Comparison of CT Values between Thrombus and Postmortem Clot Based on Cadaveric Pulmonary Angiography / 法医学杂志

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 (n=4), postmortem clot group (n=5), and control group (n=5). CT values of pulmonary trunk and left and right pulmonary artery contents in each group were measured and analyzed statistically.@*RESULTS@#The average CT value in the pulmonary thromboembolism group and postmortem clot group were (168.4±53.8) Hu and (282.7±78.0) Hu, respectively, which were lower than those of the control group (1 193.0±82.9) Hu (P<0.05). The average CT value of the postmortem clot group was higher than that of the pulmonary thromboembolism group (P<0.05).@*CONCLUSIONS@#CT value is reliable and feasible as a relatively objective quantitative index to distinguish pulmonary thromboembolism and postmortem clot in postmortem CTPA. At the same time, it can provide a scientific basis to a certain extent for ruling out pulmonary thromboembolism deaths.

Automatic Identification of Brain Injury Mechanism Based on Deep Learning / 法医学杂志

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. The model classification performance was evaluated by the accuracy rate, precision rate, recall rate, F1-value and AUC value.@*RESULTS@#In the training process and validation process, the accuracy rate of the model to classify acceleration injury, deceleration injury and normal brain was 99.00% and 87.21%, which met the requirements. The optimized model was used to test the data of the testing dataset, the result showed that the accuracy rate of the model in the test set was 87.18%, and the precision rate, recall rate, F1-score and AUC of the model to recognize acceleration injury were 84.38%, 90.00%, 87.10% and 0.98, respectively, to recognize deceleration injury were 86.67%, 72.22%, 78.79% and 0.92, respectively, to recognize normal brain were 88.57%, 89.86%, 89.21% and 0.93, respectively.@*CONCLUSIONS@#Inception_v3 model has potential application value in distinguishing acceleration and deceleration injury based on brain CT images, and is expected to become an auxiliary tool to infer the mechanism of head injury.

Logistic Regression Analysis of the Mechanism of Blunt Brain Injury Inference Based on CT Images / 法医学杂志

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. χ2 test was used for primary screening, and binary logistic regression analysis was used for secondary screening. The indexes with the strongest correlation in acceleration and deceleration injury mechanism were selected.@*RESULTS@#χ2 test showed that skull fracture and EDH on the same side of the stress point; EDH, SDH and brain contusion on the opposite of the stress point; SAH, GCS were correlated with acceleration and deceleration injury (P<0.05). According to binary logistic regression analysis, the odds ratio (OR) of EDH on the same side of the stress point was 2.697, the OR of brain contusion on the opposite of the stress point was 0.043 and the OR of GCS was 0.238, suggesting there was statistically significant (P<0.05).@*CONCLUSIONS@#EDH on the same side of the stress point, brain contusion on the opposite of the stress point and GCS can be used as key indicators to distinguish acceleration and deceleration injury mechanism. In addition, skull fracture on the same side of the stress point, EDH and SDH on the opposite of the stress point and SAH were relatively weak indicators in distinguishing acceleration and deceleration injury mechanism.

Research on the Identification Model of Medical Damage / 法医学杂志

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 22.0 software was used to analyze the data of various survey results.@*RESULTS@#Compared with patients (24.92%), doctors (61.72%) believed that the current doctor-patient relationship was more tense than before; both doctors and patients were more inclined to choose voluntary consultation and people's mediation to resolve medical disputes; forensic examiners have the highest level of cognition of medical and health-related laws and regulations, followed by doctors and patients; 66.72% of doctors and 78.41% of patients believed that medical damage identification was necessary, and they were more inclined to entrust forensic identification institutions; different groups all believed that forensic examiners and doctors should participate in the identification together, 80.94% of doctors believed that the appraisal institutions should be responsible for the forensic opinion, not the appraiser.@*CONCLUSIONS@#It is suggested that the Medical Association identification and forensic identification should learn from each other and formulate basic unified rules for the identification of medical damage. It is suggested to standardize the behavior of medical damage forensic identification institutions and appraisers, to improve their own appraisal level, actively invite clinical medical experts for consultation in identification, and promote the standardized, scientization of forensic identification.

