Journal Article

The diagnostic value of chest X‐ray scanning by the help of Artificial Intelligence in Heart Failure (ART‐IN‐HF)

Celik, Ahmet, Demir, Mustafa, Surmeli, Ali Orcun, Camsari, Ahmet, Esen, Kaan

2023

Overview

Background Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X‐ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF. Objectives The study's goal was to demonstrate that the AI interpretation of chest X‐rays can assist the clinician in diagnosing HF. Methods Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X‐rays by AI algorithm. The AI‐generated report was later verified by an independent radiologist. Patients with CTR > 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF. Results Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X‐rays. One hundred and six out of 183 underwent diagnostic tests. Eighty‐two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction. Conclusion HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X‐ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction. Artificial Intelligence (AI) may help in the early and quicker diagnosis of heart failure (HF). Patients with cardiothoracic ratio (CTR) >0.5 and pleural effusion on chest X‐rays were marked as potential HF by AI. Potential HF patients underwent diagnostic tests such as natriuretic peptides and echocardiography. AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X‐rays of 10,100 patients. 77% of AI‐positive patients were diagnosed with HF. Eight percent of AI‐negative patients were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. The majority of newly diagnosed HF patients had non‐reduced ejection fraction.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc
• Subject: Artificial Intelligence / Cardiology / chest X‐ray / Clinical Trial / Deep learning / Diagnostic tests / Dyspnea / Heart failure / Heart Failure - diagnostic imaging / heart failure with preserved ejection fraction / Hospitalization / Humans / Mortality / Pleural effusion / Pleural Effusion - diagnostic imaging / Predictive Value of Tests / Statistical analysis / X-Rays