# Artificial Intelligence Enabled Multi-Spectral Autofluorescence Imaging for Real-time Determination of Muscle in Bladder Tumor During Resection

> **NIH NIH R43** · CYTOVERIS INC · 2021 · $399,948

## Abstract

PROJECT SUMMARY
For adequate diagnosis and staging, transurethral resection of bladder tumor (TURBT) specimens must extend
into the bladder muscle wall. Studies indicate that for patients with high-grade bladder cancer, 5-year mortality
was 8% when the muscle was present in the TURBT specimen, and 13% when absent. For this reason, if
there is not sufficient muscle in the specimen after the initial resection, guidelines recommend repeat TURBT.
Almost half of TURBTs do not contain muscle as confirmed post-operatively by histopathologic examination.
There are currently no practical tools available to surgeons to determine during the procedure whether the
resected specimen includes sufficient muscle tissue. The goal of this project is to develop an imaging device
that will be used for point-of-surgery detection of muscle in TURBT specimen in real-time. We will use
ultraviolet light-emitting diodes to selectively excite different native fluorescent molecules in the tissue sample.
We will further increase the biochemical information content by complementing the autofluorescence data with
multi-wavelength reflectance images. We hypothesize that the combined multi-spectral autofluorescence and
reflectance images will provide a snapshot of the integral biomolecular information of the tissue and, when
combined with deep learning, capture latent biochemical and morphological differences that are encoded in the
multispectral images. Our hypothesis is based on the fact that the connective tissue lamina propria and
epithelial tissue have different biochemical make-up than the muscularis propria. We will employ a deep
learning framework on the acquired images to develop a training algorithm from >200 ex vivo TURBT
specimens from > 50 patients. The measured tissue will be processed for histopathological investigation to
create true labels for algorithm training. We will interpret the deep learning classification results by correlating
the extracted class features from the trained neural network with input image parameters, and consequently
attribute them with known biological differences of the tissue types. To test the algorithm, we will acquire
independent image sets from 80 samples from 20 patients and assess the concordance between our results
and pathologists’ reading of the Hematoxylin and Eosin (H&E) slides. We will also use a convolutional neural
network trained using a generative adversarial-network model to transform wide-field autofluorescence images
acquired from unlabeled tissue sections into H&E images of the same samples. The virtual H&E images will be
evaluated by pathologists to recognize major histopathological features in images generated with our virtual
staining technique and compared with the histologically stained images of the same samples.

## Key facts

- **NIH application ID:** 10325131
- **Project number:** 1R43CA265673-01
- **Recipient organization:** CYTOVERIS INC
- **Principal Investigator:** Rishikesh Pandey
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $399,948
- **Award type:** 1
- **Project period:** 2021-09-27 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10325131

## Citation

> US National Institutes of Health, RePORTER application 10325131, Artificial Intelligence Enabled Multi-Spectral Autofluorescence Imaging for Real-time Determination of Muscle in Bladder Tumor During Resection (1R43CA265673-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10325131. Licensed CC0.

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