A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China

2024-06-14

AI Summary

In China, acute aortic syndrome (AAS) is frequently underdiagnosed on non-contrast CT due to limited access to timely contrast-enhanced CT angiography (CTA). To address this, we propose iAorta—the first end-to-end, interpretable deep learning system for AAS detection directly from routine non-contrast CT. Our method integrates a 3D ResNet backbone with attention mechanisms for multiscale feature extraction, incorporates uncertainty calibration and class activation map–based visualization, and employs multicenter, real-world data—including diverse scanner vendors and acquisition protocols—for robust training. In multicenter retrospective validation, iAorta achieved an AUC of 0.958; in real-world testing, sensitivity ranged from 91.3% to 94.2%, and specificity from 99.1% to 99.3%. Prospective deployment reduced median time to AAS diagnosis to 102.1 minutes, significantly enhancing emergency alerting and clinical decision support.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast CT protocols, achieving a sensitivity of 0.913-0.942 and a specificity of 0.991-0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway. For the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast.

Problem

• Rapid and accurate AAS diagnosis using non-contrast CT
• Reducing delayed/missed AAS diagnosis in resource-limited settings
• Shortening diagnostic time for acute chest pain patients

Innovation

• Deep learning system for AAS on non-contrast CT
• High accuracy AI warning system iAorta
• Reduces diagnosis time in emergency settings