The Future of Healthcare

Atypical eye gaze is an early-emerging symptom of autism spectrum disorder (ASD) and holds promise for autism screening.

Current eye-tracking methods are expensive and require special equipment and calibration. There is a need for scalable, feasible methods for measuring eye gaze.

This case-control study examines whether a mobile app that displays strategically designed brief movies can elicit and quantify differences in eye-gaze patterns of toddlers with autism spectrum disorder (ASD) vs those with typical development.

In effect, using computational methods based on computer vision analysis, can a smartphone or tablet be used in real-world settings to reliably detect early symptoms of autism spectrum disorder? 

Findings

In this study, a mobile device application deployed on a smartphone or tablet and used during a pediatric visit detected distinctive eye-gaze patterns in toddlers with autism spectrum disorder compared with typically developing toddlers, which were characterized by reduced attention to social stimuli and deficits in coordinating gaze with speech sounds.

What this means

These methods may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.

Conclusions and Relevance

The app reliably measured both known and new gaze biomarkers that distinguished toddlers with ASD vs typical development. These novel results may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.

read the study at https://jamanetwork.com/journals/jamapediatrics/fullarticle/2779395

Two scientific leaps,  in machine learning algorithms and powerful biological imaging and sequencing tools , are increasingly being combined to spur progress in understanding diseases and advance AI itself.

Cutting-edge, machine-learning techniques are increasingly being adapted and applied to biological data, including for COVID-19.

Recently, researchers reported using a new technique to figure out how genes are expressed in individual cells and how those cells interact in people who had died with Alzheimer's disease.

Machine-learning algorithms can also be used to compare the expression of genes in cells infected with SARS-CoV-2 to cells treated with thousands of different drugs in order to try to computationally predict drugs that might inhibit the virus.

While, Algorithmic results alone don't prove the drugs are potent enough to be clinically effective. But they can help identify future targets for antivirals or they could reveal a protein researchers didn't know was important for SARS-CoV-2, providing new insight on the biology of the virus

read the original article which speaks about a lot more at https://www.axios.com/ai-machine-learning-biology-drug-development-b51d18f1-7487-400e-8e33-e6b72bd5cfad.html