior urethral stricture assessment: Leveraging AI-assisted three-dimensional sonourethrography in clinical practice","volume":"56","author":"Feng","year":"2024","journal-title":"Int. Urol. Nephrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s11934-023-01192-3","article-title":"Smart Diagnosis of Urinary Tract Infections: Is Artificial Intelligence the Fast-Lane Solution?","volume":"25","author":"Naik","year":"2024","journal-title":"Curr. Urol. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Soe, N.N., Towns, J.M., Latt, P.M., Woodberry, O., Chung, M., Lee, D., Ong, J.J., Chow, E.P., Zhang, L., and Fairley, C.K. (2024). Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms. BMC Infect. Dis., 24.","DOI":"10.1186\/s12879-024-10285-4"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, X., Yu, Z., Ge, Z., Chow, E.P.F., Bao, Y., Ong, J.J., Li, W., Wu, J., Fairley, C.K., and Zhang, L. (2022). Web-Based Risk Prediction Tool for an Individual\u2019s Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study. J. Med. Internet Res., 24.","DOI":"10.2196\/37850"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.csbj.2024.07.018","article-title":"A step forward in the diagnosis of urinary tract infections: From machine learning to clinical practice","volume":"24","author":"Flores","year":"2024","journal-title":"Comput Struct. Biotechnol. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.cmpb.2018.10.007","article-title":"Diagnosis of urinary tract infection based on artificial intelligence methods","volume":"166","author":"Ozkan","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1093\/clinchem\/hvaf088","article-title":"Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results","volume":"71","author":"Favresse","year":"2025","journal-title":"Clin. Chem."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez Del \u00c1guila, M.M., Sorl\u00f3zano-Puerto, A., Bernier-Rodr\u00edguez, C., Navarro-Mar\u00ed, J.M., and Guti\u00e9rrez-Fern\u00e1ndez, J. (2025). Application of Machine Learning Algorithms in Urinary Tract Infections Diagnosis Based on Non-Microbiological Parameters. Pathogens, 14.","DOI":"10.3390\/pathogens14101034"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.juro.2017.03.132","article-title":"Characterization of Whole Body Pain in Urologic Chronic Pelvic Pain Syndrome at Baseline\u2014A MAPP Research Network Study","volume":"198","author":"Lai","year":"2017","journal-title":"J. Urol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1016\/j.pain.2014.09.002","article-title":"Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP network study","volume":"155","author":"Bagarinao","year":"2014","journal-title":"Pain"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lan, X., Niu, X., Bai, W.X., Li, H.N., Zhu, X.Y., Ma, W.J., Li, J.-L., Dun, W.-H., Zhang, M., and He, J. (2022). The functional connectivity of the basal ganglia subregions changed in mid-aged and young males with chronic prostatitis\/chronic pelvic pain syndrome. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.1013425"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Liao, W., Wu, X., Liao, X., Ou, Y., Zhao, L., Zhao, L., Luo, D., and Wang, F. (2026). Machine learning for the diagnosis of fibromyalgia based on magnetic resonance imaging. PLoS ONE, 21.","DOI":"10.1371\/journal.pone.0340899"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zheng, X., Lamoth, C.J., Timmerman, H., Otten, E., and Reneman, M.F. (2024). Establishing central sensitization inventory cut-off values in Dutch-speaking patients with chronic low back pain by unsupervised machine learning. Comput Biol. Med., 178.","DOI":"10.1016\/j.compbiomed.2024.108739"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, A., Wu, W., Xu, W., Zhao, T., Starkweather, A.R., Rodriguez, L., Chen, M.H., and Cong, X.S. (2025). Machine learning to phenotype pain and predict response to pain interventions among young adults with irritable bowel syndrome. medRxiv.","DOI":"10.1101\/2025.10.07.25337516"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Levitt, J., Edhi, M.M., Thorpe, R.V., Leung, J.W., Michishita, M., Koyama, S., Yoshikawa, S., Scarfo, K.A., Carayannopoulos, A.G., and Gu, W. (2020). Pain phenotypes classified by machine learning using electroencephalography features. NeuroImage, 223.","DOI":"10.1016\/j.neuroimage.2020.117256"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Slepian, P.M., Buryk-Iggers, S., Lomanowska, A.M., Nguyen, B., Janmohamed, T., Clarke, H., Katz, J., and Niederstrasser, N.G. (2026). A Therapeutic Conversational Agent (Solace) for Management of Chronic Pain: Acceptability and Usability Study. JMIR Rehabil. Assist. Technol., 13.","DOI":"10.2196\/87689"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Amidei, J., Nieto, R., Kaltenbrunner, A., Ferreira De S\u00e1, J.G., Serrat, M., and Albajes, K. (2025). Exploring the Capacity of Large Language Models to Assess the Chronic Pain Experience: Algorithm Development and Validation. J. Med. Internet Res., 27.","DOI":"10.2196\/65903"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Souza, F.L., Bowman, H., Yang, F., Hesam-Shariati, N., Linke, J., Gilanyi, Y.L., Jones, M.D., Z-Pinto, R., McAuley, J.H., and Rizzo, R.R.N. (2025). Conversational Agents to Support Pain Management: A Scoping Review. Eur. J. Pain, 29.","DOI":"10.1002\/ejp.70016"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"196","DOI":"10.5489\/cuaj.4876","article-title":"The evolving clinical picture of chronic prostatitis\/chronic pelvic pain syndrome (CP\/CPPS): A look at 1310 patients over 16 years","volume":"12","author":"Doiron","year":"2018","journal-title":"Can. Urol. Assoc. