A rapid and accurate diagnosis of heart failure(HF) is of utmost importance to decrease the mortality and to retrench the medical expenditure. Our project aims to identify potential HF patients by detecting cardiomegaly and pleural effusion in their chest X-rays by the help of Artificial Intelligence(AI) scanning
We scanned the anonymous chest X-rays of the patients who were at least 45 years old and have applied to any department of the hospital (except cardiology, cardiovascular surgery and emergency department) by the help of AI. AI detected patients who have both cardiomegaly and pleural effusion in their X-Rays and they have been invited to our cardiology clinic for further, definitive HF diagnostic tests.
5623 subjects were scanned and 119 of them(2.1%) had cardiomegaly and pleural effusion together. We reached 57 of 119 patients. We diagnosed HF in 49 of 57 patients (86%) according to the 2021 ESC HF guidelines. The mean values for left ventricular EF was 42±13 %, NT-proBNP levels were median 4218 pg/ml (Q1: 1947pg/ml-Q3:10674pg/ml) and the mean age of HF patients was 70±10 years.
ART-IN-HF project is the first initiative that had used chest X-ray scanning for the early diagnosis of HF by detecting both cardiomegaly and pleural effusion in chest X-rays facilitated by AI. Most of the patients who had both cardiomegaly and pleural effusion were diagnosed with HF. AI might be useful for detecting HF using chest X-ray scanning in undiagnosed HF patients.