Traditional craniotomy versus current minimally invasive surgery for spontaneous supratentorial intracerebral haemorrhage:A propensity-matched analysis

BACKGROUND Minimally invasive surgery(MIS)and craniotomy(CI)are the current treatments for spontaneous supratentorial cerebral haemorrhage(SSTICH).AIM To compare the efficacy and safety of MIS and CI for the treatment of SSTICH.METHODS Clinical and imaging data of 557 consecutive patients with SSTICH who underwent MIS or CI between January 2017 and December 2022 were retrospectively analysed.The patients were divided into two subgroups:The MIS group and CI group.Propensity score matching was performed to minimise case selection bias.The primary outcome was a dichotomous prognostic(favourable or unfavourable)outcome based on the modified Rankin Scale(mRS)score at 3 months;an mRS score of 0–2 was considered favourable.RESULTS In both conventional statistical and binary logistic regression analyses,the MIS group had a better outcome.The outcome of propensity score matching was unexpected(odds ratio:0.582;95%CI:0.281–1.204;P=0.144),which indicated that,after excluding the interference of each confounder,different surgical modalities were more effective,and there was no significant difference in their prognosis.CONCLUSION Deciding between MIS and CI should be made based on the individual patient,considering the hematoma size,degree of midline shift,cerebral swelling,and preoperative Glasgow Coma Scale score.

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease.In this work,a dataset containing medical,physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning,deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging(MRI)dataset for cerebral haemorrhage.In the first dataset(medical records),two features,namely,diabetes and obesity,were created on the basis of the values of the corresponding features.The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space.Meanwhile,the Recursive Feature Elimination algorithm(RFE)was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features.The features are fed into the various classification algorithms,namely,Support Vector Machine(SVM),K Nearest Neighbours(KNN),Decision Tree,Random Forest,and Multilayer Perceptron.All algorithms achieved superior results.The Random Forest algorithm achieved the best performance amongst the algorithms;it reached an overall accuracy of 99%.This algorithm classified stroke cases with Precision,Recall and F1 score of 98%,100%and 99%,respectively.In the second dataset,the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid technique.The hybrid model AlexNet+SVM performed is better than the AlexNet model;it reached accuracy,sensitivity,specificity and Area Under the Curve(AUC)of 99.9%,100%,99.80%and 99.86%,respectively.