Toward automated interpretable AAST grading for blunt splenic injury

• Published in: Emergency radiology Vol. 30; no. 1; pp. 41 - 50
• Main Authors: Chen, Haomin, Unberath, Mathias, Dreizin, David
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-02-2023; Springer Nature B.V
• Subjects: Adult; Algorithms; Automation; Emergency Medicine; Graph theory; Hemorrhage; Humans; Imaging; Injuries; Machine learning; Medicine; Medicine & Public Health; Original Article; Predictive control; Predictive Value of Tests; Radiology; Retrospective Studies; Segmentation; Spleen - injuries; Tomography, X-Ray Computed - methods; Trauma; United States; Wounds, Nonpenetrating - surgery; United States; Deep learning; Spleen; Abdominal trauma; Explainable AI; Computed tomography; Machine learning; Artificial intelligence; Splenic trauma; Blunt splenic trauma; Interpretable AI
• ISSN: 1438-1435; 1070-3004; 1438-1435
• DOI: 10.1007/s10140-022-02099-1

Background
The American Association for the Surgery of Trauma (AAST) splenic organ injury scale (OIS) is the most frequently used CT-based grading system for blunt splenic trauma. However, reported inter-rater agreement is modest, and an algorithm that objectively automates grading based on transparent and verifiable criteria could serve as a high-trust diagnostic aid.
Purpose
To pilot the development of an automated interpretable multi-stage deep learning-based system to predict AAST grade from admission trauma CT.
Methods
Our pipeline includes 4 parts: (1) automated splenic localization, (2) Faster R-CNN-based detection of pseudoaneurysms (PSA) and active bleeds (AB), (3) nnU-Net segmentation and quantification of splenic parenchymal disruption (SPD), and (4) a directed graph that infers AAST grades from detection and segmentation results. Training and validation is performed on a dataset of adult patients (age ≥ 18) with voxelwise labeling, consensus AAST grading, and hemorrhage-related outcome data (
n
 = 174).
Results
AAST classification agreement (weighted κ) between automated and consensus AAST grades was substantial (0.79). High-grade (IV and V) injuries were predicted with accuracy, positive predictive value, and negative predictive value of 92%, 95%, and 89%. The area under the curve for predicting hemorrhage control intervention was comparable between expert consensus and automated AAST grading (0.83 vs 0.88). The mean combined inference time for the pipeline was 96.9 s.
Conclusions
The results of our method were rapid and verifiable, with high agreement between automated and expert consensus grades. Diagnosis of high-grade lesions and prediction of hemorrhage control intervention produced accurate results in adult patients.