Intraoperative integration of artificial intelligence during cystoscopic surgery

PROJECT SUMMARY Bladder cancer is the sixth most common cancer in the U.S., has one of the highest recurrence rates of all cancers, and is the most expensive cancer to treat from diagnosis to death. Current standard for bladder cancer diagnosis relies on clinic-based white light cystoscopy for initial screening, followed by transurethral

resection of bladder tumor in the operating room for pathologic diagnosis and local staging. White light cystoscopy has several well recognized shortcomings, particularly incomplete detection, thereby leading to suboptimal resection and contributing to cancer recurrence and progression. Our goal is to improve outcomes

for bladder cancer patients through integration of a deep learning algorithm to improve cystoscopic detection and enhance surgical resection. Artificial intelligence (AI)-based on deep neural networks have demonstrated remarkable capacity to learn complex relationships and incorporate existing knowledge into the inference model. We hypothesize that AI-

augmented detection of bladder tumor will improve diagnostic cystoscopy in the clinic setting to identify suspicious lesions and improve the quality of transurethral resection in the operating room, thereby reducing overall cancer recurrence and outcome. Towards the goal of establishing a paradigm of AI-based framework

for augmented detection of bladder cancer, we will leverage our strong preliminary data and outstanding environment in AI research. We propose three specific aims: 1) To curate a high-quality annotated cystoscopy imaging dataset to optimize deep neural network CystoNet; 2) To design and optimize CystoNet for real-time

cystoscopic navigation and cancer detection; and 3) To conduct a prospective multicenter validation of CystoNet during bladder cancer surgery. Successful completion of the studies proposed here will serve to translate deep learning algorithm to the dynamic environment of cystoscopic surgery without the need for specialized instrumentaitons. We foresee

our approach will improve the outcome of a major cancer and genearlizable to other organ systems amenable for endsocopic interventions.