#biomarkers News & Analysis

6 articles tagged with #biomarkers. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · May 127/10

🧠

Voice Biomarkers for Depression and Anxiety

Researchers have developed a deep learning model trained on ~65,000 speech samples from over 23,000 U.S. subjects that can detect depression and anxiety from voice biomarkers with 71% accuracy in sensitivity and specificity. The model extracts content-agnostic acoustic features combined with lexical information, demonstrating that raw speech analysis outperforms traditional hand-engineered acoustic descriptors for mental health screening.

🏢 Hugging Face

AIBullisharXiv – CS AI · May 77/10

🧠

Human-computer interactions predict mental health

Researchers have developed MAILA, a machine learning framework that predicts mental health conditions from cursor and touchscreen interactions with biomarker-level accuracy. Trained on 1.3 million self-reports from 9,500 participants, the system tracks 13 psychological dimensions and outperforms traditional self-reporting methods, potentially enabling scalable digital mental health assessment.

AIBullisharXiv – CS AI · Mar 27/1016

🧠

MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

Researchers developed MINT, a framework that transfers knowledge from MRI brain scans to speech analysis for early Alzheimer's detection. The system achieves comparable performance to speech-only methods while being grounded in neuroimaging biomarkers, enabling population-scale screening without requiring expensive MRI scans at inference.

AIBullisharXiv – CS AI · Feb 276/107

🧠

Atlas-free Brain Network Transformer

Researchers have developed an atlas-free Brain Network Transformer (BNT) that uses individualized brain parcellations from subject-specific fMRI data instead of standardized brain atlases. The approach outperformed existing methods in sex classification and brain age prediction tasks, offering improved precision and robustness for neuroimaging biomarkers and clinical diagnostics.

AINeutralarXiv – CS AI · Mar 35/105

🧠

Dynamic Spatio-Temporal Graph Neural Network for Early Detection of Pornography Addiction in Adolescents Based on Electroencephalogram Signals

Researchers developed a Dynamic Spatio-Temporal Graph Neural Network (DST-GNN) using EEG signals to detect pornography addiction in adolescents, achieving 71% F1-score with 85.71% recall. The AI system identifies brain connectivity patterns as objective biomarkers, representing a significant advancement in neurobiological detection methods.

AIBullishGoogle Research Blog · Aug 64/104

🧠

Insulin resistance prediction from wearables and routine blood biomarkers

Research demonstrates the ability to predict insulin resistance using wearable device data combined with routine blood biomarkers. This represents an advancement in personalized healthcare monitoring through AI-driven analysis of continuous health data.