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Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry
Shungen Huang1, Zhiyong Zhou2, Xusheng Qian2,3
1Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025, China.
Insights
A new deep learning method accurately quantifies pediatric blunt hepatic trauma using CT scans. This automated approach provides objective metrics, aiding in severity assessment and supplementing current grading systems for better patient care.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatric Surgery
Background:
- Pediatric blunt hepatic trauma assessment relies on subjective grading.
- Contrast-enhanced CT is crucial for diagnosis.
- Need for objective, quantitative assessment methods.
Purpose of the Study:
- Develop an end-to-end deep learning method for automated quantitative assessment of pediatric blunt hepatic trauma.
- Utilize contrast-enhanced CT data for analysis.
- Improve accuracy and objectivity in trauma evaluation.
Main Methods:
- Retrospective study of 170 children with blunt hepatic trauma.
- Deep convolutional neural networks (CNNs) for liver and trauma segmentation.
- Calculation of liver parenchymal disruption index (LPDI) and trauma volume.
Main Results:
- High segmentation performance for liver (Dice: 94.75%) and trauma regions (Dice: 72.91%).
- LPDI and trauma volume significantly correlated with AAST liver injury grade (rho=0.823, 0.831).
- High AUC values (0.942, 0.952) for distinguishing trauma grades.
Conclusions:
- Deep learning method enables accurate automated segmentation of liver and trauma.
- Automated LPDI and trauma volume serve as objective quantitative indexes.
- These metrics supplement the AAST grading for pediatric blunt hepatic trauma.
Background:
To develop an end-to-end deep learning method for automated quantitative assessment of pediatric blunt hepatic trauma based on contrast-enhanced computed tomography (CT).
Methods:
This retrospective study included 170 children with blunt hepatic trauma between May 1, 2015, and August 30, 2021, who had undergone contrast-enhanced CT. Both liver parenchyma and liver trauma regions were manually segmented from CT images. Two deep convolutional neural networks (CNNs) were trained on 118 cases between May 1, 2015, and December 31, 2019, for liver segmentation and liver trauma segmentation. Liver volume and trauma volume were automatically calculated based on the segmentation results, and the liver parenchymal disruption index (LPDI) was computed as the ratio of liver trauma volume to liver volume. The segmentation performance was tested on 52 cases between January 1, 2020, and August 30, 2021. Correlation analysis among the LPDI, trauma volume, and the American Association for the Surgery of Trauma (AAST) liver injury grade was performed using the Spearman rank correlation. The performance of severity assessment of pediatric blunt hepatic trauma based on the LPDI and trauma volume was evaluated using receiver operating characteristic (ROC) analysis.
Results:
The Dice, precision, and recall of the developed deep learning framework were 94.75, 94.11, and 95.46% in segmenting the liver and 72.91, 72.40, and 76.80% in segmenting the trauma regions. The LPDI and trauma volume were significantly correlated with AAST grade (rho = 0.823 and rho = 0.831, respectively; p < 0.001 for both). The area under the ROC curve (AUC) values for the LPDI and trauma volume to distinguish between high-grade and low-grade pediatric blunt hepatic trauma were 0.942 (95% CI, 0.882-1.000) and 0.952 (95% CI, 0.895-1.000), respectively.
Conclusions:
The developed end-to-end deep learning method is able to automatically and accurately segment the liver and trauma regions from contrast-enhanced CT images. The automated LDPI and liver trauma volume can act as objective and quantitative indexes to supplement the current AAST grading of pediatric blunt hepatic trauma.
