AIMC Topic: Machine Learning
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Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.
Scientific reports
Jul 12, 2025
Right ventricular dysfunction (RVD) is strongly associated with increased mortality in patients with acute pulmonary embolism (PE), making its early detection crucial. Identifying RVD risk factors rapidly, accurately, and economically within the acut...
Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.
Translational psychiatry
Jul 12, 2025
Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Lan...
Incidental Finding of Coronary and Non-Coronary Artery Calcium: What Do Clinicians Need To Know?
Current atherosclerosis reports
Jul 12, 2025
PURPOSE OF REVIEW: This review summarizes the role of incidentally and non-incidentally discovered coronary artery calcification (CAC) and the evolving role of non-coronary artery calcification in atherosclerotic cardiovascular disease (ASCVD) risk a...
Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.
World journal of microbiology & biotechnology
Jul 12, 2025
Glucose oxidase (GOD), an oxidoreductase (EC 1.1.3.4), catalyzes the oxidation of β-D-glucose to gluconic acid using molecular oxygen as the electron acceptor, with concomitant generation of hydrogen peroxide. Owing to its versatile catalytic propert...
Integrated clinicopathological-radiomic-blood model for glioma survival prediction via machine learning: a multicenter cohort study.
Neurosurgical review
Jul 12, 2025
BACKGROUND: Glioma is characterized by a poor prognosis and limited possibilities for treatment. Previous studies have developed prediction models for glioma using genetic, clinical, pathological, imaging and other aspects; however, few studies have ...
Machine learning-based prediction of stone-free rate after retrograde intrarenal surgery for lower pole renal stones.
World journal of urology
Jul 12, 2025
BACKGROUND: Lower pole renal stones (LPS) present unique challenges for retrograde intrarenal surgery (RIRS) due to unfavorable anatomical features, often resulting in suboptimal stone-free rates (SFR). Recent advancements in machine learning (ML) of...
Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.
BMC genomic data
Jul 12, 2025
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...
Advancing rare neurological disorder diagnosis: Addressing challenges with systematic reviews and AI-driven MRI meta-trans learning framework for neurodegenerative disorders.
Ageing research reviews
Jul 11, 2025
Neurological Disorders (ND) affect a large portion of the global population, impacting the brain, spinal cord, and nerves. These disorders fall into categories such as NeuroDevelopmental (NDD), NeuroBiological (NBD), and NeuroDegenerative (ND) disord...
Fusion of bio-inspired optimization and machine learning for Alzheimer's biomarker analysis.
Computers in biology and medicine
Jul 11, 2025
Identification of Alzheimer's Disease (AD), especially in its early phases, presents significant challenges due to the nonexistence of reliable biomarkers and effective treatments. Clinical trials for AD medications also suffer from high failure rate...
Letter to the Editor: Complementary statistical approaches for interpreting machine learning feature importance in osteoporosis risk.
Computers in biology and medicine
Jul 11, 2025
This paper comments on the valuable contribution by Carvalho and Gavaia regarding machine learning for osteoporosis risk prediction, particularly their use of a stacking ensemble model and feature importance analysis. While acknowledging the model's ...
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