Deep Learning-Based Cardiac Imaging Data Measurement and Its Application in Diagnosis of Sudden Cardiac Death / 法医学杂志

In the field of forensic medicine, diagnosis of sudden cardiac death is limited by subjective factors and manual measurement methods, so some parameters may have estimation deviation or measurement deviation. As postmortem CT imaging plays a more and more important role in the appraisal of cause of death and cardiopathology research, the application of deep learning such as artificial intelligence technology to analyze vast amounts of cardiac imaging data has provided a possibility for forensic identification and scientific research workers to conduct precise diagnosis and quantitative analysis of cardiac diseases. This article summarizes the main researches on deep learning in the field of cardiac imaging in recent years, and proposes a feasible development direction for the application of deep learning in the virtual anatomy of sudden cardiac death at present.

Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm / 法医学杂志

Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm-1, 2 854 cm-1, 1 623 cm-1, and 1 535 cm-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.

Research Progress on Postmortem Changes of Computed Tomography Imaging Characteristics on Corpses / 法医学杂志

Postmortem changes on corpses appear immediately after death, and can transform the original structure characteristics of the corpse to different degrees as well as show specific changes on computed tomography （CT） images, sometimes with false positives and false negatives, influencing the identification of injuries or diseases. This paper systematically summarizes the postmortem changes of computed tomography imaging characteristics on corpses, to further expand the application of virtopsy in the practices of forensic pathology identification, and provide reference for the identification of injuries, diseases and changes after normal death.

Metabolomics Changes of Serum in Rats Dying from Untypical Electric Injury / 法医学杂志

Objective To study the differential metabolites of serum in rats dying from untypical electric injury by 1H nuclear magnetic resonance （1 NMR）-based metabolomics methods, in order to provide clues for identification of death from antemortem untypical electric injury and instant postmortem electric injury. Methods Models of rats dying from untypical electric injury, instant postmortem electric injury, mechanical asphyxia, mechanical injury, and high temperature injury were established. The rats in control group were executed without any treatment. The serums of rats from every group were detected by 1H NMR-based metabolomics technology to screen differential metabolites. Results The rats dying from untypical electric injury group was compared with those from mechanical asphyxia group, mechanical injury group, high temperature injury group, and control group, respectively. Four chemical shift points with diagnostic value, and their corresponding metabolites were screened. These chemical shift points contained many small molecules, such as alcohols, phenols, sugars, amino acids, etc. The death from untypical electric injury group was compared with those from instant postmortem electric injury group and control group, and then eight chemical shift points with diagnostic value and their corresponding metabolites were screened. These chemical shift points contained small molecules, such as sugars, amino acids, esters, nucleic acids, etc. Conclusion The 1H NMR-based metabolomics technology can identify differential metabolites of serum in rats dying from untypical electric injury, therefore it may provide a basis for the diagnosis of death from untypical electric injury and the identification of antemortem electric injury and instant postmortem electric injury.

Infrared Spectral Characteristics of Electrical Injuries on Swine Skin Caused by Different Voltages Based on Machine Learning Algorithms / 法医学杂志

Objective To explore infrared spectrum characteristics of different voltages induced electrical injuries on swine skin by using Fourier transform infrared-microspectroscopy (FTIR-MSP) combined with machine learning algorithms, thus to provide a reference to the identification of electrical skin injuries caused by different voltages.Methods Electrical skin injury model was established on swines.The skin was exposed to 110 V, 220 V and 380 V electric shock for 30 s and then samples were took, with normal skin tissues around the injuries as the control.Combined with the results of continuous section HE staining, the FTIR-MSP spectral data of the corresponding skin tissues were acquired.With the combination of machine learning algorithms such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), different spectral bands were selected (full band 4 000-1 000 cm-1and sub-bands 4 000-3 600 cm-1, 3 600-2 800 cm-1, 2 800-1 800 cm-1, and 1 800-1 000 cm-1), and various pretreatment methods were used such as orthogonal signal correction (OSC), standard normal variables (SNV), multivariate scatter correction (MSC), normalization, and smoothing.Thus, the model was optimized, and the classification effects were compared.Results Compared with simple spectrum analysis, PCA seemed to be better at distinguishing electrical shock groups from the control, but was not able to distinguish different voltages induced groups.PLS-DA based on the 3 600-2 800 cm-1band was used to identify the different voltages induced skin injuries.The OSC could further optimize the robustness of the 3 600-2 800 cm-1band model.Conclusion It is feasible to identify electrical skin injuries caused by different voltages by using FTIR-MSP technique along with machine learning algorithms.