A retrospective U.S.-based Multi-Reader Multi-Case (MRMC) study evaluated CINA-CHEST (Avicenna.AI) for the detection and prioritization of aortic dissection (AD) using chest CT angiographies (CTAs) from 285 patients (37% with AD).
Three radiologists independently reviewed 95 CTAs per phase (pre-AI and post-AI), totaling 570 assessments (285 per phase). The AI tool aimed to improve scan-to-assessment time (STAT) and interpretation time (IT). Results showed a 68% reduction in STAT for true positive AD cases, from 15.84 minutes (pre-AI) to 5.07 minutes (post-AI). Additionally, the IT decreased by 33%, from 21.22 seconds to 14.17 seconds.
Standalone AI performance demonstrated an AUROC of 0.97, sensitivity of 94.3%, and specificity of 100%. Senior radiologists achieved greater STAT reductions (13.63 min) compared to junior readers (5.08 min), although efficiency improvements were consistent across all experience levels.
The authors conclude that the AI-assisted workflow outperformed traditional First-In, First-Out (FIFO) methods, effectively prioritizing critical cases, reducing delays, and improving diagnostic efficiency in emergency settings.
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Enhancing radiologist efficiency with AI: a multi-reader multi-case study on aortic dissection detection and prioritization
Diagnostics, 2024
Abstract
Background and objectives
Acute aortic dissection (AD) is a life-threatening condition in which early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep learning (DL)-based application for the automated detection and prioritization of AD on chest CT angiographies (CTAs) with a focus on the reduction in the scan-to-assessment time (STAT) and interpretation time (IT).
Materials and methods
This retrospective Multi-Reader Multi-Case (MRMC) study compared AD detection with and without artificial intelligence (AI) assistance. The ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists served as readers. Each reader assessed the same CTAs in two phases: assessment unaided by AI assistance (pre-AI arm) and, after a 1-month washout period, assessment aided by device outputs (post-AI arm). STAT and IT metrics were compared between the two arms.
Results
This study included 285 CTAs (95 per reader, per arm) with a mean patient age of 58.5 years ±14.7 (SD), of which 52% were male and 37% had a prevalence of AD. AI assistance significantly reduced the STAT for detecting 33 true positive AD cases from 15.84 min (95% CI: 13.37-18.31 min) without AI to 5.07 min (95% CI: 4.23-5.91 min) with AI, representing a 68% reduction (p < 0.01). The IT also reduced significantly from 21.22 s (95% CI: 19.87-22.58 s) without AI to 14.17 s (95% CI: 13.39-14.95 s) with AI (p < 0.05).
Conclusions
The integration of a DL-based algorithm for AD detection on chest CTAs significantly reduces both the STAT and IT. By prioritizing urgent cases, the AI-assisted approach outperforms the standard First-In, First-Out (FIFO) workflow.