Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study

The Imaging Database for Epilepsy And Surgery (IDEAS)

OBJECTIVE

Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data.

METHODS

Herein, we release an open-source data set of pre-processed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections and detailed demographic information. We also share scans from 100 healthy controls acquired on the same scanners. The MRI scan data include the preoperative three-dimensional (3D) T1 and, where available, 3D fluid-attenuated inversion recovery (FLAIR), as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age a onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical followup. Crucially, we also include resection masks delineated from post-surgical imaging.

RESULTS

To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of ~50%. Our imaging data replicate findings of group-level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes.

SIGNIFICANCE

We envisage that our data set, shared openly with the community, will catalyze the development and application of computational methods in clinical neurology.

Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy

Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI. Using 1178 T1-weighted images (589 temporal lobe epilepsy, 589 healthy controls) from 12 surgical centres, we trained 3D and 2D CNNs for temporal lobe epilepsy versus healthy control classification, using feature visualization to identify important regions. The 3D CNN was compared to the 2D model and to a randomized model (comparison to chance). Further, we explored the effect of sample size with subsampling, examined model performance based on single-subject clinical characteristics, and tested the impact of image harmonization on model performance. Across 50 datapoints (10 runs with 5-folds each) the 3D CNN median accuracy was 86.4% (35.3% above chance) and the median F1-score was 86.1% (33.3% above chance). The 3D model yielded higher accuracy compared to the 2D model on 84% of datapoints (median 2D accuracy, 83.0%), a significant outperformance for the 3D model (binomial test: P < 0.001). This advantage of the 3D model was only apparent at the highest sample size. Saliency maps exhibited the importance of medial-ventral temporal, cerebellar, and midline subcortical regions across both models for classification. However, the 3D model had higher salience in the most important regions, the ventral-medial temporal and midline subcortical regions. Importantly, the model achieved high accuracy (82% accuracy) even in patients without MRI-identifiable hippocampal sclerosis. Finally, applying ComBat for harmonization did not improve performance. These findings highlight the value of 3D CNNs for identifying subtle structural abnormalities on MRI, especially in patients without clinically identified temporal lobe epilepsy lesions. Our findings also reveal that the advantage of 3D CNNs relies on large sample sizes for model training.

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Background

Epilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization.

Methods

Relevant articles and reviews were searched for 2004-2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio).

Results

A total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper "Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals" was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments.

Conclusions

This study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape

Epilepsy, a neurological disorder characterized by recurrent seizures, has witnessed a remarkable transformation in its classification paradigm, driven by advances in clinical understanding, neuroimaging, and molecular genetics. This narrative review navigates the dynamic landscape of epilepsy classification, offering insights into recent developments, challenges, and the promising horizon. Historically, epilepsy classification relied heavily on clinical observations, categorizing seizures based on their phenomenology and presumed etiology. However, the field has profoundly shifted from a symptom-based approach to a more refined, multidimensional system. One pivotal aspect of this evolution is the integration of neuroimaging techniques, particularly magnetic resonance imaging (MRI) and functional imaging modalities. These tools have unveiled the intricate neural networks implicated in epilepsy, facilitating the identification of distinct brain abnormalities and the categorization of epilepsy subtypes based on structural and functional findings. Furthermore, the role of genetics has become increasingly prominent in epilepsy classification. Genetic discoveries have not only unraveled the molecular underpinnings of various epileptic syndromes but have also provided valuable diagnostic and prognostic insights. This narrative review delves into the expanding realm of genetic testing and its impact on tailoring treatment strategies to individual patients. As the classification landscape evolves, there are accompanying challenges. The narrative review underscores the transformative potential of artificial intelligence and machine learning in epilepsy classification. These technologies hold promise in automating the analysis of complex neuroimaging and genetic data, offering enhanced accuracy and efficiency in epilepsy diagnosis and classification. In conclusion, navigating the shifting landscape of epilepsy classification is a journey marked by progress, complexity, and the prospect of improved patient care. We are charting a course toward more precise diagnoses and tailored treatments by embracing advanced neuroimaging, genetics, and innovative technologies. As the field continues to evolve, collaborative efforts and a holistic understanding of epilepsy's diverse manifestations will be instrumental in harnessing the full potential of this dynamic landscape.