Data Science Center in Health (DASH)’s Post

👩⚕️ Picture a future where doctors can fine-tune patient treatment for cerebral infarctions or haemorrhages through the lens of a 'digital twin.' 🤖👥 🩺 In an exciting development, researchers from the European Gemini consortium in the Netherlands secured a €10 million Horizon grant from the European Commission. This funding propels the acceleration of simulation technology for assessing optimal treatments. 👯Armed with simulation tools in the form of 'digital twins,' practitioners can navigate the best course of action for patients facing cerebral infarctions or haemorrhages– and better determine optimal treatment options for patients. 🤝The consortium, together with 19 partners, highlights the collaborative spirit of Amsterdam’s life sciences sector. The synergy between the city’s health community and academia continues to drive advancements and innovation, leveraging AI’s potential in the healthcare transition. 🔗Read more here: https://lnkd.in/epJQARjm #LifeSciences #Healthcare #AI

What does it take to get into the fast-developing field of medical #ArtificialIntelligence research and entrepreneurship?      Find out in the latest episode of the NEJM AI Grand Rounds podcast, where David Ouyang, MD, a cardiologist and AI researcher at Cedars-Sinai Medical Center, discusses his journey from medical training to AI research and entrepreneurship, as well as his groundbreaking work in applying AI to cardiology imaging and the challenges of bringing AI innovations from academia to clinical practice.      The conversation with hosts Arjun Manrai, PhD, and Andrew Beam, PhD, also explore his experience conducting randomized controlled trials for AI algorithms in echocardiography, the process of commercializing research through Y Combinator, and the hurdles in reimbursement for AI-based medical devices.       The episode also considers the future of AI in cardiology, the importance of clinician involvement in AI development, and the potential impact of large language models on medical practice.       Dr. Ouyang shares insights on balancing clinical value with business considerations in health care AI and offers advice for researchers looking to conduct clinical trials for AI technologies.      Listen to the full episode hosted by NEJM AI Deputy Editors Arjun Manrai, PhD, and Andrew Beam, PhD: https://nejm.ai/ep23    #AIinMedicine 

Advancing Fracture Detection with AI 🚨 We’re excited to share that our study on fracture detection has been accepted for publication, and will be presented at MICAD conference in Manchester, UK! 🎉 Our latest research demonstrates that integrating AI solution can significantly elevate radiologist performance, achieving an impressive 14.6% average increase in sensitivity for detecting fractures in both pediatric and adult population. By enabling radiologists to identify more abnormalities, Carebot not only reduces the risk of missed injuries but also enhances the overall quality of patient care. 🏥 This advancement underscores the transformative potential of AI to boost diagnostic accuracy and support radiologists in delivering higher standards of healthcare. As AI integration continues to reshape clinical practice, it is setting new benchmarks in medical imaging and improving healthcare quality. 🚀 Jakub Dandár, Michaela Škodová, Zdenek Straka, Daniel Kvak, & Marek Biroš. Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study. MICAD 2024.

Revolutionizing Healthcare Imaging with AI: The pySTED Breakthrough!! In the dynamic field of healthcare, AI is making significant strides in imaging technology, particularly with the advent of pySTED, an AI-enhanced super-resolution microscopy framework. This Python-based platform simulates stimulated emission depletion (STED) microscopy, a technique that allows for imaging beyond the diffraction limit of light, revealing cellular structures at a nanoscale. Key Innovations: 1. Realistic Simulation: pySTED mimics the complex environment of STED microscopy, including fluorophore behavior and photobleaching, providing a realistic training ground for AI models. 2. AI-Driven Image Enhancement: By using deep learning, pySTED can generate synthetic high-resolution images from lower-resolution data, enhancing the capabilities of AI in healthcare imaging. 3. Reducing Photobleaching: The platform optimizes how images are captured, minimizing damage to samples, which is crucial for longitudinal studies in medical research. 4. AI Model Testing: Researchers can test and refine AI models within pySTED before real-world application, ensuring higher accuracy and efficiency in diagnosing and studying biological processes. 5. Education and Accessibility: The open-source nature of pySTED, coupled with its accessible interface via CoLaboratory notebooks, democratizes advanced imaging technology education and research. Stay ahead with the latest insights and breakthroughs in AI-driven healthcare. Subscribe now for free to read our upcoming newsletter- https://lnkd.in/drMdAFND #hearthealth #aihealthcare #healthcare #ainews #aiinmedicine #heartdisease #aibreakthroughs #aiinnovation #aidevelopments #cancercare #cancer #diabetes #breastcancer #aiimaging #oncology Image Source: Development of AI-assisted microscopy frameworks through realistic simulation with pySTED | Nature Machine Intelligence

