Convolutional neural networks to identify malformations of cortical development: A feasibility study

Fernández, Iván Sánchez; Yang, Edward; Amengual‐Gual, Marta; Aguilar, Cristina Barcia; Prieto, Paola Calvachi; Peters, Jurriaan M.

doi:10.1016/j.seizure.2021.05.023

Cited by 6 publications

(9 citation statements)

References 28 publications

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“…model out of the data. 71 Even with this limited dataset, the model achieved an AUC above 0.85 in three different tasks in the test set. 71 Therefore, deep learning approaches can be useful, even in rare neurological diseases.…”

Section: Rare Neurological Diseasesmentioning

confidence: 89%

“…A similar study trained general-purpose convolutional neural networks (InceptionV3, ResNet50, and InceptionResNetV2) to detect malformations of cortical development in brain MRIs. 71 Because the dataset consisted of only 45 subjects with normal MRIs, 52 subjects with diffuse cortical malformation, and 32 subjects with periventricular nodular heterotopia, transfer learning, data augmentation, dropout layers, and other regularizing techniques were needed to extract the most generalizable In practice, this is equivalent to creating an ensemble of different neural networks (the original neural network, but with different nodes present in each iteration) that learn slightly different features with each iteration. Dropout layers make the learned features robust and generalizable, a particularly important characteristic in small datasets.…”

Section: Rare Neurological Diseasesmentioning

confidence: 99%

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Machine learning and deep learning in medicine and neuroimaging

Fernández

Peters

2023

Annals of the Child Neurology Society

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Artificial intelligence is the science and engineering of machines that can mimic human intelligence. Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. We present a narrative review of the relevant literature with a particular focus on deep learning for image classification and image segmentation in neuroimaging. For the first time in history, computers can automatically perform some clinically relevant tasks at the level, or even above the level, of the relevant medical specialists. A turning point in machine learning occurred in the 2010s as a result of (1) the multiple technical improvements that machine learning has been accumulating over several decades, (2) the exponential increase in computing power, and (3) the wide availability of very large databases with millions of observations and thousands of variables. Machine learning is starting to be successfully applied to several areas of medicine, including predictive analytics, decision support, natural language processing of free‐text notes, and automatic interpretation of electrophysiological recordings. Among all the applications of machine learning in medicine, deep learning for computer vision is the one that has enjoyed the greatest success. The emphasis of this review is the application of convolutional neural networks for image classification and for image segmentation in neuroimaging. Machine learning and deep learning are increasingly integrated into the clinical workflow and applied in neuroimaging interpretation. Natural language processing is likely to gain increasing importance in medicine in the near future. Complex decision‐making that mimics human thinking with reinforcement learning is still far away on the horizon.

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Section: Rare Neurological Diseasesmentioning

confidence: 89%

Section: Rare Neurological Diseasesmentioning

confidence: 99%

Machine learning and deep learning in medicine and neuroimaging

Fernández

Peters

2023

Annals of the Child Neurology Society

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“…Classification analyses comparing PNH patients and HCs could improve diagnostic accuracy, which is of clinical significance. In a recent study, 7 the authors developed and tested a deep learning model to automatically detect MCD and further distinguish between diffuse cortical malformation, PNH, and normal MRI at a clinically useful performance level. Neuroimaging is promising to impact clinical practice and public health, with the potential to transform the role of neuroimaging in clinical applications.…”

Section: Discussionmentioning

confidence: 99%

“…Especially in resource‐limited regions, there are still some problems with PNH diagnostic report as the neuroimaging techniques and personal experience of radiologists are key points to diagnose PNH. Machine learning has become increasingly popular over the past decade and has served as a supplementary diagnostic tool for diseases such as glioma 5 and malignant lung nodule, 6 as well as PNH and MCDs 7 . Furthermore, multivariate pattern analysis (MVPA) has been used to develop brain signatures for clinical diagnoses that are more effective than traditional linear models 8 .…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has become increasingly popular over the past decade and has served as a supplementary diagnostic tool for diseases such as glioma 5 and malignant lung nodule, 6 as well as PNH and MCDs. 7 Furthermore, multivariate pattern analysis (MVPA) has been used to develop brain signatures for clinical diagnoses that are more effective than traditional linear models. 8 A machine learning approach to PNH diagnosis could potentially accelerate and improve conventional neuroradiological interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Localized activity alternations in periventricular nodular heterotopia‐related epilepsy

Liu

Zhang

et al. 2023

CNS Neurosci Ther

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Objective Periventricular nodular heterotopia (PNH) is a common type of heterotopia usually characterized by epilepsy. Previous studies have identified alterations in structural and functional connectivity related to this disorder, but its local functional neural basis has received less attention. The purpose of this study was to combine univariate analysis and a Gaussian process classifier (GPC) to assess local activity and further explore neuropathological mechanisms in PNH‐related epilepsy. Methods We used a 3.0‐T scanner to acquire resting‐state data and measure local regional homogeneity (ReHo) alterations in 38 patients with PNH‐related epilepsy and 38 healthy controls (HCs). We first assessed ReHo alterations by comparing the PNH group to the HC group using traditional univariate analysis. Next, we applied a GPC to explore whether ReHo could be used to differentiate PNH patients from healthy patients at an individual level. Results Compared to HCs, PNH‐related epilepsy patients exhibited lower ReHo in the left insula extending to the putamen as well as in the subgenual anterior cingulate cortex (sgACC) extending to the orbitofrontal cortex (OFC) [p < 0.05, family‐wise error corrected]. Both of these regions were also correlated with epilepsy duration. Furthermore, the ReHo GPC classification yielded a 76.32% accuracy (sensitivity = 71.05% and specificity = 81.58%) with p < 0.001 after permutation testing. Interpretation Using the resting‐state approach, we identified localized activity alterations in the left insula extending to the putamen and the sgACC extending to the OFC, providing pathophysiological evidence of PNH. These local connectivity patterns may provide a means to differentiate PNH patients from HCs.

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