Version 1
: Received: 10 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (12:33:57 CEST)
How to cite:
Laletin, V.; Ayobi, A.; Chang, P. D.; Chow, D.; Soun, J.; Junn, J. C.; Scudeler, M.; Quenet, S.; Tassy, M.; Avare, C.; Roca-Sogorb, M.; Chaibi, Y. Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification. Preprints2024, 2024070962. https://doi.org/10.20944/preprints202407.0962.v1
Laletin, V.; Ayobi, A.; Chang, P. D.; Chow, D.; Soun, J.; Junn, J. C.; Scudeler, M.; Quenet, S.; Tassy, M.; Avare, C.; Roca-Sogorb, M.; Chaibi, Y. Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification. Preprints 2024, 2024070962. https://doi.org/10.20944/preprints202407.0962.v1
Laletin, V.; Ayobi, A.; Chang, P. D.; Chow, D.; Soun, J.; Junn, J. C.; Scudeler, M.; Quenet, S.; Tassy, M.; Avare, C.; Roca-Sogorb, M.; Chaibi, Y. Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification. Preprints2024, 2024070962. https://doi.org/10.20944/preprints202407.0962.v1
APA Style
Laletin, V., Ayobi, A., Chang, P. D., Chow, D., Soun, J., Junn, J. C., Scudeler, M., Quenet, S., Tassy, M., Avare, C., Roca-Sogorb, M., & Chaibi, Y. (2024). Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification. Preprints. https://doi.org/10.20944/preprints202407.0962.v1
Chicago/Turabian Style
Laletin, V., Mar Roca-Sogorb and Yasmina Chaibi. 2024 "Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification" Preprints. https://doi.org/10.20944/preprints202407.0962.v1
Abstract
This multi-center 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 6 manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The di-agnostic performance of the device was compared with the ground truth established by the majority agreement of three US board-certified radiologists. Furthermore, the DL-algorithm's time-to-notification was evaluated to demonstrate clinical effectiveness. The study included 1,303 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 AD types 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 seconds. This deep learning-based application demonstrated strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.
Keywords
deep learning; medical and biomedical image processing; aortic dissection; AI-based solution for radiology; machine learning diagnostic performance; medical imaging automated analysis; emergency radiology
Subject
Medicine and Pharmacology, Emergency Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
