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In this video, Glenn T. Werneburg, MD, PhD, shares the take-home message from the abstracts "Machine learning algorithms demonstrate accurate prediction of objective and patient-reported response to botulinum toxin for overactive bladder and outperform expert humans in an external cohort and "Machine learning algorithms predict urine culture bacterial resistance to first line antibiotic therapy at the time of sample collection, which were presented at the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction 2024 Winter Meeting in Fort Lauderdale, Florida. Werneburg is a urology resident at Glickman Urological & Kidney Institute at Cleveland Clinic, Cleveland, Ohio.

We're very much looking forward to being able to clinically implement these algorithms, both on the OAB side and the antibiotic resistance side. For the OAB, if we can identify who would best respond to sacral neuromodulation, and who would best respond to onabotulinumtoxinA injection, then we're helping patients achieve an acceptable outcome faster. We're improving their incontinence or their urgency in a more efficient way. So we're enthusiastic about this. Once we can implement this clinically, we believe it's going to help us in this way. It's the same for the antibiotic resistance algorithms. When we can get these into the hands of clinicians, we'll be able to have a good suggestion in terms of which is the best antibiotic to use for this patient at this time. And in doing so, we hope to be able to improve our antibiotic stewardship. Ideally, we would use an antibiotic with the narrowest spectrum that would still cover the infecting organism, and in doing so, it reduces the risk for resistance. So if that same patient requires an antibiotic later on in his or her lifetime, chances areand we'd have to determine this with data and experimentsif we're implementing a narrower spectrum antibiotic to treat an infection, they're going to be less likely to be resistant to other antibiotics down the line.

This transcription was edited for clarity.

Excerpt from:
Expert on how machine learning could lead to improved outcomes in urology - Urology Times