Using machine learning, an electronic nose can "smell" early signs of ovarian cancer in the blood. The method is precise and, ...

A team of researchers from Taiwan has developed PanMETAI, an AI-powered platform that analyzes metabolic fingerprints in a ...

As healthcare systems worldwide grapple with rising cancer rates, chronic diseases and limited clinical resources, ...

University of Warwick research warns that popular deep learning systems trained for cancer pathology may be relying on hidden ...

A new review highlights how machine learning is transforming the way scientists detect and measure organic pollutants in the ...

Using machine learning, an electronic nose can "smell" early signs of ovarian cancer in the blood. The method is precise and, ...

Imagine a screening method that’s more accurate in early detection than a mammogram and less expensive than an MRI. That’s ...

A machine learning-driven eNose detects ovarian cancer in blood plasma with 97 % sensitivity and specificity, offering a promising biomarker-agnostic approach.

Researchers at the Johns Hopkins Kimmel Cancer Center report that an artificial intelligence (AI)-based liquid biopsy test using genome-wide cell-free DNA (cfDNA) fragmentation patterns and repeat ...

Researchers have developed an electronic nose that can “smell” early signs of ovarian cancer in the blood. The method is precise and could eventually be used to find many different cancers.

A blood sample does not have an obvious odor to a person in a lab coat. But to an electronic nose, it can carry a chemical signature that points toward disease.