3 articles tagged with #disease-detection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed an AI system that can detect tuberculosis from cough recordings with 70% accuracy using audio alone, improving to 81% when combined with clinical metadata. The study used real-world data from a phone-based app across Africa and Asia, suggesting mobile applications could enhance TB diagnosis in community health settings.
Researchers propose GraD-IBD, a graph-based machine learning model that analyzes patient diagnosis histories encoded in ICD codes to detect inflammatory bowel disease risk earlier and more efficiently than existing sequential models. The approach reformulates longitudinal diagnostic trajectories as temporally directed graphs with a novel message-passing mechanism, demonstrating improved accuracy while reducing computational complexity.
Researchers have developed a deep-learning model that can predict fruit fly development at the cellular level. The approach has potential applications for analyzing more complex tissues and organs, which could help identify early disease markers.
