AI tool tracks infant movement to predict neurologic changes in NICU
A team of clinicians, scientists, and engineers at Mount Sinai trained a deep learning pose-recognition algorithm on video feeds of ...
Tag: Deep Learning
AI shows promise for predicting embryonic health without invasive testing
This review evaluates AI's ability to assess embryo health by analyzing images to predict chromosome conditions without invasive methods, offering potential ...
Deep learning techniques transform protein structure prediction
In the rapidly advancing field of computational biology, a newly peer-reviewed review explores the transformative role of deep learning techniques ...
AI model analyzes retinal images to detect early Alzheimer’s disease and cognitive decline
Using high-resolution retinal images, a novel AI model detects Alzheimer’s disease and mild cognitive impairment early, offering hope for more ...
New deep learning algorithm predicts effects of rare genetic variants
Whether we are predisposed to particular diseases depends to a large extent on the countless variants in our genome. However, ...
DeepPBS: A geometric AI model for accurate prediction of protein-DNA binding specificity
A new artificial intelligence model developed by USC researchers and published in Nature Methods can predict how different proteins may bind to ...
Ensuring sustainable and responsible use of AI in healthcare
The introduction of artificial intelligence (AI) has revolutionized healthcare systems. A new paper in the journal Diagnostic and Interventional Imaging ...
Cancer researchers urged to embrace AI for groundbreaking insights and efficiency
In a recent review published in the journal Nature Reviews Cancer, researchers present compelling arguments as to why a baseline ...
Ensemble deep learning models enhance early diagnosis of Alzheimer's disease using neuroimaging data
A recent Nature Mental Health study assessed the developments in ensemble deep learning (EDL) models used to characterize and estimate ...
New AI tool 'TORCH' successfully identifies cancer origins in unknown primary cases
In a recent study published in Nature Medicine, researchers developed a deep-learning approach for tumor origin differentiation using cytological histology ...
