The AI model was more efficient at detecting signatures of atrial septal defect (ASD) in electrocardiograms (ECG) than traditional methods.

Investigators from Brigham and Women's Hospital, a founding member of the Mass General Brigham healthcare system, and Keio University in Japan have developed a deep learning artificial intelligence model to screen electrocardiogram (ECG) for signs of atrial septal defects (ASD).

What if "looking your age" refers not to your face, but to your chest? Osaka Metropolitan University scientists have developed an advanced artificial intelligence (AI) model that utilizes chest radiographs to accurately estimate a patient's chronological age. More importantly, when there is a disparity, it can signal a correlation with chronic disease.

Diagnosing autism spectrum disorder (ASD) is still a daunting challenge because of the degree of complexity involved, requiring highly specialized professionals. Autism is a multifactorial neurodevelopment disorder with widely varying symptoms. In the United States, about 1 in 36 children have been diagnosed with ASD, according to the Centers for Disease Control and Prevention (CDC), and yet there are no biochemical markers to identify it with precision.

Older adults who play digital puzzle games have the same memory abilities as people in their 20s, a new study has shown.

The study, from the University of York, also found that adults aged 60 and over who play digital puzzle games had a greater ability to ignore irrelevant distractions, but older adults who played strategy games did not show the same improvements in memory or concentration.

Researchers at the Francis Crick Institute and UCL Queen Square Institute of Neurology, working with technology company Faculty AI, have shown that machine learning can accurately predict subtypes of Parkinson’s disease using images of patient-derived stem cells.

Their work, published today in Nature Machine Intelligence, has shown that computer models can accurately classify four subtypes of Parkinson’s disease, with one reaching an accuracy of 95%.

Deep-learning technology developed by a team of Johns Hopkins engineers and cancer researchers can accurately predict cancer-related protein fragments that may trigger an immune system response. If validated in clinical trials, the technology could help scientists overcome a major hurdle to developing personalized immunotherapies and vaccines.

Through the use of eHealth application ikHerstel, patients recover from major abdominal operations 30% faster than patients who do not use the app. That is the main conclusion of research led by Amsterdam UMC across eleven Dutch hospitals. The app aims to empower patients to feel more in control of their recovery process. The results were published today in Lancet Digital Health.