Pathologic Grading of Meningioma Tissue Via Machine Learning and Noncontact Fluorescence Spectroscopy.

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]