Artificial intelligence (AI) image reconstruction dramatically reduces magnetic resonance imaging (MRI) scan time, according to new research. The first clinical study comparing AI-accelerated knee MRI scans with conventional scans shows that the AI scans are not only diagnostically interchangeable with conventional ones, but also produce higher quality images. Results of this interchangeability…
The advent of digital pathology is offering a unique opportunity to develop computerized image analysis methods to diagnose disease and predict outcomes for cancer patients from histopathology tissue sections. Such advances can help predict risk of recurrence, disease aggressiveness and long-term survival, according to a leading expert in the field, Professor Anant Madabhushi from Case Western…
Predicting cancer outcome could help with a clinical decision regarding a patient’s treatment. In his keynote speech during the online ‘7th Digital Pathology and AI Congress: Europe’, Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institute, discussed ‘Outcome and…
The U.S. Food and Drug Administration released the agency’s first Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. This action plan describes a multi-pronged approach to advance the Agency’s oversight of AI/ML-based medical software. “This action plan outlines the FDA’s next steps towards furthering oversight for AI/ML-based SaMD,”…
Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed. The intent of this work is to speed up drug repurposing, which is not a new concept – think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic…
Doctors at Addenbrooke’s Hospital in Cambridge aim to drastically cut cancer waiting times by using artificial intelligence (AI) to automate lengthy radiotherapy preparations. The AI technology, known as InnerEye, is the result of an eight-year collaboration between researchers at Cambridge-based Microsoft Research, Addenbrooke’s Hospital and the University of Cambridge.
Researchers in Finland have developed a machine learning model that can help us treat cancer more effectively.
A study published by The BMJ finds that machine learning models have similar performance to traditional statistical models and share similar uncertainty in making risk predictions for individual patients.
The field of AI-enhanced imaging provides radiologists with an unprecedented opportunity to shape patient care, leading Austrian radiologist Katja Pinker-Domenig explained at ECR 2020.
Researchers have developed a novel three-dimensional imaging system to diagnose cervical cancer faster, non-invasively and more efficiently than conventional methods.
To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study…
Today, advances in healthcare are more than ever driven by information technology and (bio)engineering. From 3D printed and smart bionic prosthetics to augmented surgeries and AI – what used to be stuff of science fiction has become medical reality.
A new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report in the journal IEEE Access. Scientists in India and Japan, including from Kyoto University’s Institute for Integrated Cell-Material Sciences (iCeMS), developed the method to help clinicians choose the most effective treatment strategy for individual…
The Society for Imaging Informatics in Medicine (SIIM) and the International Skin Imaging Collaboration (ISIC) are working together to host a machine learning challenge on melanoma classification, using the ISIC archive which contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions. Image contributors include: Hospital Clínic de Barcelona,…
Machine learning and AI are highly unstable in medical image reconstruction, and may lead to false positives and false negatives, a new study suggests. A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad…
A new app, which will be used to collect data to develop machine learning algorithms that could automatically detect whether a person is suffering from COVID-19 based on the sound of their voice, their breathing and coughing, has been launched by researchers at the University of Cambridge. The COVID-19 Sounds App is now available as a web app for Chrome and Firefox browsers. Versions for Android…
Researchers have developed the first blood test that can accurately detect more than 50 types of cancer and identify in which tissue the cancer originated, often before there are any clinical signs or symptoms of the disease. In a paper published in the leading cancer journal Annals of Oncology, the researchers show that the test, which could eventually be used in national cancer screening…
Researchers at ETH Zurich and Inselspital, Bern University Hospital, have developed a method for predicting circulatory failure in patients in intensive care units (ICU) – enabling clinicians to intervene at an early stage. Their approach uses machine learning methods to evaluate an extensive body of patient data. Patients in a hospital’s ICU are kept under close observation: clinicians…
Artificial intelligence (AI) has the potential to improve efficiencies and precision in sleep medicine, resulting in more patient-centered care and better outcomes, according to a new position statement from the American Academy of Sleep Medicine. Published in the Journal of Clinical Sleep Medicine, the position statement was developed by the AASM’s Artificial Intelligence in Sleep Medicine…
A simple blood test coupled with artificial intelligence (AI) analysis could help spot the signs of a brain tumour sooner in patients. Brain tumour diagnosis is difficult: patients often see their family doctor (GP) several times before referral for a scan. However, research presented at the 2019 National Cancer Research Institute (NCRI) Cancer Conference in Glasgow last November suggests the…
Researchers at Linköping University (LiU) have developed an algorithm that can identify patients at a higher risk of septic shock, a life-threatening condition that is difficult for doctors to predict. At the same time, it is important to recognise the symptoms as early as possible, since early treatment increases the chance of survival. A group of LiU researchers is using artificial…
Researchers at Osaka University have developed a computer method that uses magnetic resonance imaging (MRI) and machine learning to rapidly forecast genetic mutations in glioma tumors, which occur in the brain or spine. The work may help glioma patients to receive more suitable treatment faster, giving better outcomes. The research was recently published in Scientific Reports. Cancer treatment…
Using lipidomics, a technique that measures the composition of blood lipids at a molecular level, and machine learning, researchers at Lund University in Sweden have identified a blood lipid profile that improves the possibility to assess, several years in advance, the risk of developing type 2 diabetes. The blood lipid profile can also be linked to a certain diet and degree of physical activity.…
Crucial early diagnosis of dementia in general practice could improve thanks to a computer model designed in a collaboration between Brighton and Sussex Medical School (BSMS) and astrophysicists at the University of Sussex. Currently, only two-thirds of people with dementia in the UK receive a formal diagnosis, and many receive it late in the disease process, meaning that a large number are…
Researchers say a new method to analyse data from individual human cells could be a step-change for diagnosing some of the most devastating diseases, including cancer and autoimmune disease. By combining single cell analysis techniques with machine learning algorithms, a team led by researchers at the Garvan Institute of Medical Research has developed a method to ‘fingerprint’ human cells.…