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"120","DOI":"10.4103\/1008-682X.138189","article-title":"Use of the UPOINT phenotype system in treating Chinese patients with chronic prostatitis\/chronic pelvic pain syndrome: A prospective study","volume":"17","author":"Guan","year":"2015","journal-title":"Asian J. Androl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.juro.2007.09.088","article-title":"Chronic Prostatitis Collaborative Research Network Study Group. Muscle tenderness in men with chronic prostatitis\/chronic pelvic pain syndrome: The chronic prostatitis cohort study","volume":"179","author":"Shoskes","year":"2008","journal-title":"J. Urol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, W., Huang, J., Zhang, S., Jia, R., Liu, D., Dong, Y., Zhao, H., Nai, M., and Li, L. (2025). Comparative analysis of clinical feature-based machine learning models for predicting myofascial pelvic pain syndrome: A single-center retrospective study. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1689480"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, J., Yao, J., Chen, W., Zhang, F., Wang, H., Xu, X., Ge, H., Zhou, H., Cen, J., and Li, D. (2025). AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosis. eBioMedicine, 117.","DOI":"10.1016\/j.ebiom.2025.105755"},{"key":"ref_53","first-page":"1","article-title":"Novel approach for noninvasive pelvic floor muscle strength measurement using extracorporeal surface perineal pressure measurement and machine learning modeling","volume":"11","author":"Hwang","year":"2025","journal-title":"Digit Health"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"El-Sayegh, B., Dumoulin, C., Leduc-Primeau, F., and Sawan, M. (2024). Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction. Sensors, 24.","DOI":"10.3390\/s24216937"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1007\/s00192-024-05841-0","article-title":"Applicability of Deep Learning to Dynamically Identify the Different Organs of the Pelvic Floor in the Midsagittal Plane","volume":"35","year":"2024","journal-title":"Int. Urogynecol. J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1016\/j.bja.2024.01.036","article-title":"Artificial intelligence for ultrasound scanning in regional anaesthesia: A scoping review of the evidence from multiple disciplines","volume":"132","author":"Bowness","year":"2024","journal-title":"Br. J. Anaesth."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s00345-015-1720-3","article-title":"Prevalence of sexual dysfunction in men with chronic prostatitis\/chronic pelvic pain syndrome: A meta-analysis","volume":"34","author":"Li","year":"2016","journal-title":"World J. Urol."},{"key":"ref_58","first-page":"222","article-title":"Chronic prostatitis\/chronic pelvic pain syndrome-related pain symptoms and their impact on sexual functioning","volume":"16","author":"Tripp","year":"2022","journal-title":"Can. Urol. Assoc. J."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1055\/a-2233-2736","article-title":"Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text","volume":"62","author":"Harrison","year":"2023","journal-title":"Methods Inf. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"448","DOI":"10.5534\/wjmh.240228","article-title":"Unlocking the Potential of ChatGPT in Male Sexual Health and Dysfunction: A Hold Endeavor and Comprehensive Study","volume":"43","author":"Ran","year":"2025","journal-title":"World J. Mens. Health"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, X.Y., Lu, W.T., Zhang, D., Tan, M.Y., and Qin, X. (2024). Development and validation of a prediction model for ED using machine learning: According to NHANES 2001\u20132004. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-78797-2"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yu, L., Cao, S., Song, B., and Hu, Y. (2025). Predictive role of the muscle quality index for testosterone deficiency in adult males based on interpretable machine learning methods. BMC Public Health, 25.","DOI":"10.1186\/s12889-025-24212-y"},{"key":"ref_63","first-page":"508","article-title":"Chronic Primary Pelvic Pain Syndrome in Men","volume":"120","author":"Franz","year":"2023","journal-title":"Dtsch. Arztebl. Int."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Samenezhad, S., and Rafighi, D. (2025). The role of artificial intelligence in advancing urologic care: From diagnostics to therapeutics. Surg. Pract. Sci., 24.","DOI":"10.1016\/j.sipas.2025.100322"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T08:28:08Z","timestamp":1786004888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/15\/2479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,6]]},"references-count":64,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["diagnostics16152479"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16152479","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,6]]}}}