
Mouth and throat cancer: Robotic surgery may improve outcomes
Robotic surgery for patients with early stage, oropharyngeal squamous cell cancer is associated with improved health outcomes, including better long-term survival.
Robotic surgery for patients with early stage, oropharyngeal squamous cell cancer is associated with improved health outcomes, including better long-term survival.
Researchers report innovative use of machine learning to help understand the interplay of genetic and other breast cancer risk factors.
Myriad Genetics, Inc. announced a new collaboration with OptraHEALTH to implement a cognitive chatbot named Gene to provide genetic and financial assistance information to prospective patients.
Researchers have developed a machine learning model that can predict chemotherapy-associated nephrotoxicity.
Nanoengineers plan to develop an immunotherapy for ovarian cancer using 3D-bioprinted plant virus nanoparticles.
Scientists at Purdue University have developed a skin patch that can deliver chemotherapy into melanoma tumors in an effective and painless way.
Improving the prediction of survival indicators in patients with breast cancer using tools from artificial intelligence and probabilistic modelling is the aim of ModGraProDep.
A new AI approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report.
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 AI model for automated classification of colorectal polyps could benefit cancer screening programs by improving efficiency, reproducibility, and accuracy.
Commercially available app-based technology now makes early detection of lymphedema easier, allowing for proactive treatment.
Using a robot to treat brain aneurysms is feasible and could allow for improved precision when placing stents, coils and other devices.
Researcher have developed a computer method that uses MRI and machine learning to rapidly forecast genetic mutations in glioma tumors,
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
Using a computer algorithm, scientists at Uppsala University have identified a promising new treatment for neuroblastoma.
A computer algorithm has been shown to be as effective as human radiologists in spotting breast cancer from x-ray images.
Artificial intelligence can detect one of the most common forms of blood cancer—acute myeloid leukemia (AML)—with high reliability.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
Researchers have developed a new algorithm that enables automated detection of metastases at the level of single disseminated cancer cells in whole mice.
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
Researchers have used a chip-based sensor with an integrated laser to detect very low levels of a cancer protein biomarker in a urine sample.
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.
Scientists can determine which lung-cancer patients will benefit from expensive immunotherapy.
Radiologists assisted by deep learning based software were better able to detect malignant lung cancers on chest X-rays.
Researchers show that deep learning algorithms perform similar to human experts when classifying blood samples from patients suffering from acute myeloid leukemia.
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.
A 3D-printed cell trap developed in the laboratory at Georgia Tech captures blood cells to isolate tumor cells from a blood sample.
Researchers from Thomas Jefferson University use machine learning on ultrasound images of thyroid nodules to predict risk of malignancy.
An AI tool identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists.