AI can be used in the triage of patients with chest pain. AI algorithms can analyze various patient data, such as medical history, vital signs, and electrocardiogram results, to assist healthcare professionals in determining the severity of chest pain and the appropriate level of care required. However, AI should not replace the clinical judgement of healthcare professionals, and it is important to use AI as a tool to aid in decision making, rather than relying solely on AI-generated results.
According to a study published in Radiology, a journal of the Radiological Society of North America, artificial intelligence (AI) may help improve care for patients who present to the hospital with acute chest pain (RSNA).
“To the best of our knowledge, our deep learning AI model is the first to use chest X-rays to identify individuals among acute chest pain patients who require immediate medical attention,” Márton Kolossváry, M.D., Ph.D., radiology research fellow at Massachusetts General Hospital (MGH) in Boston, said in a statement.
Acute chest pain syndrome may consist of tightness, burning or other discomfort in the chest or a severe pain that spreads to your back, neck, shoulders, arms, or jaw. It may be accompanied by shortness of breath. Acute chest pain syndrome accounts for over 7 million emergency department visits annually in the United States, making it one of the most common complaints.
Using our automated deep learning model, we were able to provide more accurate predictions regarding patient outcomes than a model that uses age, gender, troponin, or d-dimer information. Our findings suggest that chest X-rays could be used to help triage patients with chest pain in the emergency department.
Dr. Kolossváry
Fewer than 8% of these patients have acute coronary syndrome, pulmonary embolism, or aortic dissection, the three major cardiovascular causes of acute chest pain syndrome. However, the life-threatening nature of these conditions, combined with the low specificity of clinical tests such as electrocardiograms and blood tests, leads to extensive use of cardiovascular and pulmonary diagnostic imaging, which frequently yields negative results. As emergency departments struggle with high patient volumes and a shortage of hospital beds, it is critical to effectively triage patients who are at very low risk of these serious conditions.
Deep learning is a type of advanced artificial intelligence (AI) that can be trained to search X-ray images for disease-related patterns. Dr. Kolossváry and colleagues created an open-source deep learning model based on a chest X-ray 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.
Between January 2005 and December 2015, patients 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 were studied using electronic health records. The study included 5,750 patients (mean age 59, including 3,329 men).
Based on chest X-ray images, 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.
The deep-learning tool significantly improved prediction of these adverse outcomes beyond age, sex and conventional clinical markers, such as d-dimer blood tests. The model maintained its diagnostic accuracy across age, sex, ethnicity and race. Using a 99% sensitivity threshold, the model was able to defer additional testing in 14% of patients as compared to 2% when using a model only incorporating age, sex, and biomarker data.
“Using our automated deep learning model, we were able to provide more accurate predictions regarding patient outcomes than a model that uses age, gender, troponin, or d-dimer information,” Dr. Kolossváry said. “Our findings suggest that chest X-rays could be used to help triage patients with chest pain in the emergency department.”
According to Dr. Kolossváry, in the future, such an automated model could analyze background chest X-rays and help select those who would benefit the most from immediate medical attention, as well as identify patients who could be safely discharged from the emergency department.
