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Machine Learning to Predict Physiological Age Using Features Extracted From Arterial Blood Pressure (ABP) Waveforms (Case No. 2025-264)

Summary: UCLA researchers in the Department of Anesthesiology have introduced a novel machine learning model that accurately predicts physiological aging by analyzing features extracted from patients’ arterial blood pressure (ABP) waveforms. This approach enables efficient, data-driven assessment of biological aging in clinical settings to facilitate...

Published: 7/20/2026 | Inventor(s): Maxime Cannesson, Ravi Pal

Keywords(s): Aging, Anesthesiology, biomedical sensors, clinical care methodology, clinical decision support, clinical outcome, Clinical tool, Computer-Aided Diagnosis, consumer health products, Digital Health, digital health system, Fatigue (Medical) , Health and fitness, health risk assessment, Machine Learning, machine learning modeling, Medical Device, Medical diagnostics, Monitoring (Medicine), personalized medicine, Preventive Medicine, public health monitoring, Smart medical device, Structural health monitoring, Telemedicine

Category(s): Diagnostic Markers, Diagnostic Markers > Aging, Life Science Research Tools, Medical Devices

2020-763 Dental White Spot Treatment

UCLA Researchers in the department of Dentistry have developed a toothpaste formulation for the preventative treatment of dental white spots that otherwise left untreated can lead to cavity formation.BACKGROUND:White spots are a visual indicator of the process of demineralization (early caries) underneath intact dental enamel. Bacteria in the mouth,...

Published: 7/20/2026 | Inventor(s): Alireza Moshaverinia, Sahar Ansari, Benjamin Wu, Mohammad Mahdi Hasani Sadrabadi

Keywords(s): consumer health products, Dental, tooth decay, toothpaste

Category(s): Therapeutics > Dental