Sciqst’s Post

Introducing FH-POISE's #innovative capabilities in the early detection and management of glaucoma, powered by #advanced AI and deep learning models. FH-POISE transforms ocular #diagnostics with precise heatmap visualizations and #detailed AI-generated reports, allowing for thorough risk assessments and improved clinical decision-making.   With robust image processing #algorithms, FH-POISE not only addresses glaucoma but also provides insights into other #ocular conditions such as diabetic retinopathy, cataracts, and dry eye.   Beyond eye care, FH-POISE extends its capabilities into #cardiovascular detection, with ongoing trials for #neurological and #renal conditions, ensuring #holistic healthcare solutions with unmatched #accuracy and #reliability. Midhula Vijayan, PhD Dr. Deepthi K Prasad, PhD #ForusHealth #POISE #Glaucoma #ArtificialIntelligence #Healthcare #VisionTech #OcularHealth #PrecisionDiagnosis #TechInnovation #VisionForTomorrow

𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐎𝐩𝐡𝐭𝐡𝐚𝐥𝐦𝐨𝐥𝐨𝐠𝐲 👁️🤖 🔍 Early Detection & Diagnosis: AI-driven tools are pioneering the early detection of ocular diseases like glaucoma and diabetic retinopathy, ensuring timely intervention and better outcomes. 📈 📊 Data-Driven Precision: By analyzing vast datasets, AI models achieve precision in diagnosing complex conditions, leading to personalized patient care and bespoke treatment plans. 🎯 🛠️ Enhanced Clinical Workflow: From automating routine exams to streamlining patient records, AI optimizes clinical efficiency, allowing researchers and clinicians more time for critical decision-making. ⏱️ 🔬 Cutting-Edge Research Insights: Harness AI algorithms for groundbreaking experiments and hypothesis generation, unlocking new dimensions in ophthalmic research. 🌟 For a deep dive into the latest breakthroughs, generate comprehensive literature reviews with SciQst: https://www.sciqst.com #Ophthalmology #AIRevolution #MedicalResearch #FutureOfHealthcare

It's amazing to see how the narrative around opportunistic screening is evolving in the US. I remember advocating for this approach, particularly for lung cancer, about three years ago, but it garnered little interest—especially when "X-Ray" was mentioned. Fast forward to today, Qure.ai, along with its partners, has built substantial evidence. With support from major stakeholders in the field, we've proven this model and are intending to drive a significant shift in the lung cancer landscape. Some key points to consider for those not in the know: -A very small fraction of eligible Americans have been screened for lung cancer. -A significant portion of lung cancers are missed on plain film imaging, mainly due to the ever growing burden on radiologists and speed at which they are reading today -A substantial number of cases are never followed up, leading to high rates of patient leakage. -Only a small percentage of cases are diagnosed at an early stage, and an even smaller percentage of lung cancer patients undergo operative therapy. -The five-year survival rate after diagnosis remains alarmingly low. -LUNG CANCER IS STILL THE LEADING CAUSE OF CANCER DEATH IN THE UNITED STATES. The economic impact on health systems is profound. But more importantly opportunistic screening has the potential to significantly improve patient outcomes and survival rates. By identifying lung cancer at earlier stages, when treatment options are more effective, we can increase the chances of successful interventions and extend survival. This approach not only benefits patients but also supports health systems in achieving better long-term outcomes, both clinically and economically. AABIP Bhargava ReddyDara KelleherSamir Shah, MD, MMM FACR Dhruv PandeyRanjana Devi Prashant Warier

The ITEA project 𝗔𝗦𝗦𝗜𝗦𝗧 (🇧🇪🇳🇱🇸🇪🇹🇷) integrates AI to improve diagnosis, treatment, and patient care across multiple healthcare use cases. 🧠 One of their use case focuses on intracranial haemorrhage (ICH), a life-threatening condition that requires urgent detection. Using advanced AI, the project helps quickly identify ICH and differentiate it from ischemic stroke, supporting faster, life-saving decisions. 📽️ Watch the video to see how radiology departments in Türkiye are using AI to elevate patient care and achieve better outcomes in critical situations. ➡ https://lnkd.in/em74rpK4 #HealthcareInnovation #AI #IntracranialHaemorrhage #ICH Robert Hofsink

"AI can be used to look for incidental findings to act as a second set of eyes" says American College of Radiology CEO in an interview with Radiology Business. We couldn't agree more! 💥 The engaging article continues, "opportunistic screening algorithms will likely offer health systems a new way to not only better serve patients by catching diseases earlier when they are easier to treat or prevent, but also serve as an entry point for additional testing and treatments without large amounts of time or investment to create new screening programs." 👉 Take the example of #lungcancer - Chest X-ray AI has the power to support incidentally and cast the detection net wider than traditional screening initiatives. Samir Shah, MD, MMM FACR Nate Hunter Dhruv Pandey Bhargava Reddy Ranjana Devi Sagar Sen https://lnkd.in/gMkDpyGY

