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This article, published in the Journal of Neurological Surgery, explores the use of machine learning and 3D MRI-based analysis to predict the pathologic grade of skull base meningiomas. The study analyzed preoperative MRIs from 38 patients and used a custom machine learning pipeline to predict the grade of the tumor. The results showed that the machine learning model had an area under the receiver operating characteristic curve (AUROC) of 0.75, indicating its potential for predicting the pathologic grade. However, further research with a larger sample size is needed to validate these findings and improve prognostication for patients with skull base meningiomas. [Extracted from the article]

This article discusses the development of a new technology called "TumorID" that uses machine learning and noncontact fluorescence spectroscopy to analyze meningioma tissue in real-time during surgery. The aim of this technology is to differentiate between Grade 1 and Grade 2 meningiomas, which have different prognostic and management implications. The study found that the multi-layer perceptron and logistic regression models performed the best in predicting the grade of meningiomas, with high accuracy. However, further research is needed to validate these findings on larger sample sizes. The authors hope that this technology will eventually improve the efficiency and accuracy of prognostication for patients with meningiomas. [Extracted from the article]

Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. TBI patients' data were prospectively collected in Kampala, Uganda, from 2016 to 2020. To predict good versus poor outcome at hospital discharge, we created deep neural network, shallow neural network, and elastic-net regularized logistic regression models. Predictors included 13 easily acquirable clinical variables. We assessed model performance with 5-fold cross-validation to calculate areas under both the receiver operating characteristic curve and precision-recall curve (AUPRC), in addition to standardized partial AUPRC to focus on comparisons at clinically relevant operating points. We included 2164 patients for model training, of which 12% had poor outcomes. The deep neural network performed best as measured by the area under the receiver operating characteristic curve (0.941) and standardized partial AUPRC in region maximizing recall (0.291), whereas the shallow neural network was best by the area under the precision-recall curve (0.770). In several other comparisons, the elastic-net regularized logistic regression was noninferior to the neural networks. We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts. [ABSTRACT FROM AUTHOR]