Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort

Maurice Pradella; Rita Achermann; Jonathan I Sperl; Rainer Kärgel; Saikiran Rapaka; Joshy Cyriac; Shan Yang; Gregor Sommer; Bram Stieltjes; Jens Bremerich; Philipp Brantner; Alexander W Sauter

doi:10.3389/fcvm.2022.972512

Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort

Front Cardiovasc Med. 2022 Aug 22:9:972512. doi: 10.3389/fcvm.2022.972512. eCollection 2022.

Authors

Maurice Pradella^{1

2}, Rita Achermann¹, Jonathan I Sperl³, Rainer Kärgel³, Saikiran Rapaka⁴, Joshy Cyriac¹, Shan Yang¹, Gregor Sommer^{1

5}, Bram Stieltjes¹, Jens Bremerich¹, Philipp Brantner^{1

6}, Alexander W Sauter^{1

7}

Affiliations

¹ Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
² Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
³ Siemens Healthineers, Forchheim, Germany.
⁴ Siemens Healthineers, Princeton, NJ, United States.
⁵ Hirslanden Klinik St. Anna, Luzern, Switzerland.
⁶ Regional Hospitals Rheinfelden and Laufenburg, Rheinfelden, Switzerland.
⁷ Department of Radiology, University Hospital Tuebingen, University of Tuebingen, Tuebingen, Germany.

Abstract

Purpose: Thoracic aortic (TA) dilatation (TAD) is a risk factor for acute aortic syndrome and must therefore be reported in every CT report. However, the complex anatomy of the thoracic aorta impedes TAD detection. We investigated the performance of a deep learning (DL) prototype as a secondary reading tool built to measure TA diameters in a large-scale cohort.

Material and methods: Consecutive contrast-enhanced (CE) and non-CE chest CT exams with "normal" TA diameters according to their radiology reports were included. The DL-prototype (AIRad, Siemens Healthineers, Germany) measured the TA at nine locations according to AHA guidelines. Dilatation was defined as >45 mm at aortic sinus, sinotubular junction (STJ), ascending aorta (AA) and proximal arch and >40 mm from mid arch to abdominal aorta. A cardiovascular radiologist reviewed all cases with TAD according to AIRad. Multivariable logistic regression (MLR) was used to identify factors (demographics and scan parameters) associated with TAD classification by AIRad.

Results: 18,243 CT scans (45.7% female) were successfully analyzed by AIRad. Mean age was 62.3 ± 15.9 years and 12,092 (66.3%) were CE scans. AIRad confirmed normal diameters in 17,239 exams (94.5%) and reported TAD in 1,004/18,243 exams (5.5%). Review confirmed TAD classification in 452/1,004 exams (45.0%, 2.5% total), 552 cases were false-positive but identification was easily possible using visual outputs by AIRad. MLR revealed that the following factors were significantly associated with correct TAD classification by AIRad: TAD reported at AA [odds ratio (OR): 1.12, p < 0.001] and STJ (OR: 1.09, p = 0.002), TAD found at >1 location (OR: 1.42, p = 0.008), in CE exams (OR: 2.1-3.1, p < 0.05), men (OR: 2.4, p = 0.003) and patients presenting with higher BMI (OR: 1.05, p = 0.01). Overall, 17,691/18,243 (97.0%) exams were correctly classified.

Conclusions: AIRad correctly assessed the presence or absence of TAD in 17,691 exams (97%), including 452 cases with previously missed TAD independent from contrast protocol. These findings suggest its usefulness as a secondary reading tool by improving report quality and efficiency.

Keywords: aorta - thoracic; aortic aneurysm (thoracic); artifical intelligence (AI); computed tomography; deep learning; diameter measurement; dilatation; guidelines.