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[How well does artificial intelligence detect fractures in the cervical spine on CT?].
Nederlands tijdschrift voor geneeskunde
Sep 25, 2024
OBJECTIVE: To compare diagnostic accuracy of artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT with attending radiologists.
Deep learning for blood glucose level prediction: How well do models generalize across different data sets?
PloS one
Sep 25, 2024
Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the...
Upfront surgery for intrahepatic cholangiocarcinoma: Prediction of futility using artificial intelligence.
Surgery
Sep 24, 2024
OBJECTIVE: We sought to identify patients at risk of "futile" surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)-based model based on preoperative variables.
Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice.
Endoscopy
Sep 24, 2024
BACKGROUND: Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect esophageal squamous cell carcinoma (ESCC); however, none of the previous reports were investigations in...
Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.
Academic radiology
Sep 24, 2024
RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pat...
Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.
Japanese journal of radiology
Sep 24, 2024
PURPOSE: To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter...
A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly.
Journal of affective disorders
Sep 24, 2024
BACKGROUND: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China ...
Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.
Journal of psychiatric research
Sep 24, 2024
Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suic...
Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.
Informatics for health & social care
Sep 24, 2024
INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quali...
A comprehensive comparison of machine learning models for ICH prognostication: Retrospective review of 1501 intra-cerebral hemorrhage patients from the Qatar stroke database.
Neurosurgical review
Sep 24, 2024
Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since the advent of machine learning(ML), different ML models have also been developed for sICH prognostica...
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