News • Bacterial antigens
Machine learning tool predicts peptides' potential as immune activators
A deep neural network algorithm called BOTA uses bacterial genomes to identify unrecognized bacterial antigens.
AI
Article • AI structure
Machine Learning: into the pumpkin patch
Standardised and well-structured data, as well as the definition of clear objectives, are indispensable prerequisites for artificial intelligence implementation into clinical processes. ‘Ask not what artificial intelligence can do for you but what you can do for artificial intelligence!’ This variation on John F Kennedy’s famous quote comes from Dr Ben Glocker, Senior Lecturer in Medical…
Article • Connected laboratory
Digitisation and automation: Game-changers in histopathology?
Often referred to as the ‘Achilles’ heel’ of histopathology, the sample entry has posed considerable challenges in pre-analytics for several decades. We visited the Munich-based lab automation start-up Inveox GmbH. Time-intense, highly manual processes in labs are expensive, error-prone and the most common reason for irregularities in cancer diagnoses. In Germany alone, every year hundreds…
Article • Artificial Intelligence
AI allocates cases to the right radiologist
Synergy is key to ensuring Artificial Intelligence (AI) can play a critical role in helping radiologists raise their game. Integrating AI with innovative platforms to optimise workflow and make diagnosis more efficient, whilst also creating more accurate reports, offers enormous potential benefits to patients, clinicians and hospitals, according to industry specialist Tomer Zonens, Worldwide…
Article • The revolution escalates
AI image analysis: Opportunity or threat?
'Image Computing, including image analysis, artificial intelligence, artificial neural networks und deep learning, is starting a revolution,’ says Dr Paul Suetens, professor of Medical Imaging and Image Processing at University Hospital Leuven. Artificial Intelligence (AI) is not new – research in this field was carried out as far back as the 1950s – but, whilst in the early days AI learnt…
News • Pilot project
Could AI 'Audrey' be the future first response assistant?
Imagine a first responder answering the call to a natural disaster, a house fire, or an active shooter incident where there may be multiple injuries and unknown casualties. The information the responder needs to fulfill the mission is immeasurable. When you also consider the volume of data they receive from other responders, dispatch, command centers, victims, and onlookers while receiving and…
News • Pathology test
AI predicts prostate cancer progression following surgery
A pathology test that applies artificial intelligence (AI) to characterize tissue samples can accurately predict clinically significant prostate cancer disease progression following surgery, according to a study conducted at the Icahn School of Medicine at Mount Sinai.
Article • A valuable assistant
AI will transform radiologists into data scientists
Machine learning is increasingly helping radiologists to acquire faster and better quality images, and measure heart function. This is just the tip of the iceberg; artificial intelligence has far more to bring to the heart, explained Daniel Rueckert, Head of the Department of Computing at Imperial College London, during CMR 2018. Machine learning (ML) is becoming a valuable assistant for…
Article • A challenger arrives
AI – just a tool or the future of healthcare?
Neuroscientist Lynda Chin MD, Founder and CEO of Real-world Education Detection and Intervention, has little doubt: ‘Artificial intelligence to the rescue,’ she proclaimed in her keynote address at the Artificial Intelligence and Machine Learning Summit, held in Las Vegas this spring. ‘We need a system and analytics to interpret data!’ she urged, despite being well aware that building a…
News • Reinforced learning
AI masters tightrope walk of cancer treatment dosage
Using a new approach called 'reinforced learning', researchers have taught an artificial intelligence (AI) to responsibly choose the right amount of chemo- and radiotherapy for glioblastoma patients. The technique, which is insprired by behavioural psychology, has given the AI the ability to master the tightrope walk between effective tumor shrinkage and the medications' severe side effects.
