NEW YORK (Reuters Health), Oct 31 - Capsule endoscopy to evaluate the esophagus is a reasonable alternative to conventional upper endoscopy in certain patients, according to Spanish researchers.
As lead investigator Dr. Andres Sanchez-Yague told Reuters Health, capsule endoscopy "is a valuable non-invasive screening option in patients unable or unwilling to undergo standard endoscopy."
In the September issue of the European Journal Gastroenterology and Hepatology, Dr. Sanchez-Yague and colleagues at Virgen Macarena University Hospital, Seville report on 30 consecutive capsule examinations carried out in 28 patients who had refused to undergo conventional endoscopy.
Most had symptoms of chronic gastroesophageal reflux disease (GERD) and the remainder had cirrhosis and required screening for esophageal varices. Esophageal erosions were seen in 58% of the GERD patients and varices were detected in 80% of the cirrhosis patients.
The team concludes that "esophageal capsule endoscopy is an adequate alternative" to upper GI endoscopy in this setting.
Almost all patients found the capsule easy to swallow and characterized the procedure as comfortable and, if needed, all were prepared to repeat the procedure.
The capsule approach, Dr. Sanchez-Yague continued, "allows us to study some esophageal conditions and indicate appropriate treatment in some patients that would otherwise remain untreated."
"As a screening modality," he concluded, "it will also help us recommend further diagnostic or therapeutic measures in selected patients."
By David Douglas
Last Updated: 2006-10-31 11:14:12 -0400 (Reuters Health)
Eur J Gastroenterol Hepatol 2006;18:977-983.
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
