AIMI Grand Rounds: Enhanced Imaging Technologies and Image-Guided Surgery for Bladder Cancer - Joseph Liao, MD

The AIMI Grand Rounds, sponsored by the Center for Artificial Intelligence in Medicine and Imaging (AIMI), is a virtual series held on every fourth Tuesday. The series is a crucial initiative for disseminating the latest AI advancements in medicine, aiming to drive transformative innovations in healthcare. Stanford participants of the live event can claim 1.0 CME credits: AMA PRA Category 1 Credits ™ or Non-Physician Participation Credit.

Speaker: 

Joseph Liao, MD: Professor of Urology, Stanford University 

Bio: Joseph Liao is the Kathryn Simmons Stamey Professor and Vice Chair of Urology at Stanford University School of Medicine. Dr. Liao is a physician scientist whose primary research interest lies in the development and translation of precision diagnostics for major urological diseases, particularly bladder cancer. He leads a multidisciplinary lab that has advanced endoscopic imaging and image-guided surgery of bladder cancer using fluorescence, endomicroscopy, molecular imaging, and most recently, artificial intelligence. His research is funded by the NIH and Department of Veterans Affairs, and he directs an NIH K12 career development program for physician scientists at Stanford. A graduate of Harvard College, he earned his medical degree from Stanford University and completed his urology residency and fellowship training at UCLA.

Abstract: Bladder cancer ranks as the sixth most common malignancy, with 75% of the patients presenting at an early stage amenable to endoscopic management. Diagnostic cystoscopy combined with transurethral resection of the bladder tumor (TURBT) remains the standard for diagnosis, treatment, and surveillance. The ability to inspect the interior of the bladder reflects a long history of advances in optical imaging and endoscopy. However, despite these advances, recurrence and progression after endoscopic resection remain substantial, in part due to incomplete resection and provider-related variability. Consequently, unmet clinical needs exist for solutions that improve tumor enumeration, delineation and resection quality. Recent advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to address these challenges. This presentation will review the current status and opportunities in realizing AI-assisted cystoscopy and TURBT, including dataset curation, model development and training, system integration, and steps toward clinical translation.