Today’s AI algorithms require tens of thousands of expensive medical images to detect a patient’s disease. What if we could drastically reduce the amount of data needed to train an AI, making diagnoses low-cost and more effective? TED Fellow Pratik Shah is working on a clever system to do just that. Using an unorthodox AI approach, Shah has developed a technology that requires as few as 50 images to develop a working algorithm — and can even use photos taken on doctors’ cell phones to provide a diagnosis. Learn more about how this new way to analyze medical information could lead to earlier detection of life-threatening illnesses and bring AI-assisted diagnosis to more health care settings worldwide. Check out more TED Talks: The TED Talks channel features the best talks and performances from the TED Conference, where the world’s leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design — plus science, business, global issues, the arts and more. Follow TED on Twitter: Like TED on Facebook: Subscribe to our channel:
Parkinson’s disease (PD) is a progressive disorder with a presymptomatic interval; that is, there is a period during which the pathologic process has begun, but motor signs required for the clinical diagnosis are absent. There is considerable interest in discovering markers to diagnose this preclinical stage. Current predictive marker development stems mainly from two principles; first, that pathologic processes occur in lower brainstem regions before substantia nigra involvement and second, that redundancy and compensatory responses cause symptoms to emerge only after advanced degeneration. Decreased olfaction has recently been demonstrated to predict PD in prospective pathologic studies, although the lead time may be relatively short and the positive predictive value and specificity are low. Screening patients for depression and personality changes, autonomic symptoms, subtle motor dysfunction on quantitative testing, sleepiness and insomnia are other potential simple markers. More invasive measures such as detailed autonomic testing, cardiac MIBG-scintigraphy, transcranial ultrasound, and dopaminergic functional imaging may be especially useful in those at high risk or for further defining risk in those identified through primary screening. Despite intriguing leads, direct testing of preclinical markers has been limited, mainly because there is no reliable way to identify preclinical disease. Idiopathic RBD is characterized by loss of normal atonia with REM sleep. Approximately 50% of affected individuals will develop PD or dementia within 10 years. Dataset Link: #machinelearning #artificialintelligence #ai #datascience #python #programming #technology #deeplearning #coding #bigdata #computerscience #tech #data #pythonprogramming #programmer #developer #dataanalytics #software #datascientist #javascript #iot #java #coder #ml #innovation #robotics #linux #analytics [More]
This video is about building a Heart Disease Prediction system using Machine Learning with Python. This is one of the important Machine Learning Projects. Machine Learning Projects Playlist: Hello everyone! I am setting up a donation campaign for my YouTube Channel. If you like my videos and wish to support me financially, you can donate through the following means: From India 👉 UPI ID : siddhardhselvam2317@oksbi Outside of India? 👉 Paypal id: (No donation is small. Every penny counts) Thanks in advance! Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision. I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence. Let’s build a Community of Machine Learning experts! Kindly Subscribe here👉 I am making a “Hands-on Machine Learning Course with Python” in YouTube. I’ll be posting 3 videos per week: Monday Evening; Wednesday Evening; Friday Evening. Dataset file: Colab File Link: Download the Course Curriculum File from here: LinkedIn: Telegram Group: Facebook group: #machinelearningcourse #machinelearningprojects