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REVIEW ARTICLE
Year : 2021  |  Volume : 32  |  Issue : 1  |  Page : 2-7

Toward a validated diagnostic test with machine learning algorithm for interstitial cystitis


Department of Urology, Beaumont Health System, Royal Oak, MI, USA

Correspondence Address:
Michael B Chancellor
Department of Urology, Beaumont Health System, 3811 West 13 Mile Road, Suite 504, Royal Oak 48073, MI
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/UROS.UROS_155_20

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Diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) is difficult as there is no definitive test for IC/BPS. Instead, the diagnosis is based on urinary symptoms and cystoscopy may be recommended. However, cystoscopic diagnosis is associated with potentially exacerbating painful side effects and is highly subjective among physicians. Furthermore, IC/PBS symptoms overlap with symptoms of bladder cancer, urinary tract infection, or overactive bladder. As a result, many patients may go years without a correct diagnosis and proper disease management. The goal of our current IC/BPS research is to develop a simple diagnostic test based on several urine proteins called the IC-risk score (IC-RS). A machine learning (ML) algorithm uses this information to determine if a person has IC/BPS or not; if they have IC/BPS, whether their IC/BPS is characterized by Hunner's lesions. We are currently in the middle of a grant to collect urine samples from 1000 patients with IC/BPS and 1,000 normal controls from across the United States. We are using social media such as Twitter and Facebook and working with patient advocacy organizations to collect urine samples from across the country. We hope to validate the IC-RS and apply for regulatory approval. Having a validated diagnostic test for IC/BPS would be a major advancement to help urology patients. In addition, drug companies developing new drugs and therapies for IC/BPS would have a better way to determine who to include in their clinical trials, and possibly another way to measure if their drug or therapy is effective. We will hereby review the steps that have led us in urine biomarker discovery research from urine protein assessment to use crowdsourcing stakeholders participation to ML algorithm IC-RS score development.


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