Revolutionizing Healthcare Imaging with AI: The pySTED Breakthrough!! In the dynamic field of healthcare, AI is making significant strides in imaging technology, particularly with the advent of pySTED, an AI-enhanced super-resolution microscopy framework. This Python-based platform simulates stimulated emission depletion (STED) microscopy, a technique that allows for imaging beyond the diffraction limit of light, revealing cellular structures at a nanoscale. Key Innovations: 1. Realistic Simulation: pySTED mimics the complex environment of STED microscopy, including fluorophore behavior and photobleaching, providing a realistic training ground for AI models. 2. AI-Driven Image Enhancement: By using deep learning, pySTED can generate synthetic high-resolution images from lower-resolution data, enhancing the capabilities of AI in healthcare imaging. 3. Reducing Photobleaching: The platform optimizes how images are captured, minimizing damage to samples, which is crucial for longitudinal studies in medical research. 4. AI Model Testing: Researchers can test and refine AI models within pySTED before real-world application, ensuring higher accuracy and efficiency in diagnosing and studying biological processes. 5. Education and Accessibility: The open-source nature of pySTED, coupled with its accessible interface via CoLaboratory notebooks, democratizes advanced imaging technology education and research. Stay ahead with the latest insights and breakthroughs in AI-driven healthcare. Subscribe now for free to read our upcoming newsletter- https://lnkd.in/drMdAFND #hearthealth #aihealthcare #healthcare #ainews #aiinmedicine #heartdisease #aibreakthroughs #aiinnovation #aidevelopments #cancercare #cancer #diabetes #breastcancer #aiimaging #oncology Image Source: Development of AI-assisted microscopy frameworks through realistic simulation with pySTED | Nature Machine Intelligence

Revolutionizing Healthcare Imaging with AI: The pySTED Breakthrough!! In the dynamic field of healthcare, AI is making significant strides in imaging technology, particularly with the advent of pySTED, an AI-enhanced super-resolution microscopy framework. This Python-based platform simulates stimulated emission depletion (STED) microscopy, a technique that allows for imaging beyond the diffraction limit of light, revealing cellular structures at a nanoscale. Key Innovations: 1. Realistic Simulation: pySTED mimics the complex environment of STED microscopy, including fluorophore behavior and photobleaching, providing a realistic training ground for AI models. 2. AI-Driven Image Enhancement: By using deep learning, pySTED can generate synthetic high-resolution images from lower-resolution data, enhancing the capabilities of AI in healthcare imaging. 3. Reducing Photobleaching: The platform optimizes how images are captured, minimizing damage to samples, which is crucial for longitudinal studies in medical research. 4. AI Model Testing: Researchers can test and refine AI models within pySTED before real-world application, ensuring higher accuracy and efficiency in diagnosing and studying biological processes. 5. Education and Accessibility: The open-source nature of pySTED, coupled with its accessible interface via CoLaboratory notebooks, democratizes advanced imaging technology education and research. Stay ahead with the latest insights and breakthroughs in AI-driven healthcare. Subscribe now for free to read our upcoming newsletter- https://lnkd.in/drMdAFND #hearthealth #aihealthcare #healthcare #ainews #aiinmedicine #heartdisease #aibreakthroughs #aiinnovation #aidevelopments #cancercare #cancer #diabetes #breastcancer #aiimaging #oncology Image Source: Development of AI-assisted microscopy frameworks through realistic simulation with pySTED | Nature Machine Intelligence

