All Work Published on Healthcare
Researchers are establishing standards to validate the efficacy of AI agents in clinical settings.
Researchers are establishing standards to validate the efficacy of AI agents in clinical settings.
Vision-based Estimation of MDS-UPDRS Gait Scoresfor Assessing Parkinson’s Disease Motor Severity
Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments.
Parkinson’s disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments.
This brief warns that AI systems that infer patients’ race in medical settings could deepen existing healthcare disparities.
This brief warns that AI systems that infer patients’ race in medical settings could deepen existing healthcare disparities.
A specialized chatbot named Noora is helping individuals with autism spectrum disorder practice their social skills on demand.
A specialized chatbot named Noora is helping individuals with autism spectrum disorder practice their social skills on demand.
Improving AI Software for Healthcare Diagnostics
This brief explores current regulatory frameworks for AI use in radiology and calls for stronger regulatory guidance to improve testing, enhance safety, and establish performance standards.
This brief explores current regulatory frameworks for AI use in radiology and calls for stronger regulatory guidance to improve testing, enhance safety, and establish performance standards.
New Large Language Model Helps Patients Understand Their Radiology Reports
‘RadGPT’ cuts through medical jargon to answer common patient questions.
‘RadGPT’ cuts through medical jargon to answer common patient questions.
