Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.
Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.
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DOI：
10.1016/j.nicl.2021.102765
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
ENIGMA-Epilepsy Working Group
中科院分区：
文献类型：
--
作者：
Gleichgerrcht E;Munsell BC;Alhusaini S;Alvim MKM;Bargalló N;Bender B;Bernasconi A;Bernasconi N;Bernhardt B;Blackmon K;Caligiuri ME;Cendes F;Concha L;Desmond PM;Devinsky O;Doherty CP;Domin M;Duncan JS;Focke NK;Gambardella A;Gong B;Guerrini R;Hatton SN;Kälviäinen R;Keller SS;Kochunov P;Kotikalapudi R;Kreilkamp BAK;Labate A;Langner S;Larivière S;Lenge M;Lui E;Martin P;Mascalchi M;Meletti S;O'Brien TJ;Pardoe HR;Pariente JC;Xian Rao J;Richardson MP;Rodríguez-Cruces R;Rüber T;Sinclair B;Soltanian-Zadeh H;Stein DJ;Striano P;Taylor PN;Thomas RH;Elisabetta Vaudano A;Vivash L;von Podewills F;Vos SB;Weber B;Yao Y;Lin Yasuda C;Zhang J;Thompson PM;Sisodiya SM;McDonald CR;Bonilha L;ENIGMA-Epilepsy Working Group
机器学习和人工智能在医疗应用中越来越受欢迎。我们将支持向量机(SV)和深度学习(DL)应用于颞叶癫痫(TLE)的结构模型和扩散模型，结果显示出相似的分类精度。与偏侧化TLE的模型相比，基于扩散的模型诊断TLE的效果更好或相似。海马区硬化症患者的模型比对非病变患者分层的模型更准确。人工智能最近在不同的医学领域越来越受欢迎，以帮助根据病理样本或医学成像结果检测疾病。脑磁共振成像(MRI)是评估颞叶癫痫(TLE)患者的重要手段。机器学习和人工智能在增加TLE大脑异常检测方面的作用仍不确定。我们使用了支持向量机(SV)和深度学习(DL)模型，这些模型基于感兴趣区(ROI)结构(n=336)和弥散(n=863)脑MRI数据，这些数据来自TLE患者，这些患者的放射学特征提示多国(多中心)谜团-癫痫联盟潜在的海马区硬化症。我们的数据显示，与根据扩散数据确定TLE侧的模型相比，识别TLE的模型表现更好或相似(68-75%)(56-73%，除了基于结构的模型)，而结构数据的模式则相反(诊断的模型为67-75%，侧的模型为83%)。在其他方面，结构模型和基于扩散的模型显示出类似的分类精度。我们对海马区硬化症患者的分类模型比对非病变患者分层的模型更准确(68-76%)(53-62%)。总体而言，SV和DL模型的表现相似，但在几个实例中，SV的表现略好于DL。我们讨论了这些模型与ROI水平数据的相对性能，以及对未来机器学习和人工智能在癫痫治疗中的应用的影响。
Machine learning and artificial intelligence have gained popularity for medical applications. We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy (TLE) Structural and diffusion-based models showed similar classification accuracies. Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE. Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients. Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.
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影响因子：
5.6
作者：
Bonilha L;Edwards JC;Kinsman SL;Morgan PS;Fridriksson J;Rorden C;Rumboldt Z;Roberts DR;Eckert MA;Halford JJ
通讯作者：
Halford JJ
影响因子：
5.6
作者：
通讯作者：
--
DOI：
10.3174/ajnr.a1650
发表时间：
2009-10
期刊：
AJNR. American journal of neuroradiology
影响因子：
--
作者：
Ahmadi ME;Hagler DJ Jr;McDonald CR;Tecoma ES;Iragui VJ;Dale AM;Halgren E
通讯作者：
Halgren E
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce
影响因子：
5.6
作者：
Berg, Anne T.;Berkovic, Samuel F.;Scheffer, Ingrid E.
通讯作者：
Scheffer, Ingrid E.