Revolutionizing Healthcare Imaging with AI: The pySTED Breakthrough!! In the dynamic field of healthcare, AI is making significant strides in imaging technology, particularly with the advent of pySTED, an AI-enhanced super-resolution microscopy framework. This Python-based platform simulates stimulated emission depletion (STED) microscopy, a technique that allows for imaging beyond the diffraction limit of light, revealing cellular structures at a nanoscale. Key Innovations: 1. Realistic Simulation: pySTED mimics the complex environment of STED microscopy, including fluorophore behavior and photobleaching, providing a realistic training ground for AI models. 2. AI-Driven Image Enhancement: By using deep learning, pySTED can generate synthetic high-resolution images from lower-resolution data, enhancing the capabilities of AI in healthcare imaging. 3. Reducing Photobleaching: The platform optimizes how images are captured, minimizing damage to samples, which is crucial for longitudinal studies in medical research. 4. AI Model Testing: Researchers can test and refine AI models within pySTED before real-world application, ensuring higher accuracy and efficiency in diagnosing and studying biological processes. 5. Education and Accessibility: The open-source nature of pySTED, coupled with its accessible interface via CoLaboratory notebooks, democratizes advanced imaging technology education and research. Stay ahead with the latest insights and breakthroughs in AI-driven healthcare. Subscribe now for free to read our upcoming newsletter- https://lnkd.in/drMdAFND #hearthealth #aihealthcare #healthcare #ainews #aiinmedicine #heartdisease #aibreakthroughs #aiinnovation #aidevelopments #cancercare #cancer #diabetes #breastcancer #aiimaging #oncology Image Source: Development of AI-assisted microscopy frameworks through realistic simulation with pySTED | Nature Machine Intelligence

Upcoming talk: The Gist of Cancer and Other Adventures in Use-Inspired Basic Research.   Dive into the world of critical visual search tasks, from radiology to airport security, and learn how research and technology are enhancing these crucial processes. Explore the role of intuitive judgments, effective search strategies, and AI in improving performance in high-stakes situations. Click for more details.

Revolutionizing Healthcare Imaging with AI: The pySTED Breakthrough!! In the dynamic field of healthcare, AI is making significant strides in imaging technology, particularly with the advent of pySTED, an AI-enhanced super-resolution microscopy framework. This Python-based platform simulates stimulated emission depletion (STED) microscopy, a technique that allows for imaging beyond the diffraction limit of light, revealing cellular structures at a nanoscale. Key Innovations: 1. Realistic Simulation: pySTED mimics the complex environment of STED microscopy, including fluorophore behavior and photobleaching, providing a realistic training ground for AI models. 2. AI-Driven Image Enhancement: By using deep learning, pySTED can generate synthetic high-resolution images from lower-resolution data, enhancing the capabilities of AI in healthcare imaging. 3. Reducing Photobleaching: The platform optimizes how images are captured, minimizing damage to samples, which is crucial for longitudinal studies in medical research. 4. AI Model Testing: Researchers can test and refine AI models within pySTED before real-world application, ensuring higher accuracy and efficiency in diagnosing and studying biological processes. 5. Education and Accessibility: The open-source nature of pySTED, coupled with its accessible interface via CoLaboratory notebooks, democratizes advanced imaging technology education and research. Stay ahead with the latest insights and breakthroughs in AI-driven healthcare. Subscribe now for free to read our upcoming newsletter- https://lnkd.in/drMdAFND #hearthealth #aihealthcare #healthcare #ainews #aiinmedicine #heartdisease #aibreakthroughs #aiinnovation #aidevelopments #cancercare #cancer #diabetes #breastcancer #aiimaging #oncology Image Source: Development of AI-assisted microscopy frameworks through realistic simulation with pySTED | Nature Machine Intelligence

Excited to share our latest research article, "Deep Learning in Computed Tomography Pulmonary Angiography Imaging: A Dual-Pronged Approach for Pulmonary Embolism Detection." published with Elsevier in the international journal of expert systems with applications(ESWA) with impact factor of 8.5 . Our study focuses on enhancing Computer Assisted Diagnosis of PE through a classifier-guided detection method, marking a significant stride in automated PE diagnosis. #Research #DeepLearning #MedicalImaging #PE #Pulmonaryembolism