{"id":1430,"date":"2023-06-06T21:44:29","date_gmt":"2023-06-06T21:44:29","guid":{"rendered":"https:\/\/dailyai.com\/?p=1430"},"modified":"2023-06-06T22:28:18","modified_gmt":"2023-06-06T22:28:18","slug":"researchers-create-high-performance-ai-model-to-detect-heart-disease-from-ecgs","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/06\/researchers-create-high-performance-ai-model-to-detect-heart-disease-from-ecgs\/","title":{"rendered":"Forskere lager h\u00f8ytytende AI-modell for \u00e5 oppdage hjertesykdom fra EKG-er"},"content":{"rendered":"

Researchers at Mount Sinai have used machine learning to develop a novel electrocardiogram (ECG) analysis model called HeartBEiT. <\/b><\/p>\n

HeartBEiT was pre-trained on a colossal dataset of 8.5 million ECGs from 2.1 million patients and outperformed classic convolutional neural networks (CNNs).<\/p>\n

ECGs are ubiquitous heart diagnostic tests, and some 100 million are performed each year in the US alone.<\/span><\/p>\n

An ECG measures electrical activity from the heart and is highly informative of a huge range of rhythm disorders, abnormal heart activity associated with heart attacks, and other forms of heart disease. The issue with ECGs is many of the subtler indications are tough to interpret, especially for rare heart conditions.\u00a0<\/span><\/p>\n

Prior to this study, machine learning (ML) had already been applied to ECG images to automatically flag and classify abnormal results. This increases diagnostic speed and accuracy and reduces the chance of clinical oversight.\u00a0<\/span><\/p>\n

Mount Sinai, a New York hospital and research network, trained a vision-based transformer model on 8.5 million ECG scans.<\/span><\/p>\n

Notably, the model differs from convolutional neural networks (CNNs), the most common neural network for image classification tasks. <\/span>The <\/span>study<\/span><\/a>, published in Nature, states that the model outperformed other models for identifying various heart conditions.\u00a0<\/span><\/p>\n

How HeartBEiT works<\/span><\/h2>\n

CNNs for image classification involve supervised machine learning, meaning they require large quantities of labeled data, which can be labor-intensive to collect and annotate. Conversely, HeartBEiT uses unsupervised techniques to learn from a vast dataset, providing a foundation for downstream tuning to more specific tasks.\u00a0<\/span><\/p>\n

The research team used the DALL-E model, created by OpenAI. DALL-E learns the relationships between tokens. In this case, parts of the ECG images \u2013 the tokens \u2013 are broadly analogous to words in a sentence.\u00a0<\/span><\/p>\n

The model learns their relationship to each other, which allows it to single out abnormal tokens that indicate cardiac problems.\u00a0<\/span><\/p>\n

HeartBEiT\u2019s predictions were compared against standard CNN architectures for several heart conditions, outperforming them over smaller sample sizes.\u00a0<\/span><\/p>\n

\"\"
HeartBEiT (D) highlights specific abnormal regions more accurately than other models. Source: Nature<\/a>.<\/figcaption><\/figure>\n

One of the authors, Akhil Vaid, Instructor of Data-Driven and Digital Medicine (D3M) at the Icahn School of Medicine at Mount Sinai, said<\/a>: \u201cOur model consistently outperformed convolutional neural networks [CNNs], which are commonly used machine learning algorithms for computer vision tasks.\u201d<\/span><\/p>\n

Additionally, HeartBEiT was able to highlight the specific region of the ECG associated with the abnormality.\u00a0<\/span><\/p>\n

Another author, Girish Nadkarni, MD, MPH, Director of The Charles Bronfman Institute of Personalized Medicine, said, \u201cNeural networks are considered black boxes, but our model was much more specific in highlighting the region of the ECG responsible for a diagnosis, such as a heart attack, which helps clinicians to better understand the underlying pathology.”<\/span><\/p>\n

“By comparison, the CNN explanations were vague even when they correctly identified a diagnosis.\u201d<\/span><\/p>\n

AI\u2019s role in medical research and development is well-established, and this is another example of the innovative repurposing of machine learning (ML) models for medical applications. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Forskere ved Mount Sinai har brukt maskinl\u00e6ring til \u00e5 utvikle en ny analysemodell for elektrokardiogram (EKG) kalt HeartBEiT. HeartBEiT ble forh\u00e5ndstrenet p\u00e5 et kolossalt datasett med 8,5 millioner EKG-er fra 2,1 millioner pasienter, og utkonkurrerte klassiske konvolusjonale nevrale nettverk (CNN). EKG er en allestedsn\u00e6rv\u00e6rende diagnostisk test av hjertet, og bare i USA utf\u00f8res det rundt 100 millioner EKG-er hvert \u00e5r. Et EKG m\u00e5ler den elektriske aktiviteten i hjertet og gir god informasjon om en lang rekke rytmeforstyrrelser, unormal hjerteaktivitet i forbindelse med hjerteinfarkt og andre former for hjertesykdom. Problemet med EKG er at mange av de mer subtile indikasjonene<\/p>","protected":false},"author":2,"featured_media":1432,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[101],"class_list":["post-1430","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-medtech"],"yoast_head":"\nResearchers create high-performance AI model to detect heart disease from ECGs | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers at Mount Sinai created a unique machine learning (ML) model for diagnosing heart conditions from ECGs.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2023\/06\/researchers-create-high-performance-ai-model-to-detect-heart-disease-from-ecgs\/\" \/>\n<meta property=\"og:locale\" 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