Pioneering technology developed by University College London (UCL) and Africa Health Research Institute (AHRI) researchers could transform the ability to accurately interpret HIV test results, particularly in low- and middle-income countries. Academics from the London Centre for Nanotechnology at UCL and AHRI used deep learning (artificial intelligence/AI) algorithms to improve health workers’…
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.
The contribution of Artificial intelligence (AI) has great potential in breast imaging efficiency, Professor Linda Moy MD told attendees at the 2021 Society of Breast Imaging/American College of Radiology (SBI/ACR) Breast Imaging Symposium this April. AI models for breast imaging have focused mainly on the diagnostic classification and detection of breast cancer.
Canon Medical announced the commercial launch of the Aplio i-series / Prism Edition, a complete redesign of its premium ultrasound series. In addition, Aplio a-series, Canon Medical’s routine-to-advanced imaging range has also received a significant refresh.
When viruses infect cells, changes in the cell nucleus occur, and these can be observed through fluorescence microscopy. Using fluoresence images from live cells, researchers at the University of Zurich have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses. The procedure also identifies severe acute infections at an early stage.
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.
Three leading AI scale-ups - Aidence, ScreenPoint Medical and Thirona - have launched the informative video series “Opening the black box of AI in medical imaging”.
Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer has the potential to expedite a diagnosis, accelerate treatment, and…
Computer scientists at the University of California San Diego have developed a more accurate navigation system that will allow robots to better negotiate busy clinical environments in general and emergency departments more specifically. The researchers have also developed a dataset of open source videos to help train robotic navigation systems in the future. The team, led by Professor Laurel Riek…
An artificial intelligence (AI)-driven system that automatically combs through brain MRIs for abnormalities could speed care to those who need it most, according to a new study. “There are an increasing number of MRIs that are performed, not only in the hospital but also for outpatients, so there is a real need to improve radiology workflow,” said study co-lead author Romane Gauriau, PhD,…
Researchers from the Canadian University Healh Network (UHN) have developed and validated a deep learning model to predict a patient's long-term outcome after receiving a liver transplant. First of its kind in the field of Transplantation, this model is the result of a collaboration between the Ajmera Transplant Centre and Peter Munk Cardiac Centre (PMCC). The study, published in Lancet Digital…
AI is revamping workflows and experts showed how radiologists can integrate it into their department to improve daily practice and healthcare at ECR. The panel also discussed the health economics side of AI to help radiologists define which products make more economic sense for their department. The session tackled automated organ segmentation, an interesting application for AI in radiology.
A new deep learning system to help radiologists improve their workflow efficiency when reading high volumes of screening mammograms is being developed at Johns Hopkins University’s Radiology Artificial Intelligence Lab (RAIL) in Baltimore, MD. DeepCAT (Deep Computer-Aided Triage) is focused on workflow prioritization.
Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers. In a study, the researchers report that they developed a deep learning model that, in certain conditions, was more than 71% accurate in predicting survival expectancy of lung cancer…
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…
A deep learning model to predict RNA-Seq expression of tumours from whole slide images was among the industry innovations outlined at the 7th Digital Pathology and AI Congress for Europe. Created by French-American start-up Owkin, the detail of how the company’s HE2RNA model provides virtual spatialization of gene expression was detailed to online delegates by senior translational scientist…
AI can help tackle inequities and bias in healthcare but it also brings partiality issues of its own, experts explained in a Hot Topic session entitled "Artificial Intelligence and Implications for Health Equity: Will AI Improve Equity or Increase Disparities?" at RSNA.
A software tool to predict the most effective therapy for non-small cell lung cancer (NSCLC) developed by applying deep learning artificial intelligence (AI) to positron emission tomography/computed tomography (PET/CT) images has been developed by researchers at H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida. The tool is designed to provide a noninvasive, accurate method to…
Prostate cancer radiotherapy treatments guided by MRI are increasingly being explored to help improve patient outcomes and reduce toxicities after treatment. However, this development is being held back as the MRI approach is labour intensive and requires daily adaptive treatment planning, placing significant additional demands on clinician time and oncology services. To address this, a team of…
In the first study of its kind in cancer, researchers have applied artificial intelligence to measure the amount of muscle in patients with brain tumours to help improve prognosis and treatment.
A French expert in breast imaging looked at the latest Deep Learning (DL) applications in her field, screening their strengths and weaknesses in improving breast cancer detection. It is really important to understand which types of data sets need to be checked when evaluating an AI model for image interpretation, according to Isabelle Thomassin-Naggara, Professor of Radiology at Sorbonne…
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.
Value-based healthcare is gaining momentum and radiologists must increasingly show their contribution in improving patient care. Artificial intelligence (AI) can help them to do so and brings a series of new opportunities, according to Charles E Kahn, Professor and Vice Chairman of Radiology at the University of Pennsylvania, speaking at a meeting in Madrid in January. AI can do a lot to improve…
The Center for Noise and Vibration Control at the Korea Advanced Institute of Science and Technology (KAIST) announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis. Professor…
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…