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Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry.

Digestive diseases and sciences
BACKGROUND: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM).

Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnos...

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.

Current psychiatry reports
PURPOSE OF REVIEW: This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection an...

A New Consensus Model Based on Trust Interactive Weights for Intuitionistic Group Decision Making in Social Networks.

IEEE transactions on cybernetics
A promising feature for group decision making (GDM) lies in the study of the interaction between individuals. In conventional GDM research, experts are independent. This is reflected in the setting of preferences and weights. Nevertheless, each exper...

Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac.

Physics in medicine and biology
In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-linac. A convolutional neural network was trained on the dose of individual multi-leaf-collimator VMAT segments and was used to predict ...

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

Surgical innovation
INTRODUCTION: Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS).

Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model.

Chinese journal of integrative medicine
OBJECTIVE: To develop a multimodal deep-learning model for classifying Chinese medicine constitution, i.e., the balanced and unbalanced constitutions, based on inspection of tongue and face images, pulse waves from palpation, and health information f...

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and str...

An Intelligent Mental Health Identification Method for College Students: A Mixed-Method Study.

International journal of environmental research and public health
PURPOSE: Mental health assessments that combine patients' facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to ...