Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual’s microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.

A multimodal biometric system is proposed in this paper. Our multimodal biometric authentication solution is based on three identifiers: iris, voice and faces. A mel-cepstral voice recognition is described first. Then, face authentication is approached by using SIFT-based features. Next, LAB-based iris recognition approach is proposed. The biometric fusion is performed at the level of the decision module.

Background: This study aims to objectively evaluate the overall quality of colonoscopies using a specially trained deep learning-based semantic segmentation neural network. This represents a modern and valuable approach for the analysis of colonoscopy frames. Methods: We collected thousands of colonoscopy frames extracted from a set of video colonoscopy files. A color-based image processing method was used to extract color features from specific regions of each colonoscopy frame, namely, the intestinal mucosa, residues, artifacts, and lumen. With these features, we automatically annotated all the colonoscopy frames and then selected the best of them to train a semantic segmentation network. This trained network was used to classify the four region types in a different set of test colonoscopy frames and extract pixel statistics that are relevant to quality evaluation. The test colonoscopies were also evaluated by colonoscopy experts using the Boston scale. Results: The deep learning semantic segmentation method obtained good results, in terms of classifying the four key regions in colonoscopy frames, and produced pixel statistics that are efficient in terms of objective quality assessment. The Spearman correlation results were as follows: BBPS vs. pixel scores: 0.69; BBPS vs. mucosa pixel percentage: 0.63; BBPS vs. residue pixel percentage: −0.47; BBPS vs. Artifact Pixel Percentage: −0.65. The agreement analysis using Cohen’s Kappa yielded a value of 0.28. The colonoscopy evaluation based on the extracted pixel statistics showed a fair level of compatibility with the experts’ evaluations. Conclusions: Our proposed deep learning semantic segmentation approach is shown to be a promising tool for evaluating the overall quality of colonoscopies and goes beyond the Boston Bowel Preparation Scale in terms of assessing colonoscopy quality. In particular, while the Boston scale focuses solely on the amount of residual content, our method can identify and quantify the percentage of colonic mucosa, residues, and artifacts, providing a more comprehensive and objective evaluation.

Iris characteristics are accurate and reliable for person identification. We used a new method to extract combined features for color and texture characterization using Dual Tree Complex Wavelet Transform. We applied this feature vectors selection on the UPOL iris color image database. The identification of the subsequent images of the same iris was obtained with competitive results.

A state of the art on the actual methods for automatic video processing is necessary, toward further development of research methods in computer assisted diagnosis and image analysis regarding the pathology of the lower gastrointestinal tract. Automatic analysis of these types of video frames might be useful in the evaluation of the correctness of the procedure, in diagnosis confirmation or verification, in e-learning, in computing statistics regarding the recurrence in time of the malignant polyps, after colonoscopy investigations and eventually, polyp or adenoma resection. New technologies were developed such as autofluorescence imaging, chromoendoscopy, narrow band imaging, etc., based on the different proprieties of the hemoglobin, blood vessels or conspicuous textures of membranes to reflect certain wavelengths of light. A discussion on the new implementations in this domain is necessary before any other attempt of new research.