# AI-Assisted Microendoscopy for the Early Detection of Esophageal Cancer

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $557,624

## Abstract

Abstract: Esophageal cancer is the 6th most common cause of cancer-related mortality worldwide. While
esophageal squamous cell neoplasia (ESCN) carries a significant global burden, those in certain underserved
geographic regions (South America, eastern Africa, eastern Iran, northern China) have particularly high
incidence and mortality rates due to lack of endoscopic screening capacity. While endoscopy with Lugol’s
chromoendoscopy or “digital” chromoendoscopy has shown high sensitivity (>95%) for screening, specificity is
poor (<60%) and false-positive results abound due to confounding inflammatory areas. As a result, standard of
care endoscopy produces many unnecessary biopsies, increasing risk and cost of endoscopic screening and
surveillance.
 In our ongoing R01 project, we developed and validated a mobile, high-resolution microendoscope (mHRME)
for screening and surveillance of ESCN. Despite ≥2 years of COVID delays, which especially impacted the
Chinese sites, we completed: (1) a randomized, controlled clinical trial (USA and China; n=918) of mHRME with
visual interpretation in patients undergoing screening or surveillance for esophageal squamous cell neoplasia,
(2) deep-learning software algorithms for automated detection of neoplastic images, and (3) a pilot trial (n=41)
of the software-assisted mHRME in Brazil. The trial revealed higher specificity for qualitative (visual)
interpretation by experts but not novices, in surveillance arm (100% vs. 19%, p<0.05). In the screening arm,
diagnostic yield (neoplastic biopsies/total biopsies) increased 3.6 times (8 to 29%); 16% of patients were
correctly spared any biopsy; and 18% had a change in clinical plan. In a single-arm pilot study, we also evaluated
an artificial intelligence-based mobile HRME (AI-mHRME) in 41 Brazilian subjects. This study (January 2022)
confirmed that quantitative interpretation (AI-mHRME) doubled diagnostic yield, improved endoscopists’
confidence, and had significant clinical impact (change of clinical plan in 64%). Our initial deep-learning algorithm
had a sensitivity/specificity of 100%/85%. Participating clinicians uniformly said they favored an AI-guided
approach but expressed concerns about its implementation.
 In this competing renewal, we will build on this valuable global data to optimize an AI-mHRME and evaluate
its clinical impact and implementation potential in ethnically and socioeconomically diverse populations in the
USA and Brazil. A stakeholder-engaged approach will be used to evaluate barriers, acceptability,
appropriateness, and feasibility of using AI-mHRME in ESCN management and to determine contextual factors
influencing adoption. Data obtained will facilitate implementation and dissemination of innovative, AI-assisted
cancer screening strategies in diverse populations and other cancers.

## Key facts

- **NIH application ID:** 10931518
- **Project number:** 5R01CA181275-08
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Sharmila Anandasabapathy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $557,624
- **Award type:** 5
- **Project period:** 2014-09-17 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931518

## Citation

> US National Institutes of Health, RePORTER application 10931518, AI-Assisted Microendoscopy for the Early Detection of Esophageal Cancer (5R01CA181275-08). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10931518. Licensed CC0.

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