An artificial intelligence (AI) model combining four methods of machine learning (ML) to accurately detect thyroid cancer from routine ultrasound image data has been developed by US researchers.
Computational approaches are being applied on enormous amounts of data from sequencing technologies to develop tools to help clinicians manage cancer more effectively.
Artificial intelligence (AI) could help guide the post-treatment surveillance of non-small cell lung cancer (NSCLC) patients and improve outcomes as a result, according to a new study.
The motion capture technology called Precise Marker-less could aid doctors and physiotherapists in their consultations and diagnoses for patients in need of rehabilitation.
Scientists have utilized the integration of lightweight material engineering and artificial intelligence to create an exoskeleton robot that can aid those with mobility impairments.
Researchers have developed a noninvasive and reagent-free technique for the efficient detection of COVID-19.
Researchers from Israel have developed a technology that rectifies the effects of underrepresentation of women in clinical trials using machine learning models.
Researchers used artificial intelligence to identify sets of genes that predict whether a patient will acquire severe sepsis.
A new pilot study suggests that machine learning algorithms which fuse electrocardiogram (ECG) and electronic health record data may help doctors screen for dangerous, lung-clogging blot clots.
Researchers in the UK and China have developed an AI model that can diagnose Covid-19 as well as a panel of professional radiologists, while preserving the privacy of patient data.
A novel artificial intelligence score provides a more accurate forecast of the likelihood of patients with suspected or known coronary artery disease dying within 10 years than established scores used by health professionals worldwide. The research was presented at EuroEcho 2021, a scientific congress of the European Society of Cardiology (ESC).
Researchers at the McKelvey School of Engineering at Washington University in St. Louis found a way to significantly reduce the noise and maintain image quality while reducing the laser energy needed to generate images by 80%.
Researchers have leveraged the power of digital pathology and computational modeling to detect and quantify podocytes, a specialized type of cell in the kidney that undergoes damaging changes during early-stage kidney disease.
Researchers at King's College Hospital and Queen Mary University of London have shown that a new computer-based algorithm can rank drugs used to treat primary liver cancer, based on their efficacy in reducing cancer cell growth.
Machine learning methods are being used to predict the health of the placenta from a 30-second MRI scan. Researchers hope the approach will offer an insight into the health of expectant mothers and unborn babies by detecting the early signs of dangerous conditions such as pre-eclampsia. Researchers from the School of Biomedical Engineering & Imaging Sciences at King’s College London (KCL)…
A team of researchers are developing the use of an artificial intelligence (AI) algorithm with the aim of diagnosing deep vein thrombosis (DVT) more quickly and as effectively as traditional radiologist-interpreted diagnostic scans, potentially cutting down long patient waiting lists and avoiding patients unnecessarily receiving drugs to treat DVT when they don’t have it.
Scientists use super-resolution microscopy to study previously undiscovered cellular worlds, revealing nanometer-scale details inside cells. This method revolutionized light microscopy and earned its inventors the 2014 Nobel Prize in Chemistry. In an international collaboration, AI researchers from Tübingen have now developed an algorithm that significantly accelerates this technology.
Artificial Intelligence (AI) is providing numerous opportunities across clinical care in the field of cardiovascular imaging. While challenges remain, AI is being applied in terms of diagnosis and prognosis, defining cardiovascular imaging pathways, and image acquisition and analysis. It can also help cardiologists predict which patients may do well, or which treatments are best applied to those…
Could machine learning (ML) help to predict sudden cardiac death (SCD)? According to Dr Sanjiv Narayan, Professor of Medicine at Stanford University, California, many exciting studies are using ML to predict sudden death in ways not previously possible. ‘Complex data, such as MRI geometry, very large electronic health records or continuous data streams from wearables, are difficult to probe…
El laboratorio de Genómica Biomédica del IRB Barcelona (Institute for Research in Biomedicine) ha desarrollado un método computacional que identifica las mutaciones causantes del cáncer para cada tipo de tumor.
The Biomedical Genomics laboratory at the Institute for Research in Biomedicine (IRB) Barcelona has developed a computational tool that identifies cancer driver mutations for each tumour type. This and other developments produced by the same lab seek to accelerate cancer research and provide tools to help oncologists choose the best treatment for each patient. The study has been published in the…
A research team at Universitat Autònoma de Barcelona (UAB), in collaboration with the Hospital de Mataró, developed a new machine learning-based model that predicts the risk of mortality of intensive care unit patients according to their characteristics. The research was published in the latest edition of the journal Artificial Intelligence in Medicine, with a special mention as a…
An international team of scientists led by EPFL has developed a system that combines information from the brain’s connectome – the “wiring” between neurons – and machine learning to assess and predict the outcome of stroke victims. When blood flow to the brain is somehow reduced or restricted, a person can suffer what we know as a stroke (from “ischemic stroke” in medical jargon).…
Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use, according to new WHO guidance.
University of Washington researchers have discovered that AI models—like humans—have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.