Abuse and Neglect, Alzheimer's - Dementia, Autism, Medical Imaging, Mental Health Research, MRI, Multiple Sclerosis, Neurology, Technology / 23.12.2019

MedicalResearch.com Interview with: 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. (more…)
Aging, Alzheimer's - Dementia, Author Interviews, MRI, Technology / 23.12.2019

MedicalResearch.com Interview with: Dr. Weidong Luo Principal Scientist CorTechs Labs  MedicalResearch.com: What is the background for this study? Response: We were interested in whether or not we can predict the age of the brain accurately from T1 weighted MRI and/or fluorodeoxyglucose (FDG) PET scans using the brain volumetric and the relative metabolic activity. The uptake of FDG is a clinical marker used to measure the uptake of glucose and therefore metabolism. Also, we were interested in the patterns of the predicted ages for Alzheimer's disease (AD) and minor cognitive impairment (MCI) subjects when using their brain measurements for age prediction in the normal brain age model.   (more…)
Author Interviews, Medical Imaging, Technology / 11.12.2019

MedicalResearch.com Interview with: Dr. David Steiner, MD PhD Google Health, USA MedicalResearch.com: What is the background for this study? Response: Advances in artificial intelligence raise promising opportunities for improved interpretation of chest X-rays and many other types of medical images. However, even before researchers begin to address the critical question of clinical validation, there is important work to be done establishing strategies for evaluating and comparing different artificial intelligence algorithms. One challenge is defining and collecting the correct clinical interpretation or “label” for the large number of chest X-rays needed to train and evaluate these algorithms. Another important challenge is evaluating the algorithm on a dataset that actually represents the diversity of the cases encountered in clinical practice. For example, it might be relatively easy to make an algorithm that performs perfectly on a few hundred or so “easy” cases, but this of course might not be particularly useful in practice. (more…)