# Development and Validation of an Automated Algorithm for Real-time Detection of Neoplasia in Barrett's Esophagus using a Low-cost, Portable Microendoscope

> **NIH NIH K23** · BAYLOR COLLEGE OF MEDICINE · 2022 · $200,639

## Abstract

Project Summary/Abstract
Endoscopic surveillance of Barrett’s esophagus (BE) is recommended for early diagnosis and treatment of
neoplasia (i.e., esophageal adenocarcinoma and high-grade dysplasia). However, neoplasia is difficult to detect
on regular white-light endoscopy (WLE; sensitivity 64%), and 26% of neoplasia is missed with WLE alone. On
the other hand, confocal high-resolution microendoscopy (cHRME) is a low-cost, portable, reusable imaging
technology that provides microscopic “optical biopsy” images of the esophageal mucosa at the time of endoscopy
and has sensitivity of neoplasia detection upwards of 89% in the hands of experts. Despite these advantages,
the dissemination of cHRME is limited by the availability of expert microendoscopists capable of interpreting
these histopathology-like images. Artificial intelligence algorithms that automate interpretation of cHRME images
could bridge this gap by providing a real-time computer-assisted diagnosis to users in community-based BE
surveillance settings and reducing the need for expert review.
My objective is to develop and validate an automated software algorithm for real-time BE neoplasia detection
using cHRME. Furthermore, I will optimize the software algorithm by incorporating traditional clinical risk factors
for comprehensive risk stratification. Thus, I propose the following Specific Aims: Aim 1: Technology
Development: To develop a software algorithm that automates interpretation of cHRME images in BE neoplasia
detection. Aim 2. Technology Evaluation and Optimization: (a) To validate the cHRME automated software
algorithm for real-time neoplasia detection in BE; (b) To optimize the automated software algorithm by integrating
demographic, lifestyle, and clinical risk factors for comprehensive BE neoplasia detection. Aim 3. Technology
Acceptability: (a) To evaluate the acceptability and experiences of endoscopists using computer-assisted
diagnosis; (b) To assess feasibility of the automated software algorithm in clinical BE neoplasia detection.
The overarching goal of this proposal is to develop artificial intelligence algorithms that facilitate dissemination
of novel, low-cost technologies into community settings for rapid, real-time, accurate neoplasia detection. Future
longitudinal studies will focus on validation of comprehensive risk models that use macroscopic and microscopic
metrics to predict future neoplasia risk in BE patients undergoing surveillance endoscopy.
My long-term goal is to become an independently funded investigator in novel techniques for early
gastrointestinal cancer detection. I have assembled an experienced mentoring committee comprised of senior,
funded investigators with expertise in technology development, artificial intelligence algorithms, epidemiology,
bioinformatics, and qualitative research. My career development plan includes additional formal research training
in artificial intelligence methodology, machine learning, and clinical trials. With the s...

## Key facts

- **NIH application ID:** 10449787
- **Project number:** 1K23DK129776-01A1
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Mimi Chang Tan
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $200,639
- **Award type:** 1
- **Project period:** 2022-04-18 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449787

## Citation

> US National Institutes of Health, RePORTER application 10449787, Development and Validation of an Automated Algorithm for Real-time Detection of Neoplasia in Barrett's Esophagus using a Low-cost, Portable Microendoscope (1K23DK129776-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10449787. Licensed CC0.

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