AIMC Topic: Aged, 80 and over
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FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated through machine learning models.
International journal of medical informatics
Aug 19, 2024
BACKGROUND: Frailty is an age-related syndrome characterized by loss of strength and exhaustion and associated with multi-morbidity. Early detection and prediction of the appearance of frailty could help older people age better and prevent them from ...
Machine learning to predict completion of treatment for pancreatic cancer.
Journal of surgical oncology
Aug 19, 2024
BACKGROUND: Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would c...
Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.
Cardiology
Aug 17, 2024
INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioides difficile infection (CDI). However, the risk factors for the development of CDI in HF patients hav...
Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.
Acta neuropathologica communications
Aug 17, 2024
Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveragi...
Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty.
Journal of shoulder and elbow surgery
Aug 16, 2024
BACKGROUND: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and ...
Designing an effective semantic fluency test for early MCI diagnosis with machine learning.
Computers in biology and medicine
Aug 16, 2024
Semantic fluency tests are one of the key tests used in batteries for the early detection of Mild Cognitive Impairment (MCI) as the impairment in speech and semantic memory are among the first symptoms, attracting the attention of a large number of s...
Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.
Radiological physics and technology
Aug 15, 2024
This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective st...
Development of a deep learning model for detecting lumbar vertebral fractures on CT images: An external validation.
European journal of radiology
Aug 15, 2024
OBJECTIVE: To develop and externally validate a binary classification model for lumbar vertebral body fractures based on CT images using deep learning methods.
Definition and Validation of Prognostic Phenotypes in Moderate Aortic Stenosis.
JACC. Cardiovascular imaging
Aug 14, 2024
BACKGROUND: Adverse outcomes from moderate aortic stenosis (AS) may be caused by progression to severe AS or by the effects of comorbidities. In the absence of randomized trial evidence favoring aortic valve replacement (AVR) in patients with moderat...
Phenotype
Prognosis
Female
Reproducibility of Results
Humans
Echocardiography
Middle Aged
Risk Assessment
Comorbidity
Severity of Illness Index
Risk Factors
Aged
Aged, 80 and over
Male
Unsupervised Machine Learning
Predictive Value of Tests
Heart Failure
Time Factors
Cluster Analysis
Aortic Valve Stenosis
Aortic Valve
Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT.
Ophthalmology
Aug 14, 2024
PURPOSE: To quantify morphological changes of the photoreceptors (PRs) and retinal pigment epithelium (RPE) layers under pegcetacoplan therapy in geographic atrophy (GA) using deep learning-based analysis of OCT images.
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