AIMC Topic: Aged
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Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.
BMJ open
Sep 5, 2024
OBJECTIVES: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however,...
Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.
IEEE journal of biomedical and health informatics
Sep 5, 2024
The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed...
Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.
PloS one
Sep 5, 2024
Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fasting blood glucose levels, that result in an increased risk of heart disease and stroke. In this resear...
Integrating artificial intelligence (S-Detect software) and contrast-enhanced ultrasound for enhanced diagnosis of thyroid nodules: A comprehensive evaluation study.
Journal of clinical ultrasound : JCU
Sep 5, 2024
PURPOSE: This study aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound (CEUS) when employed individually, as well as their combined application, fo...
Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.
Journal of imaging informatics in medicine
Sep 4, 2024
Acute radiation dermatitis (ARD) is a common and distressing issue for cancer patients undergoing radiation therapy, leading to significant morbidity. Despite available treatments, ARD remains a distressing issue, necessitating further research to im...
Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners.
European radiology
Sep 4, 2024
OBJECTIVES: It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an artificial intelligence (AI)-assisted system by using quantitative computed tomography (QCT) as a reference without additional radiation exposure or ...
Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.
Internal medicine (Tokyo, Japan)
Sep 4, 2024
Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parameters for predicting major adverse cardiac events (MACEs). Methods The Nationwide Gender-specific Ath...
Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound.
Clinical breast cancer
Sep 4, 2024
PURPOSE: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model c...
Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.
ASAIO journal (American Society for Artificial Internal Organs : 1992)
Sep 4, 2024
Accurate activity classification is essential for the advancement of closed-loop control for left ventricular assist devices (LVADs), as it provides necessary feedback to adapt device operation to the patient's current state. Therefore, this study ai...
External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-reported Overactive Bladder Treatment Outcomes.
Urology
Sep 4, 2024
OBJECTIVE: To predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesi...
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