Vironix Health’s Post

Your weekly selection of #articles from the #ICMJournal: 🔹Systemic inflammation and delirium during critical illness 👉 https://rdcu.be/dFBvJ 🔹Noninvasive neuromonitoring in acute brain injured patients 👉 https://rdcu.be/dFBuR 🔹Serial lactate measurements to guide resuscitation: more evidence not to? 👉 https://rdcu.be/dFBvk 🔹OPEN ACCESS ~ Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care 👉 https://rdcu.be/dFBvt 🔹Less inappropriate medication: first steps in medication optimization to improve post-intensive care patient recovery 👉https://rdcu.be/dFBvy 🔹Visualizing ICP “Dose” of neurological critical care patients 👉 https://rdcu.be/dFBuU 🔹Mild hypocapnia and outcomes in mechanically ventilated acute brain-injured patients: another piece in the puzzle 👉 https://rdcu.be/dFBuV 🔹Relationship between the arterial partial pressure of carbon dioxide and outcomes in mechanically ventilated acute brain injured patients 👉 https://rdcu.be/dFBuW 🔹Concern for meta-analysis combining randomized parallel and cross-over trials 👉 https://rdcu.be/dFBuZ 🔹Positive or negative pressure: plus ça change, plus c'est la même chose 👉 https://rdcu.be/dFBvA 🔹OPEN ACCESS ~ Non-occlusive mesenteric ischemia: the wolf in sheep’s clothing 👉 https://rdcu.be/dFBuo Read more articles here! 👉 https://lnkd.in/eeBieiy

A new study demonstrates AI's potential to diagnose rare diseases years earlier than conventional methods. The AI program accurately identified individuals at risk of a rare immune disorder, with 74 out of 100 high-risk cases confirmed to likely have the disorder. Lead researcher Ruth Johnson highlights the detrimental effects of delayed diagnosis, including increased infections and hospitalizations.  The AI, named PheNet, learns disease patterns from verified cases and ranks individuals' risk based on electronic health records. With promising results, the research team has secured funding to further develop and implement PheNet across medical centers, aiming to expedite diagnosis for CVID and potentially other rare diseases, enhancing patient care. Read the full article: https://lnkd.in/e_jUtJ5U #health  #healthai  #healthcare  #ai  #artificialintelligence  #healthnews  #ModerndoctorAI  #AIinHealthcare #DigitalHealth #HealthcareInnovation #MedTech #HealthTech #AIHealthcare #HealthIT #HealthTechNews #AIandHealthcare

👁️ Selena proves AI can drastically improve human ability in detecting eye diseases. But the real win? Faster, cheaper, and more accessible care—especially in underserved areas. Learn how AI is reshaping eye health. Details here: https://lnkd.in/dKpXV3RQ

The FDA is placing a bet on #digitalhealth to transform the lives of #Parkinson's patients by tackling one of the most challenging symptoms they face: freezing of gait (FoG). This phenomenon, where individuals experience temporary immobilization of their feet while walking, not only jeopardizes their balance but also heightens the risk of dangerous falls. With the Digitally-Derived Endpoints for Freezing-of-Gait Detection (DEFoGD) Challenge kicking off today, the FDA is calling on the brightest minds in digital health technology to harness the power of #AI and #machinelearning. 💡 Why is this project so crucial? Consider this: about 38% of Parkinson's patients suffer a fall each year, signaling disease progression and diminishing their quality of life. Recurrent falls not only pose physical risks but also take a toll on mental well-being. This initiative aims to accelerate the development of effective interventions for Parkinson's patients. It's not just a competition; it's a collective effort to pioneer new pathways for disease management and therapy development. #FDA #ParkinsonsCare #DigitalHealth #Innovation

