Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification

Abstract

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm's time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8-97.5%] and a specificity of 97.3% [95% CI: 96.2-98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9-99.8%] for type A and 97.5 [95% CI: 96.4-98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Keywords: AI-based solution for radiology; aortic dissection; deep learning; emergency radiology; machine learning diagnostic performance; medical and biomedical image processing; medical imaging automated analysis.

Figure 1

Examples of CINA-CHEST (AD) outputs upon true and false positive AD cases automatically determined by CINA-CHEST (AD). Red boxes are placed by CINA-CHEST (AD) to indicate the localisation of a detected AD. (a) Correct detection of type A AD. (b) Correct detection of type B AD. (c) False-positive identification of a complicated case in the presence of intramural hematoma following aortic repair.

Figure 2

Examples of missed AD cases by CINA-CHEST (AD). (a) Missed subtle type B AD due to a streak artefact. (b) Missed type B AD in the presence of penetrating atherosclerotic ulcer. (c) Missed type B AD in the presence of large intramural hematoma and penetrating atherosclerotic ulcer.