Mount Sinai Researchers Use New Deep Learning

NEW YORK, NY (June 6, 2023) – Mount Sinai researchers have developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that can interpret the electrocardiogram (ECG) as language. This approach can increase the accuracy and validity of ECG-related diagnoses, especially for cardiology where training data are limited.

In a study published online June 6, npj digital medicine , the team reported that a new deep learning model known as HeartBEiT forms the basis on which specialized diagnostic models can be created. The research team noted that the model created using HeartBEiT outperformed his established ECG analysis method in comparative tests.

“Our model consistently outperformed convolutional neural networks.” [CNNs], is a machine learning algorithm commonly used for computer vision tasks. Such CNNs are often pre-trained using publicly available images of real-world objects,” says lead author Data-Driven and Digital Medicine (D3M) at Icahn School of Medicine, Mount Sinai. Lecturer Akil Vide, M.D., said: “because Heartbeat Because it is ECG specific, it can perform as well or better than these methods using 1/10th of the data. This makes ECG-based diagnosis fairly feasible, especially for rare diseases with small patient populations and limited available data. “

Over 100 million ECGs are performed each year in the United States alone, thanks to its low cost, non-invasiveness, and broad applicability in heart disease. Nonetheless, physicians are unable to consistently identify patterns that describe disease with the naked eye, particularly in situations where there are no established diagnostic criteria or where such patterns are subtle or chaotic for human interpretation. For conditions, the usefulness of the ECG is limited in scope. However, artificial intelligence is currently revolutionizing science, and most of the research so far has centered around his CNN.

Based on his keen interest in so-called generative AI systems such as ChatGPT, Mount Sinai is taking the field in bold new directions. The system is built on Transformers, a deep learning model trained on large text datasets to generate human-like text. Respond to user prompts on almost any topic. The researchers used a related image generation model to create a discrete representation of a small portion of his ECG, allowing the ECG to be analyzed as language.

“These representations can be considered individual words, and the entire ECG can be considered one document,” explains Dr. Vaid. “Heartbeat understands the relationships between these representations and uses this understanding to perform downstream diagnostic tasks more effectively. The three tasks we tested the model on were learning whether a patient was having a heart attack, whether they had a genetic condition called hypertrophic cardiomyopathy, and how efficiently their heart was functioning. was to do In both cases, our model outperformed all other baselines tested. “

pre-trained researchers Heartbeat It includes 8.5 million ECGs from 2.1 million patients collected over 40 years from four hospitals within the Mount Sinai medical system. We then tested its performance against standard CNN architectures for three cardiac diagnostic domains. As a result of research, Heartbeat The performance was significantly improved even with small sample sizes, and the ‘explainability’ was also improved. Senior Author Girish Nadkarni, M.D., MPH, Eileen and Arthur M. Fishberg, Ph.D., Icahn Mount Sinai Professor of Medicine, Director of the Charles Bronfman Institute for Personalized Medicine, Office of Data-Driven and Digital Medicine The system manager of the department) explains in detail. M.D.: “Neural networks are thought of as black boxes, but our model is more specific to the ECG regions involved in diagnosing heart attacks and other conditions, allowing clinicians to gain a deeper understanding of the underlying pathology. By comparison, CNN’s description was vague, even if the diagnosis was pinpointed accurately.”

In fact, the Mount Sinai team has greatly enhanced the ways and opportunities a physician can interact with an ECG through a sophisticated new modeling architecture. “We want to make it clear that artificial intelligence is in no way a replacement for ECG-based expert diagnosis,” explained Dr. Nadkarni. health. “

The title of this paper is “Fundamental Vision Transformers Improve ECG Diagnostic Performance.”

This study was funded by the NIH National Heart, Lung, and Blood Institute (Grant No. R01HL155915) and the NIH National Center for Promotion of Translational Sciences (Grant No. UL1TR004419).

To view the full list of interests competing with the authors, please see: .

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About Icahn School of Medicine at Mount Sinai

The Icahn School of Medicine at Mount Sinai is internationally renowned for its excellence in research, education and clinical care programs. It is the sole academic partner of eight member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to a large and diverse patient population.

Icahn Mount Sinai University ranks 14th in the nation for National Institutes of Health (NIH) funding, ranks in the 99th percentile for research spending per researcher according to the Association of American Medical Colleges, It has a strong, productive and successful faculty. With more than 3,000 full-time scientists, educators, and clinicians working within and across 44 academic departments and 36 interdisciplinary research institutes, the structure fosters tremendous collaboration and synergy. increase. We focus on translational research and therapeutics in areas as diverse as genomics/big data, virology, neuroscience, cardiology, geriatrics, as well as gastrointestinal and liver diseases. is clear.

Icahn Mount Sinai offers highly competitive M.D., Ph.D., and Masters degree programs and currently has approximately 1,300 students. It has the largest graduate medical education program in the country, with more than 2,000 clinical residents and fellows trained throughout the healthcare system. In addition, over 550 postdoctoral fellows have been trained within the healthcare system.

A culture of innovation and discovery pervades all Icahn Mount Sinai programs. The Mount Sinai Technology Transfer Office, one of the largest in the country, works with faculty and trainees to pursue optimal commercialization of intellectual property, transforming Mount Sinai’s discoveries and innovations into healthcare products and services that benefit the public. We strive to ensure that it is reflected.

Icahn Mount Sinai’s commitment to groundbreaking science and clinical care is enhanced by academic partnerships that complement and complement the school’s programs.

Through Mount Sinai Innovation Partners (MSIP), Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai. In addition, MSIP has established research partnerships with industry leaders such as Merck & Co., AstraZeneca and Novo Nordisk.

Mount Sinai Icahn School of Medicine is located on the border of New York City’s Upper East Side and East Harlem, with classroom instruction on a campus facing Central Park. Icahn Mount Sinai’s location offers many opportunities to interact with and care for a diverse community. Learning extends well beyond the boundaries of the physical campus to his eight hospitals in the Mount Sinai Health System, academic affiliates, and around the world.

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* Mount Sinai Health System Member Hospitals: Mount Sinai Hospital. Mount Sinai Beth Israel. Mount Sinai, Brooklyn. Morningside on Mount Sinai. Mount Sinai Queens. Mount Sinai South Nassau. Sinai Mountain West. New York Department of Ophthalmology and Otolaryngology, Mount Sinai.