Cellular Deepfake Engine: A Dual-GAN Framework Redefining Data Generation for Biomedical Imaging AI
This study presents the Cellular Deepfake Engine, a novel generative AI framework designed to produce high-fidelity, biologically plausible microscopy images across three domains: blood cells, nuclei, and cancerous cells.
Posted by buchanle on Wednesday, June 3rd, 2026 in May 2026, Biomedical images, Blood cell, Deep learning, Generative Adversarial Network, Nuclei
A Machine Learning Framework for Predicting Speech Outcomes in Cochlear Implant Recipients Using Genetic Markers
This study develops an AI-based Random Forest framework that integrates genetic variants and demographic data to accurately predict speech outcomes after cochlear implantation, supporting personalized prognosis and rehabilitation strategies.
Posted by buchanle on Friday, May 15th, 2026 in May 2026, Cochlear implants, genotype, hearing loss, Machine learning, speech outcomes
Rhythm of Life: Revolutionizing cardiac diagnostics with AI-driven lub-dub analysis
This study leverages machine learning algorithms to analyze "lub-dub" heart sounds from digital stethoscopes, achieving 98% accuracy in detecting abnormal heartbeat-related heart diseases, offering a breakthrough in non-invasive cardiac diagnostics.
Posted by buchanle on Friday, June 20th, 2025 in May 2025, CVDs, Heart sounds, LSTM, Machine learning, MFCC
Cognitive, Genetic, and Lifestyle Synergy: A Machine Learning Approach to Alzheimer’s Diagnosis
This study employs a multidisciplinary approach, integrating machine learning techniques, gene expression analysis, clinical assessments, and lifestyle data to uncover potential biomarkers and therapeutic targets for Alzheimer's disease
Posted by buchanle on Thursday, June 19th, 2025 in May 2025, Alzheimer’s Disease, Cognitive Scores, Diagnosis, Gene Expression, Machine learning