AIMC Topic: Middle Aged

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Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.

Orphanet journal of rare diseases
BACKGROUND: Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals,...

Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI.

Scientific reports
Accurate detection and classification of clinically significant prostate cancer remain critical challenges in medical imaging. Despite numerous studies focusing on feature extraction and classification, none have systematically assessed the impact of...

Development of a machine learning-based interface for insulin dependency prediction using clinical data.

Scientific reports
Diabetes mellitus is a major global health burden, and early identification of insulin dependency is important for timely intervention. This study developed an artificial intelligence-based diagnostic system using a real-world clinical dataset of 100...

Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.

Scientific reports
We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the ...

Introducing FREM: a decision-support approach for automated identification of individuals at high imminent fracture risk.

Archives of osteoporosis
UNLABELLED: This study used explainable AI to improve the Danish FREM model for predicting one-year risk of major osteoporotic fractures in over 2.4 million individuals aged ≥ 45. A DART boosting algorithm improved performance (AUC 0.77), with explai...

Sentiment analysis of cancer screening in Chinese social media: Qualitative studies based on machine learning.

PloS one
PURPOSE: Explore public perceptions and sentiments about cancer screening on social media. The dissemination of misinformation and negative attitudes continue to impede the access of many individuals with perceived risk to cancer screening services d...

Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective.

PloS one
As artificial intelligence (AI) systems become increasingly integrated into decision-making across various sectors, understanding public trust in these systems is more crucial than ever. This study presents a quantitative analysis of survey data from...

Differentiation of light chain cardiac amyloidosis and hypertrophic cardiomyopathy by ensemble machine learning-based radiomic analysis of cardiac magnetic resonance.

Orphanet journal of rare diseases
BACKGROUND: We aim to assess the diagnosis performance of an ensemble machine learning (ML) based radiomic analysis of multiparametric cardiac magnetic resonance (CMR) to differentiate light chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomy...

Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.

BMC medical informatics and decision making
BACKGROUND: Myocardial infarction (MI) is a life-threatening condition caused by sudden interruption of blood supply to the heart. Electrocardiogram (ECG) is the primary tool for MI diagnosis, but interpretation challenges exist. This study aimed to ...