Computer scientists working with pathologists have trained an AI tool to determine which patients with lung cancer have a higher risk of their disease coming back after treatment.
An anaesthesia team used 3D printing and virtual reality to produce an exact model of the airway of a 7-year-old girl in order to prepare properly for an operation to remove part of her lung.
EPFL students teamed up with startup IcosaMed to develop the SmartBra – the first piece of smart clothing that can be used for cancer prevention.
Researchers have developed a new approach to early diagnosis of lung cancer: a urine test that can detect the presence of proteins linked to the disease.
Thanks to smart software doctors will soon be able to detect early signs of esophageal cancer in patients with so-called Barrett’s esophagus.
To better leverage cancer data for research, scientists are developing an artificial intelligence-based natural language processing tool to improve information extraction from textual pathology reports.
By adding infrared capability to the ubiquitous, standard optical microscope, researchers hope to bring cancer diagnosis into the digital era.
Usind deep learning and digital scanning of conventional hematoxylin and eosin-stained tumor tissue sections, researchers have developed a clinically useful prognostic marker.
Using a computer algorithm, scientists at Uppsala University have identified a promising new treatment for neuroblastoma.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Radiologists assisted by deep learning based software were better able to detect malignant lung cancers on chest X-rays.
Using machine learning, researchers have built a tool that detects genetic mutations that trigger the immune system, helping identify which cancer patients are likely to benefit from immunotherapy.
Researchers from Johns Hopkins Medicine show that wearable activity trackers are a reliable tool for predicting death risk in older adults.
Researchers from Thomas Jefferson University use machine learning on ultrasound images of thyroid nodules to predict risk of malignancy.