A Multi-reader Study Finds Unnecessary CT Exams Can Be Reduced by 30% When Analyzing Chest Radiographs with AI
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Deep Learning Enables Dual Screening for Cancer and Cardiovascular Disease
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Rensselaer algorithm can identify risk of cardiovascular disease using lung cancer scan
Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer has the potential to expedite a diagnosis, accelerate treatment, and improve patient outcomes.
In research published today in Nature Communications, a team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer. This approach paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients t