Objective To discuss the time-dependent changes of facet joint asymmetry in patients with lumbar disc herniation (LDH). Methods A total of 54 patients with LDH were prospectively analyzed. CT was used to judge whether there existed small joint facet asymmetry and to measure the facet joints. The present results were compared with the results that would be investigated two years later. Results The sagittal process of small facet plane accounted the majority in patients with LDH after two years, with no statistical difference compared with the results before two years (P ＞ 0.05). During 2-year period, the facet joint asymmetry disappeared in seven patients, while the facet joint asymmetry occurred in other seven patients, with no statistical difference (P ＞ 0.05). Conclusions Facet plane and the asymmetry will change with time. Observation of the relationship between facet joint asymmetry and LDH should be carried out in a period of time rather than at a time point.

Objective To explore the predictive efficacy of three machine learning algorithms for carotid plaque stability in patients with cerebral infarction.Methods The clinical data of 500 pa-tients with cerebral infarction were retrospectively analyzed.Univariate analysis and multivariate anal-ysis were used to determine the predictive factors entering the model.The prediction model of carotid plaque stability in patients with cerebral infarction was constructed based on nomogram,decision tree and random forest respectively.The enrolled patients were randomly divided into training set and test set according to the ratio of 7∶3.Sensitivity,specificity,accuracy,recall,accuracy and area under the curve(AUC)were used to compare the application efficiency of the model.Results The AUC of the nomogram model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.910(95％CI,0.950 to 0.983),the sensitivity was 0.910,the specificity was 0.917,the accuracy was 0.886,the recall rate was 0.910,and the accuracy rate was 0.914.The AUC of the decision tree model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.932(95％CI,0.903 to 0.961),the sensitivity was 0.903,the specificity was 0.922,the accuracy was 0.891,the recall rate was 0.903,and the accuracy rate was 0.914.The AUC of the random forest model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.984(95％CI,0.970 to 0.998),the sensitivity was 0.972,the specificity was 0.995,the accuracy was 0.993,the recall rate was 0.972,and the ac-curacy was 0.986.Conclusion The model based on the random forest algorithm has a better pre-diction effect and stability in evaluating the stability of carotid plaque in patients with cerebral infarc-tion,and its prediction efficiency is better than that of the Nomogram and decision tree.

Objective To explore the predictive efficacy of three machine learning algorithms for carotid plaque stability in patients with cerebral infarction.Methods The clinical data of 500 pa-tients with cerebral infarction were retrospectively analyzed.Univariate analysis and multivariate anal-ysis were used to determine the predictive factors entering the model.The prediction model of carotid plaque stability in patients with cerebral infarction was constructed based on nomogram,decision tree and random forest respectively.The enrolled patients were randomly divided into training set and test set according to the ratio of 7∶3.Sensitivity,specificity,accuracy,recall,accuracy and area under the curve(AUC)were used to compare the application efficiency of the model.Results The AUC of the nomogram model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.910(95％CI,0.950 to 0.983),the sensitivity was 0.910,the specificity was 0.917,the accuracy was 0.886,the recall rate was 0.910,and the accuracy rate was 0.914.The AUC of the decision tree model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.932(95％CI,0.903 to 0.961),the sensitivity was 0.903,the specificity was 0.922,the accuracy was 0.891,the recall rate was 0.903,and the accuracy rate was 0.914.The AUC of the random forest model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.984(95％CI,0.970 to 0.998),the sensitivity was 0.972,the specificity was 0.995,the accuracy was 0.993,the recall rate was 0.972,and the ac-curacy was 0.986.Conclusion The model based on the random forest algorithm has a better pre-diction effect and stability in evaluating the stability of carotid plaque in patients with cerebral infarc-tion,and its prediction efficiency is better than that of the Nomogram and decision tree.