Abstract
Celiac disease is a disorder of the immune system that mainly affects the small intestine but can also affect the skeletal system. The diagnosis relies on histological assessment of duodenal biopsies acquired by upper digestive endoscopy. Immunological tests involve collecting a blood sample to detect if the antibodies have been produced in the body. Endoscopy is invasive and histology is time-consuming. In recent years there have been various algorithms that use artificial intelligence (AI) and neural convolutions (CNN, Convolutional Neural Network) to process images from capsule endoscopy, a non-invasive endoscopy approach, that provides magnified, high qualitative images of the small bowel mucosa, to quickly establish a diagnosis. The proposed innovative approach do not use complex learning algorithms, instead it find some artefacts in the endoscopies using kernels and use classified machine learning algorithms. Each used artefacts have a psychical meaning: atrophies of the mucosa with a visible submucosal vascular pattern; the presence of cracks (depressions) that have an appearance similar to that of dry land; reduction or complete loss of folds in the duodenum; the presence of a submerged appearance at the Kerckring folds and a low number of villi. The results obtained for video capsule endoscopy images processing reveal an accuracy of 94.1% and F1 score of 94%, which is competitive with other complex algorithms. The main goal of the present research was to demonstrate that computer-aided diagnosis of celiac disease is possible even without the use of very complex algorithms, which require expensive hardware and a lot of processing time. The use of the proposed automated images processing acquired noninvasively by capsule endoscopy would be assistive in detecting the subtle presence of villous atrophy not evident by visual inspection. It may also be useful to assess the degree of improvement of celiac. Patients on a gluten-free diet, the main treatment method for stopping the autoimmune process and improving the state of the small intestinal villi. The novelty of the work is that the algorithm uses two modified filters to properly analyse the intestine wall texture. It is proved that using the right filters, the proper diagnostic can be obtained by image processing, without the use of a complicated machine learning algorithm.
Highlights
The Celiac disease is a disorder of the immune system that mainly affects the small intestine with systemic complications[1]
Due to the limitation of the data set, it is easy to say that this study should be repeated with a greater sample size to verify if the results are correct and it should contain another type of data, the one that comes from patients with other diseases than celiac
Its main idea is to demonstrate that a diagnosis of celiac disease is possible with a computer, and it is not necessary to use very complex algorithms such as the references from Table 14
Summary
The Celiac disease (which origins came from the Greek word, Koiliakos, which means abdomen) is a disorder of the immune system that mainly affects the small intestine with systemic complications[1]. The most diagnosis relies on histological assessment of duodenal biopsies, obtained by upper digestive endoscopy. Endoscopy is the procedure performed by a medical professional to be able to see inside the human body It uses an endoscope (tubular instrument, optical lighting that transmits information through cameras) that is inserted into a hole in the body to analyse, in general, the digestive s ystem[1]. In the case of celiac disease, the upper digestive endoscopy is mandatory, as it enables duodenal biopsy. Different endoscopic techniques, such as water immersion, dye-based chromoendoscopy, and virtual chromoendoscopy (narrow band imaging) are used in order to better depict small changes of duodenal mucosa suggestive of incipient celiac d isease[1]. In order to make a more rapid and comfortable diagnosis, capsule endoscopy, a non-invasive device is used to acquire magnified, high qualitative images from small bowel
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