This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

Subjective evaluation of multi-exposure HDR deghosting methods has been performed.Evaluation of objective metrics to assess if they can predict deghosting artefacts.HDR-VDP-2 was found to be most reliable for evaluating HDR deghosting algorithms.For most scenes, Sen etal.'s method outperforms other evaluated deghosting methods. Display Omitted To avoid motion artefacts when merging multiple exposures into a high dynamic range image, a number of HDR deghosting algorithms have been proposed. However, these algorithms do not work equally well on all types of scenes, and some may even introduce additional artefacts. As the number of proposed deghosting methods is increasing rapidly, there is an immediate need to evaluate them and compare their results. Even though subjective methods of evaluation provide reliable means of testing, they are often cumbersome and need to be repeated for each new proposed method or even its slight modification. Because of that, there is a need for objective quality metrics that will provide automatic means of evaluation of HDR deghosting algorithms. In this work, we explore several computational approaches of quantitative evaluation of multi-exposure HDR deghosting algorithms and demonstrate their results on five state-of-the-art algorithms. In order to perform a comprehensive evaluation, a new dataset consisting of 36 scenes has been created, where each scene provides a different challenge for a deghosting algorithm. The quality of HDR images produced by deghosting method is measured in a subjective experiment and then evaluated using objective metrics. As this paper is an extension of our conference paper, we add one more objective quality metric, UDQM, as an additional metric in the evaluation. Furthermore, analysis of objective and subjective experiments is performed and explained more extensively in this work. By testing correlation between objective metric and subjective scores, the results show that from the tested metrics, that HDR-VDP-2 is the most reliable metric for evaluating HDR deghosting algorithms. The results also show that for most of the tested scenes, Sen etal.s deghosting method outperforms other evaluated deghosting methods. The observations based on the obtained results can be used as a vital guide in the development of new HDR deghosting algorithms, which would be robust to a variety of scenes and could produce high quality results.

Background: A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data. Methods: This study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies (Jangi et al, Nat Commun 7:1–11, 2016), (Miyake et al, PLoS ONE 10:0137429, 2015), (McDonald et al, Msystems 3:00031–18, 2018) and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the (Jangi et al, Nat Commun 7:1–11, 2016) dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways. Results: The highest accuracy of the classification model was obtained at the genus level of taxonomy (76.82%) and the third hierarchical level of KEGG pathways (70.95%). The second dataset’s 18 MS samples (Miyake et al, PLoS ONE 10:0137429, 2015) and 18 self-reported healthy samples from the (McDonald et al, Msystems 3:00031–18, 2018) dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98%) and third hierarchical level of KEGG pathways (67.24%). The accuracy of the independent set remained very relevant. Conclusions: Our results demonstrate that the developed classification model provides a good tool that can be used to suggest the presence or absence of MS condition by collecting and analyzing gut microbiome samples. The accuracy of the model can be further increased by using sequencing methods that allow higher taxa resolution (i.e. shotgun metagenomic sequencing).

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS.