Diagnostics

A deep neural network crafted by research specialists at Dartmouth’s Norris Cotton Cancer Center identified different types of lung adenocarcinoma as well as practicing pathologists in a recent study, according to work published March 4 in Scientific Reports.

Researchers from the Icahn School of Medicine at Mount Sinai have developed an AI platform that’s reportedly capable of detecting a range of neurodegenerative diseases in human brain tissue samples, according to a study published in the February issue of Laboratory Investigation.

Researchers at the Children’s Hospital of Philadelphia (CHOP) developed machine learning models that can detect the presence of sepsis in infants, hours before physicians. Findings from the study were published in PLOS One.

Though AI systems have shown promise for detecting skin cancer, more work is needed before they can be utilized in “real world” applications, according to researchers at the 2019 American Academy of Dermatology annual meeting in Washington, D.C.

OOVA, a Mount Sinai Health System spinout and diagnostic device company, is piloting an AI-based fertility tool that measures and monitors the concentrations of luteinizing hormone and progesterone—two key fertility hormones. The program is being piloted in collaboration with Thorne Research.

Researchers at the University of California, San Francisco found Google Translate, Google’s AI-based app, can help non-English speaking patients and their providers. However, it's not perfect, according to research published in JAMA Internal Medicine.

A new machine learning-based system, detailed in the journal Frontiers in Neurology, screens children for fetal alcohol spectrum disorder (FASD) in a quick and more cost-efficient way. The system was developed by researchers at the University of Southern California, Queen’s University in Ontario and Duke University, and will be accessible to children in more remote areas of the globe. 

An AI-based sepsis screening tool could better help physicians find patients most at risk of developing the illness after it outperformed other traditional screening methods, according to a study published in the Annals of Emergency Medicine.

Researchers have developed a machine learning system that predicts the severity of arthritis in a pediatric population, allowing for treatment to be personalized. Findings of their research were published in PLOS Medicine.

As the number of diabetes cases continues to rise in India, Google and Verily, the life sciences research organization under Alphabet, have a solution to better screen for the disease and associated eye diseases.

Researchers at the University of Alberta have developed a new AI-based software––Ensemble Algorithm with Multiple Parcellations for Schizophrenia Prediction, or EMPaSchiz––that will allow physicians to identify schizophrenia in fMRI scans with 87 percent accuracy. Their research was published in NJP Schizophrenia.

A new machine learning model allows for physicians to determine whether atypical ductal hyperplasia (ADH) could upgrade to cancer, according to new research published in JCO Clinical Cancer Informatics. The model can identify 98 percent of all malignant cases prior to surgery, while sparing 16 percent of women from unnecessary surgeries.