ATTEND FOR FREE: https://buff.ly/3SwJMi3 At the Annual Ophthalmic AI Summit, attendees will: ✅ Discuss the testing and validation behind AI systems ✅ Identify practice management considerations associated with AI systems ✅ Review the potential of AI to tailor therapies based on distinct disease characteristics ✅ Describe how AI-assisted screening and diagnosis can improve practice efficiencies and patient outcomes ✅ Explain the current limitations of AI and challenges associated with its use ✅ Demonstrate real-world applications of AI-related algorithms in various disease states including but not limited to Age-related macular degeneration, diabetic retinopathy, geographic atrophy, uveitis, and inherited retinal disorders. #Ophthalmology #Medicine #Research #Vision #VisionResearch #Innovation #ArtificialIntelligence #AI #ClinicalCare

FH-POISE leverages advanced deep #learning based AI models to enhance the #detection and management of a range of #ocular conditions, including diabetic retinopathy, glaucoma, cataracts, and dry eye conditions. With advanced #AI and image processing #algorithms, FH-POISE delivers precise diagnostics through heat maps and detailed AI-generated reports that offer risk #assessments for each condition.   Beyond ocular health, FH-POISE also provides #cardiovascular disease #detection through a hybrid AI model, with trials underway for neurological and nephrological-related conditions. Validated on extensive datasets, FH-POISE delivers reliable #results with high #sensitivity and #specificity, ensuring accurate detection and analysis of ocular and systemic diseases for #enhanced clinical decision-making. Midhula Vijayan, PhD Dr. Deepthi K Prasad, PhD #ForusHealth #POISE #ArtificialIntelligence #Healthcare #VisionTech #OcularHealth #PrecisionDiagnosis #TechInnovation #VisionForTomorrow

In a notable achievement at the Innovate Thyroid Eye Disease (TED) 2024 Multidisciplinary Symposium, Dr. Jimmy Uddin and Dr. Mohsan Malik, renowned ophthalmologists, secured best poster prize for their groundbreaking poster presentation titled “Development of Novel Objective Outcomes in Thyroid’s Eye Disease.” Their research promises to significantly enhance the understanding and treatment of Thyroid Eye Disease (TED), a complex and debilitating condition. In particular, it will be the best and most objective way to measure eyelid and ocular parameters in any ophthalmic and neurological condition with Bulbitech's technology. Our neural network approach enables automated segmentation and mapping of key periocular aspects to assess eyelid function. Our model can automate the evaluation of eyelids to show objective, reproducible measurements of essential lid and ocular function, utilised in clinical practice. Based on valuable clinical input Bulbitech has also developed novel dynamic measures of eyelid function (e.g., velocity and lid acceleration), which provide further insights into an underlying pathological state and patient presentation. Read more by clicking the following link: https://lnkd.in/ehUyM9H4 #thyroideyedisease #medtech #AI #eyelid #oculoplastics

AI is transforming cardiology by streamlining workflows, improving image interpretation, and potentially reducing burnout among cardiologists. This translates to enhanced diagnostics and further improved patient treatment accuracy. However, the adoption and practical use of AI tools also have other implications, and the need for a deeper understanding and evaluation of AI’s clinical value is essential before any decisions are made. In this new white paper, we explore the exciting possibilities and challenges of AI in cardiology. Read now: https://lnkd.in/g_spttgT   #cardiology #AI #enterpriseimaging #ACC24

I'm really pleased to see our "Clinical Applications of Artificial Intelligence in Ophthalmology" topical collection is now at 17 articles, with more in the pipeline this year! The full collection is available here: https://lnkd.in/e6cwZTdY It's been a pleasure working alongside Editorial Board member and collection editor Andrzej Grzybowski curating these articles. I thought I would take the opportunity to summarise how the series has expanded over the past year or so since its creation! The collection launched with a number of commissioned review articles providing an overview of the application and potential of AI in various disease areas e.g. Diabetic retinopathy Anterior segment AMD (T. Y. Alvin Liu, M.D.) Last year, as with all areas of healthcare and beyond, LLMs surged in popularity and use, and we published a handful of studies looking into Chat GPT as a tool for diagnosing patients e.g. Glaucoma (Siamak Yousefi) Rare diseases (Carol Cheung) ... and answering patient questions on optic disc drusen (Yousif Subhi) We've also included a number of studies utilising AI and clinical images for a more automated diagnosis. This is something which has been around in ophthalmology for quite some time (Google/Deepmind), however, increasingly clinicians now have greater access to this technology with more specific use cases e.g. Diagnosing keratoconus Diffuse chorioretinal atrophy in pathologic myopia (Zi-Bing Jin) Investigating the rate and predictors of misclassification by AI in DME (Maria Vittoria Cicinelli) Last but not least, we also retrospectively added in our EiC Tariq Aslam's article from 2021 on skills humans need to learn in the age of AI - which I think has become even more relevant as time progresses. I could go on, there's plenty more in there - if you're interested please check out the full collection linked above. And do let me know if you have an idea for an article, or something to submit yourself, we're always open to discussing this further. #ophthalmology #artificialintelligence #machinelearning #healthcare #chatgpt