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Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.
Journal of science and medicine in sport
May 22, 2024
OBJECTIVES: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based i...
A new machine learning model to predict the prognosis of cardiogenic brain infarction.
Computers in biology and medicine
May 22, 2024
Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis or stenosis of the coronary arteries in the patient's heart, which leads to neurological deficits. T...
Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.
Computers in biology and medicine
May 22, 2024
Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not ha...
An ingenious deep learning approach for pressure injury depth evaluation with limited data.
Journal of tissue viability
May 22, 2024
BACKGROUND: The development of models using deep learning (DL) to assess pressure injuries from wound images has recently gained attention. Creating enough supervised data is important for improving performance but is time-consuming. Therefore, the d...
Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 22, 2024
BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-b...
Development and Validation of Artificial Intelligence-Based Algorithms for Predicting the Segments Debulked by Rotational Atherectomy Using Intravascular Ultrasound Images.
The American journal of cardiology
May 22, 2024
We develop and evaluate an artificial intelligence (AI)-based algorithm that uses pre-rotation atherectomy (RA) intravascular ultrasound (IVUS) images to automatically predict regions debulked by RA. A total of 2106 IVUS cross-sections from 60 patien...
Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.
Surgical endoscopy
May 22, 2024
BACKGROUND: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative ...
Telephone follow-up based on artificial intelligence technology among hypertension patients: Reliability study.
Journal of clinical hypertension (Greenwich, Conn.)
May 22, 2024
Artificial intelligence (AI) telephone is reliable for the follow-up and management of hypertensives. It takes less time and is equivalent to manual follow-up to a high degree. We conducted a reliability study to evaluate the efficiency of AI telepho...
Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis.
BMC musculoskeletal disorders
May 22, 2024
BACKGROUND: Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing event...
Machine learning models for predicting early hemorrhage progression in traumatic brain injury.
Scientific reports
May 22, 2024
This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with ...
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