Robotics & Machine Learning Daily News2024,Issue(Feb.26) :101-102.DOI:10.3389/fneur.2024.1323623

Chinese Academy of Sciences Reports Findings in Temporal Lobe Epilepsy (Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning)

Robotics & Machine Learning Daily News2024,Issue(Feb.26) :101-102.DOI:10.3389/fneur.2024.1323623

Chinese Academy of Sciences Reports Findings in Temporal Lobe Epilepsy (Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning)

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Abstract

New research on Central Nervous System Diseases and Conditions - Temporal Lobe Epilepsy is the subject of a report. According to news reporting out of Suzhou, People’s Republic of China, by NewsRx editors, research stated, “Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms.” Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “There is a pressing need for a quantitative, automated method for detecting TLE. This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation. The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019. The method proposed in this study can significantly assist in the preoperative identification of TLE patients.”

Key words

Suzhou/People’s Republic of China/Asia/Brain Diseases and Conditions/Brain Research/Central Nervous System/Central Nervous System Diseases and Conditions/Cerebral Cortex/Cyborgs/Emerging Technologies/Epilepsy/Health and Medicine/Machine Learning/Prosencephalon/Telencephalon/Temporal Lobe/Temporal Lobe Epilepsy

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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