Knowlepsy

🔍🧠 Dive into my new article "Decoding Neurological Conditions through AI's Lens: An Artistic Exploration" on LinkedIn. Together, let's explore how AI can give us a unique perspective on various neurological conditions through the power of art. Don't forget to 👍, 💬, and 🔄 if you find it insightful! #AI #Neurology #ArtificialIntelligence #AIart #Neuroscience #Inclusion #Diversity #AIinHealthcare #MedicalArt #VisualLearning #PatientExperience #HealthcareAwareness #FutureOfHealthcare #NeurologicalDisorders #multiplesclerosis #msawareness

Our latest paper on Functional Neurological Disorder (FND) in Saudi Arabia is now published. Explore our findings on the current state of FND education and practice among neurologists in Saudi Arabia. #FND #Neurology #Research #MedicalEducation #SaudiArabia #Healthcare

Artificial Intelligence: Future Perspectives in Mental Health and Neurology Part 2 Applications of AI in Neurology 1.Medical Imaging and Diagnostics To Analyze MRI and CT scans, aiding in the early detection and diagnosis of neurological disorders 2.Predictive Analytics for Patient Outcomes AI algorithms can analyze patient data to predict outcomes 3. Stroke Prediction and Prevention AI algorithms analyze patient data, risk factors and medical history, to predict the likelihood of strokes. 4. Cognitive Rehabilitation and Training AI-based applications for patients with Dementia to improve cognitive functions 5. Remote Patient Monitoring Continuous monitoring of patient with real-time health record data in outside hospital setting 6. Natural Language Processing in Electronic Health Records (EHR) Extracting insights from electronic health records, assist in data analysis and decision-making for neurologists. 7. Brain-Computer Interfaces (BCIs) BCIs can enable direct communication between the brain and external devices, beneficial for patients with limb weakness or Aphasia due to stroke. #neurology #artificialintelligence #mentalhealth #aiapp #doctors #health #neuroplasticity

"Artificial intelligence in epilepsy — applications and pathways to the clinic" The paper highlights the potential of AI in neuroimaging, electroencephalography (EEG), electronic health records, medical devices, and multimodal data integration to aid in epilepsy management. Key Findings and Applications: 1. Seizure Detection and Prediction: AI models have been developed to predict seizures by analyzing long-term EEG data. The use of machine learning (ML) models has proven useful, and recent applications increasingly incorporate deep learning (DL) techniques due to their higher accuracy and performance.    2. Seizure Lateralization and Localization: AI can assist in identifying the hemisphere of seizure origin and locating the seizure-onset zone, a critical step in preparing patients for surgical intervention or neurostimulation therapy. Advances in neuroimaging AI tools have shown promising results in improving the precision of these localizations. 3. Medical Devices and Wearables: AI is being integrated into wearable devices to monitor patients in real-time. These devices collect vast amounts of data, which AI systems can analyze to provide continuous monitoring, detect seizure activity, and predict potential seizures before they occur. 4. Surgical and Neurostimulation Assessment: AI is aiding clinicians in determining candidates for surgical resection of epileptic foci or neurostimulation interventions. Machine learning models analyze multiple data types to guide decision-making in these interventions. Challenges Highlighted: - Despite numerous innovations, few AI models have been successfully integrated into clinical practice. The primary reasons include a lack of large, diverse datasets for training models, difficulties in translating research-grade AI tools to real-world healthcare environments, and ethical concerns regarding AI’s decision-making in critical healthcare situations.    - The paper emphasizes the importance of collaboration between researchers and clinicians, especially in data sharing, to overcome these barriers. Without larger and more diverse datasets, AI models may struggle to generalize across different patient populations. - Ethical and methodological considerations, such as ensuring patient safety, accuracy in diagnosis, and addressing biases in AI models, must be carefully managed. Rigorous clinical trials are necessary to validate the efficacy and safety of AI tools before they can be trusted for routine clinical use. Statistics: - The paper reports that AI has led to a 20-30% improvement in the accuracy of seizure prediction models, which is significant in clinical contexts. - AI-guided seizure detection tools have demonstrated sensitivity rates of up to 90% in controlled environments, though real-world application sees varying results depending on the quality and quantity of input data. Paper link: https://lnkd.in/e35dR_ET Follow Leon Zheng to join the unique journey of AI!

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Did you know as many as 60% of people with epilepsy have experienced unequal treatment or discrimination at work due to their condition? This often leads to employees becoming reluctant to inform their employers about their epilepsy, in fear they will be treated differently or may lose their jobs. Although many employers are making adjustments within their workplaces for those suffering from epilepsy, there is still a lack of awareness and many workers still encounter prejudice and discrimination. Follow the link to learn more about epilepsy: https://lnkd.in/e_ak-UQA #ShareYourStory #Epilepsystrong #EmotionalSupport #MentalHealth #epilepsygroup #epilepsyhero #Stigma #Community #Workplace #Employers

Hear “How Worms 🪱 🧬 Helped Us Understand Epilepsy!” (right?! 😱😆) - in our new podcast 🎧📹 episode with scientist, epilepsy research, and author: David C. Henshall of FutureNeuro 🇮🇪 ! David tells us how the information gathered when researching worms led to the discovery of microRNA, a far improved understanding of the epilepsies, and potential treatments - all explained in his new book too! This is episode 2 of 2. 📹 YouTube https://shorturl.at/Vi2L2 🎧 Podcast https://shorturl.at/jI0kp 📖 Transcript & more on David and his book https://shorturl.at/RKsJC

Every patient with drug-resistant epilepsy deserves to understand the adjunctive VNS Therapy™ option. It could change their life, like the 125,000+ patients treated, including 35,000+ children 4 years and older. The VNS Therapy system is indicated for use as an adjunctive therapy in reducing the frequency of seizures in patients 4 years of age and older with partial onset seizures that are refractory to antiepileptic medications. To learn more about VNS Therapy™ visit: https://bit.ly/3YCyn2q Important safety information: https://lnkd.in/dSQ3BYgK Reference: Data on file. Survey Report. LivaNova USA, Inc. Houston, TX. October 2021.