Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis
“…The unit pixel volume was calculated according to slice spacing and pixel spacing values obtained from CT scan metadata. Finally, the LPDI was calculated according to definition from Dreizin et al [ 13 ] as follows: where represents the estimated volume of a segmentation region. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…For example, the percentage of liver parenchyma disrupted by laceration or intraparenchymal hematoma is one of the main CT imaging criteria for determining the AAST grade. In 2021, Dreizin et al coined the term ‘liver parenchymal disruption index’ to measure the degree of parenchymal injury, which is abbreviated to LPDI for simplicity [ 13 ]. The LPDI is computed as the ratio of liver trauma volume to liver volume, where liver trauma volume and liver volume are conventionally obtained based on manually labeled CT images.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, there have been limited attempts to use deep learning [ 15 ] for quantitative trauma diagnosis based on CT images. Dreizin et al utilized a multiscale deep learning algorithm [ 16 ] for voxel-wise measurements of liver laceration, and found that the derived LPDI was a significantly independent predictor of major hepatic arterial injury in patients with blunt hepatic injury that underwent CT prior to angiography [ 13 ]. To our knowledge, this is the first study to segment and quantify liver trauma on CT using computer vision methods.…”
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.
“…The unit pixel volume was calculated according to slice spacing and pixel spacing values obtained from CT scan metadata. Finally, the LPDI was calculated according to definition from Dreizin et al [ 13 ] as follows: where represents the estimated volume of a segmentation region. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…For example, the percentage of liver parenchyma disrupted by laceration or intraparenchymal hematoma is one of the main CT imaging criteria for determining the AAST grade. In 2021, Dreizin et al coined the term ‘liver parenchymal disruption index’ to measure the degree of parenchymal injury, which is abbreviated to LPDI for simplicity [ 13 ]. The LPDI is computed as the ratio of liver trauma volume to liver volume, where liver trauma volume and liver volume are conventionally obtained based on manually labeled CT images.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, there have been limited attempts to use deep learning [ 15 ] for quantitative trauma diagnosis based on CT images. Dreizin et al utilized a multiscale deep learning algorithm [ 16 ] for voxel-wise measurements of liver laceration, and found that the derived LPDI was a significantly independent predictor of major hepatic arterial injury in patients with blunt hepatic injury that underwent CT prior to angiography [ 13 ]. To our knowledge, this is the first study to segment and quantify liver trauma on CT using computer vision methods.…”
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.
“…Another interesting study by Drezin et al evaluated AI in blunt hepatic trauma, to increase the diagnostic capacity of CT in detecting vascular damage [ 72 ]. Liver injuries are frequent findings in patients who undergo an emergency CT scan for blunt trauma, and contrast extravasation represents the most direct sign of arterial bleeding, but may be discordant with angiographic data, often underestimating the extent of bleeding [ 73 ].…”
Section: Automatic Detectionmentioning
confidence: 99%
“…In this study, the authors re-trained and validated previously described DL algorithms, to obtain the automated measurement of the liver parenchymal disruption index (auto-LPDI) on 73 patients from two centers, submitted to catheter-directed hepatic angiography post-CT [ 72 ]. The results showed that a decision tree based on auto-LPDI and volumetric contrast extravasation measurements had the highest accuracy (0.84), and was a significant improvement over contrast extravasation assessment alone (0.68), with the demonstration that auto-LPDI was a significant independent predictor of major hepatic arterial injuries in these patients.…”
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients’ lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS–PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients’ clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease’s severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
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