Effectiveness and Performance of an Optical Biopsy Technology for Esophageal Cancer in Brazil and the United States

The investigators' hypothesis is that the artificial intelligence (AI) mobile, high-resolution microendoscope (mHRME) will increase the accuracy of Lugol's chromoendoscopy (LCE) in endoscopic cancer detection in low- and middle-income countries (LMICs) and high-income countries (HICs). Objective 1: The investigators' first objective is to evaluate the diagnostic performance, efficiency, and impact of this automated optical biopsy device. In a single-arm study (n=200) of high-risk subjects undergoing LCE followed by AI-mHRME for ESCN screening in Brazil and the US, the investigators will evaluate the diagnostic performance and efficiency of this automated optical biopsy device. The investigators' other hypotheses are that the AI-mHRME will: 1. increase the mHRME accuracy in novices and be non-inferior to experts, 2. increase user confidence among experts and novices, and 3. increase the LCE efficiency and impact byreducing biopsies and second procedures. The investigators will compare the accuracy of the AI-mHRME software read to novice and expert clinicians' subjective reading to gold-standard histopathology by an expert gastrointestinal (GI) pathologist. For clinician confidence and clinical impact, they will determine the clinician's confidence level in the software diagnosis and the potential clinical impact of this diagnosis among novice and expert endoscopists using AI-mHRME. The clinician reads will be part of the mHRME procedure and treatment "plan" (biopsy vs. not biopsy vs. treat). Clinicians are not considered study subjects in objective 1. The clinical impact will be determined by the change in the clinician's decision in the treatment "plan" before and after the AI-mHRME read. For efficiency (biopsy saving and diagnostic yield), they will determine the number of patients spared any biopsy due to AI-mHRME. The investigators will compare the diagnostic yield of AI-mHRME and LCE vs. LCE alone (diagnostic yield = neoplastic biopsies/total number of biopsies obtained in biopsied patients). Objective 2: This objective will have three study populations, with a total sample size of n=50 subjects. To determine barriers and facilitators to implementing AI-mHRME, the team will form Health Sector Stakeholder Advisory Boards (HS-SAB) in the US and Brazil as the first study population. The HS-SABs will include academic partners, primary care providers referring patients, doctors performing esophageal cancer screening, hospital administrators, and patient and caregiver representatives. The HS-SAB sample size will be 6-10 members in the US and Brazil each, a standard number of participants for research advisory boards. The team will collect feedback and input through focus group discussions (FGDs) at 6 time points across the project period per HS-SAB. FGD objectives will match the research stage: clinical trial planning (recruitment and retention plan refinement), data collection (stakeholders identification), result interpretation, and dissemination. For the second study population, the team will conduct semi-structured individual interviews with implementers to assess barriers and facilitators to implementing AI-assisted cancer technologies (n=40). Interviews will be with patients and caregivers(n=10), GI clinicians (n=10), primary care physicians (n=10), and hospital and health leadership (n=10). There will be surveys with endoscopists (n=40) at the participating sites to understand their thoughts on HRME.