Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization
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 p...
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2689 |
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| author | Martina Cotena Angela Ayobi Colin Zuchowski Jacqueline C. Junn Brent D. Weinberg Peter D. Chang Daniel S. Chow Jennifer E. Soun Mar Roca-Sogorb Yasmina Chaibi Sarah Quenet |
| author_facet | Martina Cotena Angela Ayobi Colin Zuchowski Jacqueline C. Junn Brent D. Weinberg Peter D. Chang Daniel S. Chow Jennifer E. Soun Mar Roca-Sogorb Yasmina Chaibi Sarah Quenet |
| author_sort | Martina Cotena |
| collection | DOAJ |
| description | 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 (<i>p</i> < 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 (<i>p</i> < 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. |
| format | Article |
| id | doaj-art-3fc66435a3eb49a3a7604b37b2ed9555 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-3fc66435a3eb49a3a7604b37b2ed95552025-08-20T01:55:28ZengMDPI AGDiagnostics2075-44182024-11-011423268910.3390/diagnostics14232689Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and PrioritizationMartina Cotena0Angela Ayobi1Colin Zuchowski2Jacqueline C. Junn3Brent D. Weinberg4Peter D. Chang5Daniel S. Chow6Jennifer E. Soun7Mar Roca-Sogorb8Yasmina Chaibi9Sarah Quenet10Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, FranceAvicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, FranceDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USADepartment of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USADepartment of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USADepartment of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USADepartment of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USADepartment of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USAAvicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, FranceAvicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, FranceAvicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, FranceBackground 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 (<i>p</i> < 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 (<i>p</i> < 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.https://www.mdpi.com/2075-4418/14/23/2689aortic dissectionautomated detectiondeep learningprioritized worklistemergency radiologymulti-reader multi-case study |
| spellingShingle | Martina Cotena Angela Ayobi Colin Zuchowski Jacqueline C. Junn Brent D. Weinberg Peter D. Chang Daniel S. Chow Jennifer E. Soun Mar Roca-Sogorb Yasmina Chaibi Sarah Quenet Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization Diagnostics aortic dissection automated detection deep learning prioritized worklist emergency radiology multi-reader multi-case study |
| title | Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization |
| title_full | Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization |
| title_fullStr | Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization |
| title_full_unstemmed | Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization |
| title_short | Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization |
| title_sort | enhancing radiologist efficiency with ai a multi reader multi case study on aortic dissection detection and prioritization |
| topic | aortic dissection automated detection deep learning prioritized worklist emergency radiology multi-reader multi-case study |
| url | https://www.mdpi.com/2075-4418/14/23/2689 |
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