In a previous clinical trial in China and the United States (US), the investigators developed and validated a mobile, high-resolution microendoscope (mHRME) for screening and surveillance of esophageal squamous cell neoplasia (ESCN). The trial revealed higher specificity for qualitative (visual) interpretation by experts but not the novice and in the 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 pilot study in Brazil, the investigators tested a software-assisted mHRME with deep-learning software algorithms to aid in the detection of neoplastic images and determine the performance, efficiency, and impact of the AI-mHRME when to Lugol's chromoendoscopy (LCE) alone and when using AI-mHRME with LCE. In this clinical trial, the investigators will build on the Brazil pilot trial data to optimize an artificial intelligence (AI) mHRME and evaluate its clinical impact and implementation potential in ethnically and socioeconomically diverse populations in the US and Brazil.
Suspected or Known Squamous Cell Neoplasia, Prior History of Squamous Cell Dysplasia and /or Neoplasia
In a previous clinical trial in China and the United States (US), the investigators developed and validated a mobile, high-resolution microendoscope (mHRME) for screening and surveillance of esophageal squamous cell neoplasia (ESCN). The trial revealed higher specificity for qualitative (visual) interpretation by experts but not the novice and in the 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 pilot study in Brazil, the investigators tested a software-assisted mHRME with deep-learning software algorithms to aid in the detection of neoplastic images and determine the performance, efficiency, and impact of the AI-mHRME when to Lugol's chromoendoscopy (LCE) alone and when using AI-mHRME with LCE. In this clinical trial, the investigators will build on the Brazil pilot trial data to optimize an artificial intelligence (AI) mHRME and evaluate its clinical impact and implementation potential in ethnically and socioeconomically diverse populations in the US and Brazil.
Effectiveness and Performance of an Optical Biopsy Technology for Esophageal Cancer in Brazil and the United States
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Baylor St. Luke's Medical Center, Houston, Texas, United States, 77030
Ben Taub Hospital (Harris Health Systems), Houston, Texas, United States, 77030
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18 Years to
ALL
No
Baylor College of Medicine,
Sharmila Anandasabapathy, MD, PRINCIPAL_INVESTIGATOR, Baylor College of Medicine
2027-03-01