From LCSR
Capsule Endoscopy
Project Description
Capsule endoscopy has recently emerged as a valuable imaging technology for the gastrointestinal (GI) tract, especially the small bowel and the esophagus. With this technology, it has become possible to directly evaluate the gut mucosa of patients with a variety of conditions, such as obscure gastrointestinal bleeding, celiac disease and Crohn’s disease.
Although the use of capsule endoscopy is gaining rapidly, the evaluation of capsule endoscopic imagery presents numerous practical challenges for the clinician. In a typical case, the capsule acquires 50,000 or more images over an eight-hour period. The quality of these images is highly variable due to the uncontrolled motion of the capsule itself as it moves through the GI tract, the complexity of the structures being imaged, and inherent limitations of the imager itself. In practice, relatively few (often less than 100) of these images contain significant diagnostic content. As a result, it is challenging to create an effective, repeatable means for evaluating capsule endoscopic sequences.
Our goal is to address these challenges by creating a tool for semi-automated, objective, quantitative assessment of pathologic findings in capsule endoscopic data. In this project, our clinical focus will be on quantitative assessment of lesions that appear in Crohn’s disease of the small bowel. We have chosen this disease model for a number of reasons:
- Involvement of the small bowel with Crohn’s disease is characterized by discrete, easily identifiable and well-circumscribed (“punched-out”) erosions and ulcers;
- More severe mucosal disease predicts a more aggressive clinical course and, conversely, mucosal healing induced by anti-inflammatory therapies is associated with improved patient outcomes; and
- Although approximately 70% of patients with Crohn’s disease have small bowel involvement, there is an unmet need in adequately and routinely imaging the small bowel mucosa.
Our technical approach to this problem will make use of statistical learning methods to create algorithms that perform lesion classification and assessment in a manner consistent with a trained expert. We will apply these methods to first delineate areas of abnormally appearing tissue, and subsequently to perform lesion classification based on size and appearance. Our hypothesis is that appropriately constructed algorithms will be able to perform assessment of lesions appearing in capsule endoscopic images with a level of consistency and accuracy comparable to human observers.
People
- Greg Hager
- Rajesh Kumar
- Sharmi Seshamani
- Dr. Themistocles Dassopoulos, MD, Assistant Professor of Medicine, Johns Hopkins University
- Dr. Gerard E. Mullin, MD, Director of Capsule Endoscopy, Johns Hopkins Hospital
