23 Dec AI-Support Using Volumetric Biomarkers and Clinical Data Improves Diagnosis in MS, Autism and Alzheimer’s
MedicalResearch.com Interview with:
Dr. Sebastian Magda
Sebastian Magda, Ph.D
Director of Science & Engineering
CorTechs Labs, Inc
MedicalResearch.com: What is the background for this study?
Response: Previous studies have shown that the changes of brain structure volume and/or metabolic activity are associated with various neurological diseases.
We have created an artificial intelligence clinical decision support tool based on brain volumetric and PET metabolic activity measurements as well as other clinical measurements.
MedicalResearch.com: What are the main findings?
Response: We have found that our AI tool can classify multiple sclerosis (MS) subjects and Autism subjects with high accuracy and sensitivity. It can also provide good classification for Alzheimer’s Disease, Minor Cognitive Impairment and normal subjects.
MedicalResearch.com: What should readers take away from your report?
Response: Our AI-powered decision support tool can combine all of the information (patient age, gender and brain structure volumetric and metabolic activity measurements) to provide a diagnostic prediction. In this way it can help in clinical interpretation of a potentially large number of measurements. In addition, it also shows the measurements that are the most influential in the prediction.
MedicalResearch.com: What recommendations do you have for future research as a result of this work?
Response: MRI and tracer PET imaging combined with other clinical data, such as memory tests and genetic tests should improve the performance and should be studied.
Citation: RSNA 2019 abstract
AI-Powered Diagnostic Decision Support Using Volumetric Biomarkers and Non-Imaging Patient Data (Innovation Theater Presentation). Presented by CEO Chris N Airriess, PhD and Director of Science and Engineering, Sebastian Magda, PhD
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Last Updated on December 23, 2019 by Marie Benz MD FAAD