AIMC Topic: Aged

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Development and External Validation of a Motor Intention-Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke.

Archives of physical medicine and rehabilitation
OBJECTIVE: To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from...

Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

Magma (New York, N.Y.)
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...

An Integrated Radiopathomics Machine Learning Model to Predict Pathological Response to Preoperative Chemotherapy in Gastric Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of pa...

Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery.

International journal of medical informatics
BACKGROUND: Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and effectiveness of using prophylactic anticoagulants for preventing DVT after spinal surgery remain cont...

Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms.

International journal of cardiology
Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart dis...

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

International journal of cardiology
BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combi...

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

Computer methods and programs in biomedicine
BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and system...

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical da...

Health economic evaluation of an artificial intelligence (AI)-based rapid nutritional diagnostic system for hospitalised patients: A multicentre, randomised controlled trial.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Malnutrition is prevalent among hospitalised patients, and increases the morbidity, mortality, and medical costs; yet nutritional assessments on admission are not routine. This study assessed the clinical and economic benefits of u...

Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.

Scientific reports
This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans wit...