AIMC Topic: Aged

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Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.

American heart journal
BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk a...

Prospective Comparison of Free-Breathing Accelerated Cine Deep Learning Reconstruction Versus Standard Breath-Hold Cardiac MRI Sequences in Patients With Ischemic Heart Disease.

AJR. American journal of roentgenology
Cine cardiac MRI sequences require repeated breath-holds, which can be difficult for patients with ischemic heart disease (IHD). The purpose of the study was to compare a free-breathing accelerated cine sequence using deep learning (DL) reconstruct...

Combining Transcranial Direct Current Stimulation With Hand Robotic Rehabilitation in Chronic Stroke Patients: A Double-Blind Randomized Clinical Trial.

American journal of physical medicine & rehabilitation
OBJECTIVE: This study aimed to assess the impact of combining transcranial direct current stimulation with end-effector robot-assisted treatment on upper limb function, spasticity, and hand dexterity in chronic stroke patients.

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients ...

Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

Journal of nephrology
BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastos...

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study.

Surgical endoscopy
BACKGROUND: Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 an...

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Journal of epidemiology and global health
Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple ...

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

JAMA network open
IMPORTANCE: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform...

Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning.

Journal of shoulder and elbow surgery
BACKGROUND: The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recogni...