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PEER-TO-PEER CLINICAL CONVERSATIONS
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Machine Learning's Transformative Potential in Urologic Diagnosis and Care
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Gregory Tasian, MD, MSc, MSCE
In this discussion, adult nurse practitioner, Diane Newman and Greg Tasian, a urologist specializing in pediatric urology, delve into the transformative potential of machine learning in the field of urology.
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Smartphone-Based Self-Care Education Program for Women With Interstitial Cystitis
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Edward Kim, MD, MPH
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| Edward Kim joins Diane Newman to discuss a patient-centered smartphone-based program called ERICA (Educational and Remote Interstitial Cystitis Aide) to self-manage interstitial cystitis (IC) in women.
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| Revolutionizing Urodynamics: Uromonitor's Comfortable, Real-Life Bladder Pressure Tracking |
Howard Goldman, MD, FACS
Diane Newman engages in a discussion with Howard Goldman on the Uromonitor, a non-invasive urodynamic device designed to measure bladder pressures in a more comfortable, natural setting.
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Real-World Implementation of Online Versus In-Person Community-Based Continence Promotion
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Madeline Moureau, BS
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| Madeline Moureau joins Diane Newman to discuss a study comparing online versus in-person implementation of the Mind Over Matter program, a community-based self-management program for small-group behavior change intervention for women aged 50 or older who want to prevent or improve their incontinence symptoms.
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| A Machine Learning Model to Predict Likelihood of Spontaneous Ureteral Stone Passage
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| Katherine Fischer, MD
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| Katherine Fischer presents work on a machine learning model to predict the likelihood of spontaneous ureteral stone passage. Her and her team conducted a retrospective cohort study, creating distinct models for pediatric and adult patients. The models achieved 70% accuracy for pediatric patients and 63% accuracy for adults, with stone size, age, and other factors identified as important predictors, highlighting the potential for this model to aid clinical decision-making.
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| Development of a Urinary Tract Atlas Using Convolutional Neural Networks |
| Katherine Fischer, MD
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| Katherine Fischer presents research on training a convolutional neural network (CNN) to identify and highlight kidneys, ureters, and the bladder on CT scans. The study aimed to automate the identification of the urinary collecting system and organs in CT scans to aid in nephrolithiasis identification, measurements, and outcome prediction.
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| Zoom To Scale? Real-World Implementation Of Online Versus In-Person Community-Based Continence Promotion
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| Madeline Moureau, BS
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| Madeline Moureau presents a study examining the impact of the "Mind Over Matter: Healthy Bowels, Healthy Bladder" program, a community-based behavior change intervention for women aged 50 or older aiming to prevent or improve urinary incontinence and anal incontinence. The program was initially in-person and transitioned to online delivery due to the COVID-19 pandemic.
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| The Uromonitor®, BTA Stat®, Alere NMP22® BladderChek®, and UBC® Rapid Test in Comparison to Cytology as Tumor Marker for Urinary Bladder Cancer
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| Christina Meisl, MD
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| A recent German multicenter study compared several urine-based rapid tests for detecting urinary bladder cancer to urine cytology. The study included 499 BC patients, 79 patients without disease evidence, and 221 healthy controls. The findings revealed that BTA stat® and the quantitative UBC® rapid test showed higher sensitivity in detecting BC compared to urine cytology, although with slightly lower specificity.
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