Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.

Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to…

Artificial intelligence: the unstoppable revolution in ophthalmology.

Artificial Intelligence (AI) is an unstoppable force that is starting to permeate all aspects of our society as part of the revolution being brought into our lives (and into medicine) by the digital era, and accelerated by the current COVID-19 pandemic. As the population ages and developing countries move forward, AI-based systems may be a key asset in streamlining the screening, staging, and treatment planning of sight-threatening eye conditions, offloading the most tedious tasks from the experts, allowing for a greater population coverage, and bringing the best possible care to every patient. This paper presents a review of the state of the art of AI in the field of ophthalmology, focusin…

Mathematical Modeling for Neuropathic Pain: Bayesian Linear Regression and Self-Organizing Maps Applied to Carpal Tunnel Syndrome

A better understanding of the connection between risk factors associated with pain and function may assist therapists in optimizing therapeutic programs. This study applied mathematical modeling to analyze the relationship of psychological, psychophysical, and motor variables with pain, function, and symptom severity using Bayesian linear regressions (BLR) and self-organizing maps (SOMs) in carpal tunnel syndrome (CTS). The novelty of this work was a transfer of the symmetry mathematical background to a neuropathic pain condition, whose symptoms can be either unilateral or bilateral. Duration of symptoms, pain intensity, function, symptom severity, depressive levels, pinch tip grip force, a…

Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome

Identification of subgroups of patients with chronic pain provides meaningful insights into the characteristics of a specific population, helping to identify individuals at risk of chronification and to determine appropriate therapeutic strategies. This paper proposes the use of spectral clustering (SC) to distinguish subgroups (clusters) of individuals with carpal tunnel syndrome (CTS), making use of the obtained patient profiling to argue about potential management implications. SC is a powerful algorithm that builds a similarity graph among the data points (the patients), and tries to find the subsets of points that are strongly connected among themselves, but weakly connected to others.…

Patient Profiling Based on Spectral Clustering for an Enhanced Classification of Patients with Tension-Type Headache

Profiling groups of patients in clusters can provide meaningful insights into the features of the population, thus helping to identify people at risk of chronification and the development of specific therapeutic strategies. Our aim was to determine if spectral clustering is able to distinguish subgroups (clusters) of tension-type headache (TTH) patients, identify the profile of each group, and argue about potential different therapeutic interventions. A total of 208 patients (n = 208) with TTH participated. Headache intensity, frequency, and duration were collected with a 4-week diary. Anxiety and depressive levels, headache-related burden, sleep quality, health-related quality of life, pre…

Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images

Abstract Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 arc…

Regression Modeling of the Antioxidant-to-Nephroprotective Relation Shows the Pivotal Role of Oxidative Stress in Cisplatin Nephrotoxicity

The clinical utility of the chemotherapeutic drug cisplatin is significantly limited by its nephrotoxicity, which is characterized by electrolytic disorders, glomerular filtration rate decline, and azotemia. These alterations are consequences of a primary tubulopathy causing injury to proximal and distal epithelial cells, and thus tubular dysfunction. Oxidative stress plays a role in cisplatin nephrotoxicity and cytotoxicity, but its relative contribution to overall toxicity remains unknown. We studied the relation between the degree of oxidative reduction (provided by antioxidant treatment) and the extent of nephrotoxicity amelioration (i.e., nephroprotection) by means of a regression anal…

Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element me…

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…

The number of symptoms at the acute COVID-19 phase is associated with anxiety and depressive long-term post-COVID symptoms: A multicenter study

Differences in Long-COVID Symptoms between Vaccinated and Non-Vaccinated (BNT162b2 Vaccine) Hospitalized COVID-19 Survivors Infected with the Delta Variant

This study compared differences in the presence of post-COVID symptoms among vaccinated and non-vaccinated COVID-19 survivors requiring hospitalization due to the Delta (B.1.617.2) variant. This cohort study included hospitalized subjects who had survived SARS-CoV-2 infection (Delta variant) from July to August 2021 in an urban hospital in Madrid, Spain. Individuals were classified as vaccinated if they received full administration (i.e., two doses) of BNT162b2 (“Pfizer-BioNTech”) vaccines. Other vaccines were excluded. Those with just one dose of the BNT162b2 vaccine were considered as non-vaccinated. Patients were scheduled for a telephone interview at a follow-up around six months after …

Post-COVID functional limitations on daily living activities are associated with symptoms experienced at the acute phase of SARS-CoV-2 infection and internal care unit admission: A multicenter study

Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images

The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy. However, analyzing these images is complex even for experts, hence opening an opportunity for computer-aided diagnosis systems to seize. This paper proposes a fully automatic system based on Deep Learning that takes a prostate mpMRI from a PCa-suspect patient and, by leveraging the Retina U-Net detection framework, locates PCa lesions, segments them, and predicts their most likely Gleason grade group (GGG). It uses 490 mp…