News • Personalised drug treatment
AI approach to help myeloma patients
A multidisciplinary team of researchers from the National University of Singapore (NUS) has developed an artificial intelligence (AI) technology platform that could potentially change the way drug combinations are being designed, hence enabling doctors to determine the most effective drug combination for a patient quickly. Applying the platform towards drug resistant multiple myeloma, a type of…
Article • Transformation
The USA’s digital healthcare revolution
The digital revolution in healthcare in the United States is marching steadily forward, spurred by federal government regulations and financial incentives, by technological innovations, and by the necessities of increasing healthcare treatment efficiency, of lowering its cost and economic impact, and of elevating communications among providers, patients and payers to the norms of the 21st…
Article • Heard at the 14th ECDP in Helsinki
Digital pathology: Sometimes AI can outperform experts
Machine learning is adding a new dimension to pathology and already outperforming experts during some tasks, according to several speakers at the 14th European Congress on Digital Pathology (ECDP) who revealed up-to-date developments. However, whilst AI is set to herald a new future for digital pathology, Johan Lundin, associated professor for biomedical informatics and research director at the…
News • AI & Deep Learning
How to escape from data silos
Artificial Intelligence (AI) and machine learning are poised to transform healthcare, potentially freeing practitioners across many disciplines from routine tasks and saving lives through efficient early detection. Offering insight into the health of both individuals and populations, these ’deep learning‘ algorithms have the potential to process vast amounts of data and identify warning…
News • Man against machine
AI is better than dermatologists at diagnosing skin cancer
Researchers have shown for the first time that a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) is better than experienced dermatologists at detecting skin cancer. In a study published in the leading cancer journal Annals of Oncology, researchers in Germany, the USA and France trained a CNN to identify skin cancer by showing it more…
Article • The impact of AI
Radiology and radiologists: a painful divorce
AI-based applications will replace radiologists in some areas, the physicist Bram van Ginneken predicts. ‘The profession of radiologist will change profoundly,’ predicts Gram van Ginneken, Professor of Medical Image Analysis at Radboud University Medical Centre. The cause is automatic image analysis by computers (first published in a paper in 1963) and deep learning.
Sponsored • Machine Learning
Finding the right algorithms to tackle big data
Tracy Accardi, Hologic’s Vice President (Global R&D), spoke of the importance of innovation, tomosynthesis, artificial intelligence/deep learning and open dialogue with the radiology community. Hologic addresses a broad spectrum of gynaecological, perinatal, aesthetic, skeletal and breast women’s health issues. To enhance this approach, Accardi, explained the importance of working closely…
News • Innovation
Siemens Healthineers debuts new thermocycler and AI-powered interpretation software
At the 28th European Congress of Clinical Microbiology and Infectious Diseases (ECCMID 2018), Fast Track Diagnostics, a Siemens Healthineers company, launches a new molecular thermocycler, the Fast Track cycler, and the complementary new FastFinder software. The Fast Track cycler is a compact platform that enables laboratories of all sizes to implement molecular testing with simplicity and speed…
News • Digital Ecosystem
Siemens Healthineers offers new way to manage care gaps with AI
At the 2018 HIMSS Annual Conference & Exhibition, Siemens Healthineers showcases the Proactive Follow-up solution as part of its Siemens Healthineers Digital Ecosystem. The application prompts the appropriate physician to initiate a medically necessary response based on care gaps identified. For example, an incidental finding, an abnormality that appears in a radiology report intended for a…
Article • Healthcare artificial intelligence
AI – Radiology’s next frontier
Artificial intelligence (AI) technology and its role and future impact on the radiology profession was the dominant theme at RSNA 2017, whether in scientific presentations or in the technical exhibitions. Keith J Dreyer DO PhD addressed this subject head-on in his presentation ‘Healthcare AI – Radiology’s Next Frontier.’ Dr Keith Dreyer, vice chairman of radiology informatics and chief…
Article • An era of turbulence and innovation
The birth and rebirth of imaging
The New Horizons Lecture at the RSNA annual meeting is a keynote address that looks to the future, and the inventor of a major innovation in magnetic resonance imaging (MRI) technology, Daniel K Sodickson MD PhD, did just that. His lecture entitled ‘A New Light: The Birth and Rebirth of Imaging’ looked back at how MRI has evolved and forward at what it will become.
Sponsored • A discipline transforming
Adding value with AI in medical imaging
In the next five to 10 years, artificial intelligence is likely to fundamentally transform diagnostic imaging. This will by no means replace radiologists, but rather help to meet the rising demand for imaging examinations, prevent diagnostic errors, and enable sustained productivity increases.
Article • The InnerEye Project
AI drives analysis of medical images
Some time in the distant future artificial intelligence (AI) systems may displace radiologists and many other medical specialists. However, in a far more realistic future AI tools will assist radiologists by performing very complex functions with medical imaging data that are impossible or unfeasible today, according to a presentation at the RSNA/AAPM Symposium during the Radiological Society of…
News • Machine learning
Google AI now can predict cardiovascular problems from retinal scans
Google AI has made a breakthrough: successfully predicting cardiovascular problems such as heart attacks and strokes simply from images of the retina, with no blood draws or other tests necessary. This is a big step forward scientifically, Google AI officials said, because it is not imitating an existing diagnostic but rather using machine learning to uncover a surprising new way to predict these…
Video • Automation in radiology
Machine learning techniques generate clinical labels of medical scans
Researchers used machine learning techniques, including natural language processing algorithms, to identify clinical concepts in radiologist reports for CT scans, according to a study conducted at the Icahn School of Medicine at Mount Sinai and published in the journal Radiology. The technology is an important first step in the development of artificial intelligence that could interpret scans and…