Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.

Objectives: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.

Methods: A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion.

Application of postmortem computed tomography angiography to settle a medical dispute after aortic dissection surgery: a forensic case report.

Background: In the present case, we applied postmortem computed tomography angiography (PMCTA) in a medical dispute involving sudden death after cardiovascular surgery.

Case Presentation: A 39-year-old man underwent aortic arch replacement combined with stented elephant trunk implantation surgery under extracorporeal circulation. All vital signs were stable and he was arranged for discharge seven days after surgery.

Comparison of CT Values between Thrombus and Postmortem Clot Based on Cadaveric Pulmonary Angiography.

Objectives: To explore the difference in CT values between pulmonary thromboembolism and postmortem clot in postmortem CT pulmonary angiography (CTPA) to further improve the application value of virtual autopsy.

Methods: Postmortem CTPA data with the definite cause of death from 2016 to 2019 were collected and divided into pulmonary thromboembolism group (=4), postmortem clot group (=5), and control group (=5). CT values of pulmonary trunk and left and right pulmonary artery contents in each group were measured and analyzed statistically.

Automatic Identification of Brain Injury Mechanism Based on Deep Learning.

Objectives: To apply the convolutional neural network (CNN) Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology in forensic brain injury mechanism inference.

Methods: CT images from 190 cases with acceleration and deceleration brain injury were selected as the experimental group, and CT images from 130 normal brain cases were used as the control group. The above-mentioned 320 imaging data were divided into training validation dataset and testing dataset according to random sampling method.

Logistic Regression Analysis of the Mechanism of Blunt Brain Injury Inference Based on CT Images.

Objectives: To study the correlation between CT imaging features of acceleration and deceleration brain injury and injury degree.

Methods: A total of 299 cases with acceleration and deceleration brain injury were collected and divided into acceleration brain injury group and deceleration brain injury group according to the injury mechanism. Subarachnoid hemorrhage (SAH) and Glasgow coma scale (GCS), combined with skull fracture, epidural hematoma (EDH), subdural hematoma (SDH) and brain contusion on the same and opposite sides of the stress point were selected as the screening indexes.

Research on the Identification Model of Medical Damage.

Objectives: To understand the perceptions of doctors, patients and forensic examiners on the current situation of medical disputes and medical damage identification in China, and to explore the medical damage identification model that is more conducive for the resolution of medical disputes.

Methods: A questionnaire was designed, and in-service clinicians, forensic examiners and inpatients in Sichuan Province and Chongqing City were randomly selected from April to November 2019. SPSS 22.

Diagnosis of Vascular Rupture Due to Arteriovenous Malformation Using Postmortem Angiography and 3-Dimensonal Printing: A Case Report.

Postmortem computed tomography (PMCT), PMCT angiography, and 3-dimensonal (3D) printing technology are increasingly applied to forensic practice. Although their effectiveness is undeniably confirmed, their potential role in practice still needs to be further explored. Here, we report a typical case in which such 4 technologies were applied to a woman found dead with stomach content beneath the head on the pillow in her residence.

Pulmonary PMCT angiography by right ventricle cardiac puncture: a novel, promising approach for investigating pulmonary thromboembolism.

Forensic scholars are paying more attention to postmortem computed tomography (PMCT) and PMCT angiography (PMCTA), which are gradually becoming effective and practical methods in forensic practice. However, few studies have focused on the application of PMCTA to cardiac ventricular puncture-especially of the right ventricle. In this article, we introduce a pulmonary PMCTA approach by right ventricle cardiac puncture and its potential value in fatalities from pulmonary thromboembolism (PTE).

Rupture of the Ascending Aorta Surgical Area Detected Using Isolated Cardiopulmonary Organ Computed Tomography Angiography: An Autopsy Case Report After Cardiac Surgery.

We report cause of death after cardiac surgery using isolated cardiopulmonary organ computed tomography angiography (CTA) and a conventional autopsy. A 56-year-old man underwent aortic valve replacement and coronary artery bypass graft surgery under extracorporeal circulation. Massive bleeding occurred suddenly, and the patient died 25 days later.