High concordance between trained nurses and gastroenterologists in evaluating recordings of small bowel video capsule endoscopy (VCE)

Background & Aims: The video capsule endoscopy (VCE) is an accurate and validated tool to investigate the entire small bowel mucosa, but VCE recordings interpretation by the gastroenterologist is time-consuming. A pre-reading of VCE recordings by an expert nurse could be accurate and cost saving. We assessed the concordance between nurses and gastroenterologists in detecting lesions on VCE examinations. Methods: This was a prospective study enrolling consecutive patients who had undergone VCE in clinical practice. Two trained nurses and two expert gastroenterologists participated in the study. At VCE pre-reading the nurses selected any abnormalities, saved them as “thumbnails” and classified the detected lesions as a vascular abnormality, ulcerative lesion, polyp, tumor mass, and unclassified lesion. Then, the gastroenterologist evaluated and interpreted the selected lesions and, successively, reviewed the entire video for potential missed lesions. The time for VCE evaluation was recorded. Results: A total of 95 VCE procedures performed on consecutive patients (M/F: 47/48; mean age: 63 ± 12 years, range: 27−86 years) were evaluated. Overall, the nurses detected at least one lesion in 54 (56.8%) patients. There was total agreement between nurses and gastroenterologists, no missing lesions being discovered at a second look of the entire VCE recording by the physician. The pre-reading procedure by nurse allowed a time reduction of medical evaluation from 49 (33-69) to 10 (8-16) minutes (difference:-79.6%). Conclusions: Our data suggest that trained nurses can accurately identify and select relevant lesions in thumbnails that subsequently were faster reviewed by the gastroenterologist for a final diagnosis. This could significantly reduce the cost of VCE procedure

An efficient method to classify GI tract images from WCE using visual words

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection

Pre-processing Technique for Wireless Capsule Endoscopy Image Enhancement

Wireless capsule endoscopy (WCE) is used to examine human digestive tract in order to detect abnormal area. However, it has been a challenging task to detect abnormal area such as bleeding due to poor quality and dark images of WCE. In this paper, pre-processing technique is introduced to ease classification of the bleeding area. Anisotropic contrast diffusion method is employed in our pre-processing technique as a contrast enhancement of the images. There is a drawback to the method proposed B. Li in which the quality of WCE image is degraded when the number of iteration increases. To solve this problem, variance is employed in our proposed method. To further enhance WCE image, Discrete Cosine Transform is used with anisotropic contrast diffusion. Experimental results show that both proposed contrast enhancement algorithm and sharpening WCE image algorithm provide better performance compared with B. Li’s algorithm since SDME and EBCM value is stable whenever number of iterations increases, and sharpness measurement using gradient and PSNR are both improved by 31.5% and 20.3% respectively

Optimising the performance and interpretation of small bowel capsule endoscopy

Small bowel capsule endoscopy has become a commonly used tool in the investigation of gastrointestinal symptoms and is now widely available in clinical practice. In contrast to conventional endoscopy, there is a lack of clear consensus on when competency is achieved or the way in which capsule endoscopy should be performed in order to maintain quality and clinical accuracy. Here we explore the evidence on the key factors that influence the quality of small bowel capsule endoscopy services