Endoluminal Procedures and Devices for Esophageal Tract Investigation: A Critical Review.

Diseases of the esophageal tract represent a heterogeneous class of pathological conditions for which diagnostic paradigms continue to emerge. In the last few decades, innovative diagnostic devices have been developed, and several attempts have been made to advance and standardize diagnostic algorithms to be compliant with medical procedures. To the best of our knowledge, a comprehensive review of the procedures and available technologies to investigate the esophageal tract was missing in the literature. Therefore, the proposed review aims to provide a comprehensive analysis of available endoluminal technologies and procedures to investigate esophagus health conditions. The proposed systematic review was performed using PubMed, Scopus, and Web of Science databases. Studies have been divided into categories based on the type of evaluation and measurement that the investigated technology provides. In detail, three main categories have been identified, i.e., endoluminal technologies for the (i) morphological, (ii) bio-mechanical, and (iii) electro-chemical evaluation of the esophagus.

Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients.

Objective: This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. Methods: A spectroscopic setup was prepared for acquisition of blood absorbance spectrum and tested in an operational environment. This setup is non invasive and can be applied during dialysis sessions. A support vector machine and an artificial neural network, trained with a dataset of spectra, have been implemented for the prediction of hematocrit and oxygen saturation. Results & Conclusion: Results of different machine learning algorithms are compared, showing that support vector machine is the best technique for the prediction of hematocrit and oxygen saturation.