
News • Deep learning analysis of X-rays
AI used to triage patients with chest pain
Artificial intelligence (AI) may help improve care for patients who show up at the hospital with acute chest pain, according to a new study published in Radiology.
Artificial intelligence (AI) may help improve care for patients who show up at the hospital with acute chest pain, according to a new study published in Radiology.
Transforming a regional digital pathology network into a national programme across the UK has the potential to save the NHS around £100m a year. Such a network – one that sees a centralised digital pathology image library and archive, as opposed to individual hospitals having their own infrastructure and teams to manage it – can also offer a range of other benefits alongside significant cost…
The risk for poor glycemic control in patients with type 2 diabetes can be predicted with confidence by using machine learning methods, a new study from Finland finds.
A team of researchers from Portugal and Germany tested an innovative solution to classify seizures, the main symptom of epilepsy, using infrared radar and 3D videos.
Researchers have now found that artificial intelligence (AI) can improve the effectiveness of colonoscopy in the presence of Lynch syndrome.
New research shows the power of artificial intelligence (AI) applied to endometrial carcinoma microscopy images. This could improve diagnosis and treatment of uterine cancer.
AI-based models for multimodality hybrid imaging have the potential to be a potent clinical tool but are currently held back by a lack of transparency and maturity, says Dr Irène Buvat, from the Laboratory of translational Imaging in Oncology, Institute Curie in Paris, France.
Researchers from the University of Jyväskylä and the Central Finland Health Care District have developed an AI based neural network to detect an early knee osteoarthritis from x-ray images.
A new study demonstrates that AI models, using symptom and demographic features, can help predict Covid-19 infections, providing a way for rapid screening and cost-effective infection detection.
Scientists are developing artificial intelligence (AI) and talking robots to help to detect urinary tract infections (UTIs) in vulnerable people early.
Researchers use AI to develop personalized 3D-printed joint implants so that these delicate finger parts can be replaced when necessary (e.g. after illness or injury).
Tapping the thriving Radiology AI ecosystem, Bayer recently announced three collaboration agreements for its digital platform, Calantic Digital Solutions, as well as an AI accelerator program.
In radiology, it is not about if but about when artificial intelligence (AI) will be used, said Professor Dr Tim Leiner of Utrecht University Medical Center at this year’s European Congress of Radiology in Vienna. For all those who are new to AI, the Dutch radiologist gave an overview of the lessons he and his team have learnt so far.
Radiographers could help design new artificial intelligence (AI) tools for radiation protection, Mark McEntee, professor of diagnostic radiography at University College Cork, Ireland, argued during the annual EuSoMII meeting in October.
In surgery, artificial intelligence (AI) is applied mostly in imaging, navigation, and robotic intervention. However, AI can also play a major role in preoperative planning. Objective decisions-making, optimal utilisation of operating theatres and less overtime are additional advantages that are achieved with the use of AI in surgery.
This summer, The European Commission launched I3lung, a new research initiative as a part of Horizon Europe, the EU’s research and innovation program. This research initiative aims to create a cutting-edge, decision-making tool to help clinicians and patients select the best lung cancer treatment based on each patient’s specific needs and circumstances.
New changes made to the timetable for the In vitro Diagnostic Medical Device Regulation (IVDR) across Europe could have a significant impact on manufacturers and users, an expert points out. While the extension of the transition period was a welcome step, other changes which were hoped for remain painfully absent.
Endosonography poses unique challenges for medical professionals, because two demanding disciplines have to be mastered at the same time. The use of artificial intelligence (AI) could help speed up the notoriously slow learning curve of the procedure, says Prof Dr Christoph F. Dietrich. At the Visceral Medicine Congress in Hamburg, the expert explained how AI can help endosonography achieve…
Sepsis, a life-threatening, systemic, toxic bodily reaction to infection, is often difficult to detect in its early stages. Its symptoms, including fever, shortness of breath, rapid heart rate, and confusion, are associated with many medical conditions of hospitalized patients. But if not treated rapidly, a patient may die. The Targeted Real-time Early Warning System (TREWS) for sepsis detection…
To assess diffuse liver disease, MRI is currently the modality of choice. New developments in artificial intelligence (AI) could tip the scales in favour of CT imaging. At ECR 2022 in July, experts showed how AI technology enables CT to quantify liver fat as exquisitely as MRI.
Artificial intelligence (AI) is increasing its foothold in endoscopy. Although the algorithms often detect pathologies faster than humans, their use also generates new problems. PD Dr Alexander Hann from the University Hospital Würzburg points out that the use of AI helpers can affect not only the reporting of findings – but also the person making the findings.
Clinical management of soft tissue sarcoma is particularly challenging. Dr Sebastian Foersch, researcher at the Institute of Pathology at the University Medical Center in Mainz, Germany, has used a deep learning model for diagnosis and prognosis prediction of soft tissue sarcoma using conventional histopathology slides.
Renal cell carcinoma is among the fifteen most common cancers worldwide. Dr Titus Brinker, from the German Cancer Research Center (DKFZ), looked at whether a convolutional neural network (CNN) can extract relevant image features from a typical H&E-stained slide to predict 5-year overall survival.
Deaths from cancer are currently estimated at 10 million each year worldwide. Conventional cancer staging systems aim to categorize patients into different groups with distinct outcomes. ‘However, even within a specific stage, there is often substantial variation in patient outcomes,’ Markus Plass, academic researcher from the Medical University of Graz, Austria, explained to Healthcare in…
The tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC). Sebastian Stenman, researcher from the Institute for Molecular Medicine, and the Department of Pathology at the University of Helsinki, Finland, is developing and training a deep learning algorithm using supervised learning to detect and quantify the proportion of tall cells in PTC.