Report on Cardiac Gross Pathologic Measurements of Sudden Cardiac Death in Adults / 法医学杂志

OBJECTIVES@#To analyze the gross pathological data of sudden cardiac death (SCD) with different causes, to provide data support for the identification of sudden cardiac death with unknown causes.@*METHODS@#A total of 167 adult SCD cases in the archive of the Forensic Expertise Institute of Nanjing Medical University from 2010 to 2020 were collected. The gross pathological data of SCD cases were summarized and the characteristics of different causes of death were statistically analyzed.@*RESULTS@#The ratio of male to female SCD cases was 3.4∶1. Coronary heart disease was the leading cause of SCD, and mainly distributed in people over 40 years old. SCD caused by myocarditis was mainly distributed in young people and the mean age of death was (34.00±9.55) years. By analyzing the differences in cardiac pathological parameters of SCD with different causes, it was found that the aortic valve circumference was significantly dilated in the SCD caused by aortic aneurysm or dissection (P<0.05). The heart weight of SCD caused by aortic aneurysm or dissection and combined factors was greater, and both pulmonary and tricuspid valvular rings were dilated in the SCD caused by combined factors in adult males (P<0.05).@*CONCLUSIONS@#Various gross pathological measures of SCD with different causes are different, which has reference value in the cause of death identification of SCD.

Construction and Application of YOLOv3-Based Diatom Identification Model of Scanning Electron Microscope Images / 法医学杂志

OBJECTIVES@#To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model.@*METHODS@#A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate.@*RESULTS@#The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold.@*CONCLUSIONS@#The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.