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Neural network generates lung ventilation images from CT scans – Physics World

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Incorporating ventilation images into radiotherapy plans to treat lung cancer could reduce the incidence of debilitating radiation-induced lung injuries, such as radiation pneumonitis and radiation fibrosis. Specifically, ventilation imaging can be used to adapt radiation treatment plans to reduce the dose to high-functioning lung. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) scans are the gold standard of ventilation imaging. However, these modalities are not always readily available and the cost of such exams may be prohibitive. As such, researchers are investigating the feasibility of alternatives such as MR or CT ventilation imaging.


Focus on machine learning models in medical imaging – Physics World

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Join the audience for an AI in Medical Physics Week live webinar at 3 p.m. BST on 23 June 2022 based on IOP Publishing's special issue, Focus on Machine Learning Models in Medical Imaging Want to take part in this webinar? An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research. Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research.


Telix and Invicro Advance AI Partnership

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Telix Pharmaceuticals Limited announces that it has advanced a partnership with Invicro LLC (Invicro), a global, industry-leading imaging CRO, and part of REALM IDx, Inc., to develop an artificial intelligence (AI) platform to accompany Telix's PSMA-PET imaging agent, Illuccix (kit for the preparation of gallium Ga 68 gozetotide) – known as TelixAI. TelixAI seeks to increase the efficiency and reproducibility of clinicians' imaging assessments using advanced analysis capabilities with an initial focus on prostate cancer. The platform is designed to do this by automatically separating healthy versus abnormal tracer uptake and then classifies lesions as either visceral (soft tissue) or bone lesions. Invicro has a depth of experience in AI, machine learning and algorithm development for medical imaging. Its industry leading medical image analyst team consists of over fifty medical image processing scientists.


AI recognition of patient race in medical imaging: a modelling study

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Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.


La veille de la cybersécurité

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In healthcare and medical imaging, computer vision has shown considerable promise. However, as technology advances, a growing number of medicinal applications are becoming available. To run computer vision in health care applications, nevertheless, privacy-preserving deep learning and picture identification will be necessary. As a result, Edge AI will be a crucial technology for bringing deep learning from the cloud to the edge. Edge devices interpret video streams in real-time without transferring sensitive visual data to the cloud by conducting machine learning activities on-device.


Top 8 Computer Vision Techniques Entwined with Deep Learning

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In healthcare and medical imaging, computer vision has shown considerable promise. However, as technology advances, a growing number of medicinal applications are becoming available. To run computer vision in health care applications, nevertheless, privacy-preserving deep learning and picture identification will be necessary. As a result, Edge AI will be a crucial technology for bringing deep learning from the cloud to the edge. Edge devices interpret video streams in real-time without transferring sensitive visual data to the cloud by conducting machine learning activities on-device.


Can Artificial Intelligence Help See Cancer in New Ways?

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Two identical black and white pictures of murky shapes sit side-by-side on a computer screen. On the left side, Ismail Baris Turkbey, MD, a radiologist with 15 years of experience, has outlined an area where the fuzzy shapes represent what he believes is a creeping, growing prostate cancer. On the other side of the screen, an artificial intelligence (AI) computer program has done the same--and the results are nearly identical. The black and white image is an MRI scan from someone with prostate cancer, and the AI program has analyzed thousands of them. "The [AI] model finds the prostate and outlines cancer-suspicious areas without any human supervision," Turkbey explains.


Vara collaborates with researchers at Karolinska Institutet for independent AI evaluation

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Vara, the Berlin-based deep tech startup on a mission to provide every woman worldwide with life-saving access to better breast cancer screening, is today announcing a collaboration with researchers from Sweden's world-renowned medical university Karolinska Institutet. The objective of the collaboration is to independently evaluate Vara's AI model for mammography screening, including comparisons with other similar AI systems. Following publication, the results will be used in the creation of a platform to validate AI systems being used in breast imaging, known as the VAI-B Platform (Validation of AI in Breast Imaging). The VAI-B Platform is part of a Swedish-born project financed by Vinnova, Sweden's innovation agency, and Regional Cancer Centers in Collaboration. It is intended to be used as a national and, possibly, international resource for the validation of AI systems in breast imaging.


The Future of A.I. in Healthcare

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This article is going to be a snapshot of some things going on in Artificial Intelligence at the intersection of healthcare. Are you an iOS User and like Online surveys? Jasmine Sun asked me to share this opportunity with you guys: Participate in a Substack reader interview. They are looking for Substack users who aren't power users. I most recently covered in the A.I. intersection of Healthcare the following topics: Artificial Intelligence is Taking on Parkinson's Disease.


From AI model to software medical device: Why the algorithm is only a fraction of the work - Aidence

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"For every $1 you spend developing an algorithm, you must spend $100 to deploy and support it." If you're not familiar with our industry, this may sound counterintuitive. The development of AI clinical solutions does not consist solely of modelling. It is a long and challenging process, from gathering and curating medical data to training, testing, validating, and certifying the model; deploying it in the hospitals' complex IT landscape; maintaining and improving its performance. In this article, I zoom in on the development of a'complete' AI solution, based on our approach with Veye Lung Nodules, a medical device currently used in over 70 European sites. The story of Veye is, in many ways, the story of building Aidence, from founding to our recent acquisition.