Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05643612 |
| Other study ID # |
SpleenTrNet |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
February 1, 2022 |
| Est. completion date |
November 1, 2022 |
Study information
| Verified date |
November 2022 |
| Source |
Chang Gung Memorial Hospital |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Spleen laceration is a lethal abdominal trauma and usually be diagnosed by medical images
such as computed tomography. Deep learning had been proved its usage in detect abnormalities
in medical images.
In this trial, we used de-identified registry databank to develop a novel deep-learning based
algorithm to detect the spleen trauma and to identify the injury locations.
Description:
Background
Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and
high-resolution abdominal computed tomography (CT) can adequately detect the injury. However,
these lethal injuries sometime have been overlooked in current practice. Deep learning
algorithms have proven their capabilities in detecting abnormal findings in medical images.
The aim of this study is to develop a three-dimensional, unsupervised deep learning algorithm
for detecting splenic injury on abdominal CT using a sequential localization and
classification approach.
Material and Methods
We retrospectively collected data from patients who underwent contrast-enhanced abdominal CT
in the emergency department of Chang Gung Memorial Hospital, Linko, due to trauma and acute
abdomen from Jul 2008 to Dec 2017. All patients were registered in the trauma and acute
abdomen registries. Demographic information, including age, sex, disease diagnosis, trauma
mechanism, Injury Severity Score, New Injury Severity Score , Abbreviated Injury Scale, and
spleen injury grade, was collected. Scans showing splenic injury were identified as positive,
and the remaining scans were defined as negative controls. We identified 300 venous phase
scans with splenic injury and randomly selected 300 additional venous phase scans from the
negative controls. CT scans with abdominal trauma injuries other than splenic injury were not
excluded to reduce the selection bias. All data were split by age, sex, the presence of
splenic injury, and injury severity score using stratified sampling into the developmental
dataset and the test set at a ratio of 8:2. One-eighth of the developmental dataset was
further reserved as the validation set during model construction.
Image preprocessing and labeling
The CT scan images were acquired in the original Digital Imaging and Communications in
Medicine (DICOM) format. The images were then converted to the Neuroimaging Informatics
Technology Initiative format, producing 3D voxel-based images. Our algorithm was then
developed based on the venous axial slices, the most common imaging direction in abdominal
trauma surveys. During the training process, image augmentation by translation, rotation,
scaling, and elastic distortions was applied to increase model generalizability.
A trauma surgeon with 10 years of experience confirmed all the positive and negative scans.
In all scans, the spleen with its surrounding background was covered with a manually drawn 3D
bounding box.
Spleen localization
The localization model was designed based on 3D Faster RCNN with Resnet-101as the backbone
structure and trained on the development dataset. We used cross-entropy, focal loss as the
class loss, and L1 loss, distance intersection over union (DIOU) as box regression loss, and
adopted intersection over union (IOU) and DIOU in non-maximum suppression (NMS) for training
of the object detection algorithm.
Spleen injury identification and visualization
The cropped 3D images were used to develop the splenic injury classification model. We
modified the block architecture to improve the interpretability of the reasoning process of
the learned network. The output of the model was the probability of splenic injury.
Model performance was evaluated using the area under the receiver operating characteristic
curve (AUROC), accuracy, sensitivity, specificity, positive predictive value ,and negative
predictive value.
