UC Irvine

Clinical Significance Aortic dissection is associated with high rates of morbidity and mortality 🡪 early diagnosis and prompt intervention greatly improve patient outcomes Mortality rate of 1-2% per hour during first 48 hours Provide real-world validation of FDA 510(k)-approved software application in expediting detection, triage, and ultimately treatment of patients with suspected aortic dissection Viz Aortic Dissection algorithm, in collaboration with Avicenna.AI (La Ciotat, France) Growing concern that algorithmic biases may perpetuate existing health inequities Objective: to assess the real-world performance of deep learning algorithm for detection of aortic dissection on computed tomography angiography (CTA) with a focus on evaluating differences in performance across age, sex, geography, and manufacturer Study Methods 1,303 chest and thoracoabdominal CTA exams from 200+ U.S. hospitals Ground-truth classification for presence or absence of aortic dissection determined through consensus evaluation by three board-certified radiologists Exams analyzed using FDA 510(k)-approved Viz Aortic Dissection algorithm Deep learning model trained on a representative, diverse cohort across age, sex, disease prevalence, race, and clinical settings Algorithmic performance stratified by Age (18-40, 40-60, 60+) Sex (male, female) Geographic region (Continental, Northeast, Pacific, Southeast) Manufacturer (GE Medical Systems, Philips, Siemens, Toshiba) Measured algorithmic fairness across subgroups using equalized odds (EO) differences across true positive rates (TPR) and false positive rates (FPR) Also report overall accuracy, sensitivity, specificity, PPV, and NPV Study Results 1,166 (89.5%) dissection-negative exams, 137 (10.5%) dissection-positive exams Overall accuracy: 97% Sensitivity: 94.2% [95% CI: 88.8% - 97.5%] Specificity: 97.3% [95% CI: 96.2% - 98.1%] PPV of 80.1%, NPV of 99.3% 8 false negatives, largely complex cases 32 false positives, largely result of imaging quality Overall mean EO differences across subgroups was 0.031, with individual EO values noted to be small and consistent for: age [18-40: 0.0584, 40-60: 0.0294, 60+: 0.0368] sex [M: 0.0227, F: 0.0359] geographic region [Continental: 0.0584, NE: 0.0487, Pacific: 0.0227, SE: 0.0314] manufacturer [GE: 0.0111, Philips: 0.013, Siemens: 0.0047, Toshiba: 0.0274] In general, small decreases in TPR or FPR often balanced by small increases in the complimentary metric for most subgroups. Clinical Takeaways Real-world validation of a deep learning AI-based detection algorithm for suspected aortic dissection Sensitivity: 94.2% Specificity: 97.3% Allows for rapid patient triage 🡪 earlier diagnoses 🡪 accelerated care coordination 🡪 timely initiation of life-saving interventions 🡪 better patient outcomes Generalizability across demographics and clinical parameters is critical in preventing algorithmic biases and promoting equitable health outcomes Deep learning tool for aortic dissection detection yields no significant biases across patient demographics and scanner manufacturers from 200+ U.S. hospitals Citations Gawinecka J, Schönrath F, von Eckardstein A. Acute aortic dissection: pathogenesis, risk factors and diagnosis. Swiss Med Wkly. 2017 Aug 25;147:w14489. doi: 10.4414/smw.2017.14489. PMID: 28871571. Gudbjartsson T, Ahlsson A, Geirsson A, Gunn J, Hjortdal V, Jeppsson A, Mennander A, Zindovic I, Olsson C. Acute type A aortic dissection - a review. Scand Cardiovasc J. 2020 Feb;54(1):1-13. doi: 10.1080/14017431.2019.1660401. Epub 2019 Sep 23. PMID: 31542960. Harris KM, Nienaber CA, Peterson MD, et al. Early Mortality in Type A Acute Aortic Dissection: Insights From the International Registry of Acute Aortic Dissection. JAMA Cardiol. 2022;7(10):1009–1015. doi:10.1001/jamacardio.2022.2718