Shankar Anand, Saikia Manob Jyoti, Dandapat Samarendra, Barma Shovan
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Guwahati, Assam, 781015, India.
Department of Electrical Engineering, University of North Florida, Jacksonville, FL, 32224, USA.
Npj Imaging. 2024 Sep 2;2(1):31. doi: 10.1038/s44303-024-00031-5.
Focal cortical dysplasia type II (FCD-II) is a prominent cortical development malformation associated with drug-resistant epileptic seizures that leads to lifelong cognitive impairment. Efficient MRI, followed by its analysis (e.g., cortical abnormality distinction, precise localization assistance, etc.) plays a crucial role in the diagnosis and supervision (e.g., presurgery planning and postoperative care) of FCD-II. Involving machine learning techniques particularly, deep-learning (DL) approaches, could enable more effective analysis techniques. We performed a comprehensive study by choosing six different well-known DL models, three image planes (axial, coronal, and sagittal) of two MRI modalities (T1w and FLAIR), demographic characteristics (age and sex) and clinical characteristics (brain hemisphere and lobes) to identify a suitable DL model for analysing FCD-II. The outcomes show that the DenseNet201 model is more suitable because of its superior classification accuracy, high-precision, F1-score, and large area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve.
II型局灶性皮质发育不良（FCD-II）是一种与药物难治性癫痫发作相关的显著皮质发育畸形，会导致终身认知障碍。高效的MRI及其分析（例如，皮质异常鉴别、精确的定位辅助等）在FCD-II的诊断和监测（例如，术前规划和术后护理）中起着至关重要的作用。特别是涉及机器学习技术，深度学习（DL）方法可以实现更有效的分析技术。我们通过选择六种不同的著名DL模型、两种MRI模态（T1w和FLAIR）的三个图像平面（轴位、冠状位和矢状位）、人口统计学特征（年龄和性别）以及临床特征（脑半球和脑叶）进行了一项综合研究，以确定一种适合分析FCD-II的DL模型。结果表明，DenseNet201模型更合适，因为它具有卓越的分类准确率、高精度、F1分数，以及受试者工作特征（ROC）曲线和精确召回率（PR）曲线下的大面积。
