Updated: 17. Jan 2023 22:02 IST
Illinois [United States]17 January (ANI): According to a study, artificial intelligence (AI) can help provide better care for patients who present to the hospital with severe chest discomfort.
The results of the study were published in Radiology, a journal of the Radiological Society of North America (RSNA).
“To our knowledge, our deep learning AI model is the first to use chest X-rays to identify individuals among patients with acute chest pain who need immediate medical attention,” said the study’s lead author, Marton Kolossvary, MD, Ph.D. D., a radiology researcher at Massachusetts General Hospital (MGH) in Boston.
Acute chest pain syndrome may consist of tightness, burning, or other discomfort in the chest or severe pain that spreads to your back, neck, shoulders, arms, or jaw. It can be accompanied by shortness of breath.
Acute chest pain syndrome accounts for more than 7 million emergency department visits each year in the United States, making it one of the most common complaints.
Less than 8% of these patients are diagnosed with the three main cardiovascular causes of acute chest pain syndrome, which are acute coronary syndrome, pulmonary embolism, or aortic dissection. However, the life-threatening nature of these conditions and low specificity of clinical tests, such as electrocardiograms and blood tests, lead to a large use of cardiovascular and pulmonary diagnostic imaging, often giving negative results. As emergency departments struggle with high patient volume and a shortage of hospital beds, effectively triaging patients at very low risk of these serious conditions is important.
Deep learning is an advanced type of artificial intelligence (AI) that can be trained to search X-ray images to find patterns associated with disease.
For the study, Dr. Kolossvary and colleagues developed an open-source deep learning model to identify patients with acute chest pain syndrome who were at risk for 30-day acute coronary syndrome, pulmonary embolism, aortic dissection, or all-cause mortality, based on chest X-ray .
The study used electronic health records of patients presenting with acute chest pain syndrome who had a chest X-ray and additional cardiovascular or pulmonary imaging and/or stress tests at MGH or Brigham and Women’s Hospital in Boston between January 2005 and December 2015. For the study, 5,750 patients (mean age 59, including 3,329 men) were evaluated.
The deep-learning model was trained on 23,005 MGH patients to predict a 30-day composite endpoint of acute coronary syndrome, pulmonary embolism or aortic dissection and all-cause mortality based on chest X-ray images.
The deep-learning tool significantly improved prediction of these adverse outcomes beyond age, gender and conventional clinical markers, such as d-dimer blood tests. The model maintained its diagnostic accuracy across age, gender, ethnicity, and race. Using a 99% sensitivity threshold, the model was able to delay additional testing in 14% of patients compared to 2% when using a model only incorporating age, sex, and biomarker data.
“By analyzing the initial chest X-ray of these patients using our automated deep learning model, we were able to provide more accurate predictions of patient outcomes compared to a model that uses information on age, sex, troponin or d-dimer,” Dr. Kolossvary said. “Our results show that chest x-rays could be used to help triage chest pain patients in the emergency department.” (ANI)