# Intraoperative integration of artificial intelligence during cystoscopic surgery

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $514,044

## Abstract

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.

## Key facts

- **NIH application ID:** 10365872
- **Project number:** 1R01CA260426-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** JOSEPH C LIAO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $514,044
- **Award type:** 1
- **Project period:** 2022-01-01 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10365872

## Citation

> US National Institutes of Health, RePORTER application 10365872, Intraoperative integration of artificial intelligence during cystoscopic surgery (1R01CA260426-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10365872. Licensed CC0.

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