Artificial Intelligence

An AI approach developed by Dutch researchers performed similarly to radiologists at detecting breast cancer, according to a multi-center, multi-dataset study published March 5 in the Journal of the National Cancer Institute.

The researchers believe their algorithm could help pathologists classify the histologic patterns of lung adenocarcinoma—the most common form of the disease—and potentially lead to more accurate staging.

The American College of Radiology (ACR) is asking for comments to be submitted by April 15.

Google and its sister company Verily announced on Monday, Feb. 25, the development of an AI-based algorithm that can screen eye imaging exams for diabetic retinopathy and diabetic macular edema—two of the leading causes of preventable blindness in adults with diabetes, according to a recent report by CNBC.  

A new AI-based software called the Ensemble Algorithm with Multiple Parcellations for Schizophrenia Prediction, or EMPaSchiz, can identify schizophrenia on fMRI scans with 87 percent accuracy, according to a recent report by AI in Healthcare.  

Mammography is an essential screening and diagnostic tool for the detection of breast cancer and the assessment of breast density. But, according to Victoria L. Mango, MD, a breast radiologist at Memorial Sloan Kettering Cancer Center in New York City, AI can help breast imagers and physicians see beyond basic breast density information provided by mammographic images and improve clinical management overall.

Researchers have created a machine learning model that identified 98 percent of malignant atypical ductal hyperplasia (ADH) lesions prior to surgery, according to a single-center study published in JCO Clinical Cancer Informatics. The approach saved 16 percent of women from unnecessary surgery.

There is an "immaturity" between machines and humans, said Paul J. Chang, MD, of the University of Chicago. Unless radiology departments augment their current IT infrastructure, AI could become another technological-driver of burnout.

For AI to become clinically feasible in women’s imaging, it must excel in the areas of performance, time, workflow and cost, according to an opinion piece published online in the American Journal of Roentgenology.  

The software, which analyzes tumors on CT scans, was up to four-times more accurate at predicting ovarian cancer deaths compared to standard methods, according to research published Feb. 15 in Nature Communications.

The new platform, affectionately called ‘Herman,' analyzes complex patterns in images of pathogen and human cell interactions, and can do so in a fraction of the time normally required.

A multi-institutional team of researchers has developed a new AI learning algorithm that can distinguish between low- and high-risk prostate cancer from multiparametric MRI (mpMRI) scans with higher sensitivity and predictive value than current risk assessment approaches, according to research published online Feb. 7 in the journal Scientific Reports.