Deep learning

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Article • Workflow optimisation

The potential of AI in breast imaging efficiency

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.

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News • AI-assisted analysis

Prediciting viral infections with microscopy & deep learning

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.

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News • Combining common risk factors

Deep learning enables dual screening for cancer and CVD

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…

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News • Robotic navigation

Helping robots find their way in crowded emergency rooms

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…

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News • Incidental findings identification

AI system for brain MRIs could boost workflows

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,…

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News • Outcome prediction

Deep learning to maximize lifespan after liver transplant

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…

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Article • At ECR 2021

AI experts tackle organ segmentation and health economics

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.

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News • Survival prediction

Deep learning may lead to better lung cancer treatments

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…

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Article • AI use in clinical diagnosis

Deep learning tool predicts tumour expression from whole slide images

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…

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Article • Applications of machine learning

Training AI to predict outcomes for cancer patients

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…

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Article • Clinical decision support

AI deep learning of PET/CT images to support NSCLC treatment

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…

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Article • AI-assisted MRI segmentation

Deep learning boost for prostate cancer workflow

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…

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Article • Algorithms must meet quality criteria

Deep Learning in breast cancer detection

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…

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Article • Improving the role of radiology

Value-based healthcare: AI reveals the bigger picture

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…

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Video • Deep learning application

COVID-19 cough camera: device detects location of coughing sounds in real-time​

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…

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News • Brain tumor treatment network

'Federated learning' AI approach allows hospitals to share patient data privately

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…

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News • Deep learning in imaging

1.5T MR system receives FDA clearance for AI-based image reconstruction technology

Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Orian 1.5T MR system, continuing to expand access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available on the Vantage Galan 3T MR system and across a majority of Canon Medical’s CT product portfolio, uses a deep learning…

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