Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

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 (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.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. METHODS: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS: In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.