Artificial Intelligence may Soon Change How Physicians Approach Medical Care
Artificial intelligence may allow health care providers to combine large data sources to identify and calculate likely outcomes for individual patients which may prove helpful in diabetes care. The information is so detailed and you have access to so many patients in your database that you can make a good prediction of what’s going to happen next and diagnosis of diabetes complications.
Noninvasive Monitoring Identifies Short-term Hemodynamic Changes After TAVR
NICaS is a noninvasive bioimpedance monitoring system helped to identify short-term hemodynamic changes early after transcatheter aortic valve replacement for patients with severe aortic stenosis, researchers reported. It may provide insights into the complex physiologic changes occurring during the periprocedural time in patients and thereby promotes a more precise treatment strategy.
Heart Roundness Identified by AI Algorithm May Predict Risk for Atrial Fibrillation & Heart Failure
In an analysis of more than 30,000 cardiac MRIs using an AI algorithm, researchers found that sphericity may predict risk for certain CV conditions, even when heart size and function are normal. Healthy adults with spherical hearts, identified through a cardiac MRI images, are 31% more likely to develop atrial fibrillation and 24% more likely to develop cardiomyopathy, researchers reported.
Bionic Pancreas - The Next-Generation Device To Simplify Management of Type 1 Diabetes
Bionic pancreas is an automated insulin delivery system that requires less user input than commercially available systems. It only needs user’s body weight upon setup. The user still needs to enter meals, but with an estimate of carbohydrate amount. A 13-week trial showed that the bionic pancreas significantly reducted glycated hemoglobin than standard care in adults and children with T1D.
A Speech Analysis Smartphone App can Detect Heart Failure Events
HearO speech analysis system can detect impending heart failure events in ambulatory patients with congestive HF 3 weeks ahead of time. It includes a smartphone app which asks the patient each day to speak 5 preset sentences. From there, it is uploaded into a cloud-based system where the speech analysis occurs. It detect changes in lung fluid content or pulmonary congestion indicative of HF.
Artificial Intelligence Model That Can Predict Pre-diabetes Risk Via Facial Blood Flow Patterns
NuraLogix has developed new AI models that can predict pre-diabetes risk.The machine learning-based model was trained using the facial blood flow patterns of participants who had recently undergone a blood test for fasting blood glucose and HbA1C. In future, this touchless model will allow people to screen themselves using any device with a camera setting, including a smartphone or tablet.