Great article in npj digital medicine, led by our lovely collaborators at the University of Southampton, on myCOPD usage, and the potential for machine learning to predict a worsening of their condition (e.g. exacerbations). https://lnkd.in/d3U3shDB My two key takeaways: 1. We're seeing encouraging signals that we may be able to predict critical events before they occur; this could have a profound impact on care and hospital admissions. 2. Engagement with the app is extremely good, but a significant cohort of users interact with our app only when they need us, e.g. before and after an exacerbation. This is exactly how a digital health solution should work. We aren't another facebook driving daily active users; people don't want to be reminded that they have a chronic condition every day. The lesson here is that people should be respected and encouraged to use digital health solutions only when they need them. In-between, just let people get on with their lives, and stop pestering them with notifications. You will actually get more engagement, and as a result, you can develop cool algorithms which will make your product even more valuable for end users.

Ahead of the European Respiratory Society #ERS2024, where cough will be a key topic in multiple discussions, I wanted to share a recent publication by Chung K.F. et al., published in LUNG, on cough variability and predictability in persistent coughers. The study reveals significant within-day and day-to-day variability for each subject with persistent cough recorded over a 30-day period. The two panels on the left highlight the heterogeneity in ‘within-subject’ cough variability regarding the one-day (24h of cough monitoring) predictability for their cough dynamics. The panel on the right presents results for all 97 persistent coughers, ordered by cough frequency predictability. For those at the top of the plot, one day of cough data accurately predicts daily cough frequency over the entire 30-day period. In contrast, for those at the bottom, one day of cough data does not reliably predict daily cough frequency on other days, highlighting the importance of continuous cough monitoring. This is crucial for capturing actual cough dynamics in clinical trials evaluating drug effects and in real-world evidence studies. Access the link to 'Longitudinal Cough Frequency Monitoring in Persistent Coughers: Daily Variability and Predictability' in the comments 👇 If you're at #ERS2024 - this cough monitoring data captured with Hyfe will be presented on Sunday at PS-4 'Artificial intelligence and machine learning in respiratory care: the features and algorithms for monitoring, diagnosis, prediction and advice' - see you there! #Cough #Biomarker #DigitalHealth #AI #ClinicalTrials

New AI tech from Oxford's Caristo Diagnostics detects hidden heart inflammation, predicting heart attacks early. Piloted in the five NHS hospitals and under review in the US, it identifies high-risk patients 20-30 times more likely to have a heart attack. Visit www.healthorbit.ai to discover how our AI-driven solutions can elevate your healthcare services. Schedule a demo today and join the revolution in healthcare efficiency! #HealthOrbitAI #healthcareAI #healthtech #medicalAI #medtech #GenAI #healthiswealth #medicine #physician #doctor #Global #ArtificialIntelligence #AI #healthcareprofessionals #diseaseprevention #EarlyDetection #HealthcareRevolution #healthiswealth2024 #revenuegrowth #healthyliving #diagnosis #heartinflammation

Excited to be at Intelligent Health 2024 and to learn about progress of AI in health. Given the shortage of healthcare professionals and the cost pressure healthcare systems are facing, AI/ ML holds immense potential to increase efficiency and improve health outcomes! I am looking forward to learning more about opportunities to help HCPs to detect rare, initially asymptomatic renal diseases and system supported carepathways for cardiovascular diseases given the health burden! #AI #healthcaresystems #healthoutcomes #cardiovascular

Spirometry is a key tool for diagnosing and managing respiratory conditions like asthma and COPD. But did you know that only 13.4% of primary care tests fully meet clinical guidelines? Post-pandemic challenges, such as workforce shortages and limited training, have made high-quality #spirometry even harder to deliver. That’s where #AI comes in. ✨   A recent UK case study highlighted how our ArtiQ.Spiro solution transforms spirometry in primary care, providing a quality and diagnostic assessment, while cutting interpretation time by 50% and boosting clinician confidence. Routine AI use could expand access and enable early, accurate diagnosis of asthma, #COPD, and often-overlooked conditions like #ILD.   ➡️ For findings, takeaways, and more details, read the full article: https://lnkd.in/dHNQ6qDD  Health Innovation North East and North Cumbria University of Sunderland