Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify deviations from a healthy brain-aging trajectory in patients with mild cognitive impairment, according to a study published in Radiology: Artificial Intelligence. The model has the potential to aid in early detection of cognitive impairment at an individual level.

Flowchart showing the framework of the brain age prediction model. A, The imaging data were split into training and test datasets. The training dataset consisted of structural magnetic resonance imaging data from 974 healthy individuals, whereas the test dataset included data from 2 groups, 231 healthy controls and 224 aMCI subjects. B, A Conventional Statistical Parametric Mapping structural preprocessing pipeline was used to generate GMV maps in the MNI space. C, The intensity values from the GMV maps were extracted and concatenated to create a feature matrix that was then cleaned and normalized. D, The best elastic net model was obtained by performing supervised learning on the training dataset. To optimize the hyperparameters, a grid search was performed. E, The test dataset was input into the trained model. An age was predicted for every participant included in the test dataset. The PAD scores were calculated by subtracting the participant's chronological age from his or her predicted age. aMCI = amnestic mild cognitive impairment, GMV = gray matter volume, MNI = Montreal Neurologic Institute, Dartel = Diffeomorphic Anatomic Registrations Through Exponentiated Lie Algebra, PAD = predicted age difference. Chart courtesy of Radiological Society of North America

News | Artificial Intelligence | June 23, 2021
June 23, 2021 — Researchers have developed an artificial intelligence (AI)-based brain age prediction model to quantify...
GE Healthcare also recently announced Xeleris V, a new virtualized, flexible AI-powered solution that provides clinicians secure access to data from anywhere – helping them make personalized care decisions and treatment recommendations that are at the heart of precision health.
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Artificial intelligence (AI)-driven healthcare has potential to transform medical decision-making and treatment, but AI algorithms must be thoroughly tested and continuously monitored to avoid unintended consequences to patients. In JAMA Network Open, Regenstrief Institute President Peter Embí, M.D., calls for algorithmovigilance (a term he coined for scientific methods and activities relating to evaluation, monitoring, understanding and prevention of adverse effects of algorithms in healthcare) to address inherent biases in healthcare algorithms and their deployment. Image courtesy of Regenstrief Institute

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