We performed a search on the terms ("Artificial intelligence"[All Fields] OR ("AI"[All Fields] AND "epilepsy"[All Fields] AND "surgery"[All Fields] [All Fields]) OR "AI"[All Fields] AND "seizure disorder"[All Fields] AND "machine learning"[All Fields]) OR "AI"[All Fields]) AND ("surgery"[All Fields] AND "epilepsy"[All Fields] OR "AI"[All Fields] OR "surgery"[All Fields] AND "seizure disorder"[ All Fields] OR "Artificial intelligence"[All Fields]) AND ("seizure disorder"[All Fields] AND "machine learning"[All Fields] OR "Artificial intelligence"[All Fields]) AND ("surgery"[All Fields] AND ("epilepsy"[All Fields]). The search was limited to articles published between 2015 and 2022, excluding those from 2017, using all relevant phrases and Medical Subject Heading terms in four medical literature databases, including PubMed, Google Scholar, Scopus, Wiley, Web of Science, and Microsoft Academia. We consider only English-written articles. Furthermore, this systemic review follows the Preferred Reporting Items for Systematic Review approach.

Studies that satisfied the subsequent criteria were possibly included. A study on coexisting illnesses associated with seizures and artificial intelligence intervention. Studies that met the eligibility criteria were incorporated, and references were examined to find other relevant research. To make sure that no pertinent papers were overlooked, the references of the included articles were manually searched. Excluded materials included unpublished research, conference presentations, abstracts, non-English articles, articles with no participants stated in the study, and publication without peer review. Following an initial search approach, duplicate articles were eliminated. All records collected from potentially eligible studies were subjected to an independent screening process for titles and abstracts. Subsequently, each full-text record was evaluated independently. Eligibility criteria determined whether the articles should be included or excluded.

We extracted data separately in four Excel sheets, which were then cross-checked against each other and the source material. The data collected included study type, AI type or modalities, methods used/EEG data or epilepsy detection, age group, AI group, control group, brief description on the methods of AI, validation methods, outcomes, statistical analysis used, recommendations, and limitations. In the case of unresolved discordance, the senior author would adjudicate.

A narrative synthesis and graphical representation of data were performed to summarize and present the findings from the included studies. This involved synthesizing the data qualitatively and identifying patterns, similarities, and differences across the studies.

We identified 27 studies that used an ML approach to aid in the diagnosis of epilepsy and a variety of ML algorithms were employed. The majority were trained on EEG data, EEG along with magnetic resonance imaging (MRI), and few on MRI and other images. Eighteen studies used algorithms based on CNN, which are well-suited for image and signal processing tasks, the CNNs can learn complex patterns in data by using a series of convolutional layers (used to detect interictal epileptiform discharges in EEG recordings). The highest performance rate in detecting epileptic discharge was achieved AUC, 0.99; and this was achieved by Abou Jaoude et al.12 and Zheng et al.,13 demonstrating the importance of assessing external validity in model evaluation. In those studies, the CNN was trained on epileptiform discharges from 46 subjects, which were detected by one epileptologist and were from one type of epilepsy (mesial temporal lope), and on epileptic magnetoencephalography signals from 20 subjects with only one type of epilepsy. Using MRI images as input to train CNN algorithms for the diagnosis of temporal lobe epilepsy was identified in two studies, which revealed a sensitivity of 91.1% and an AUC of 0.94,14 as well as an accuracy of 70–90%.15 The long-term recurrent convolutional network (LRCN) classifier, which is a spatiotemporal deep learning approach, utilizing two-dimensional images from EEG for multichannel fusion for the early prediction of epileptic seizures. Compared to the traditional CNN classifier, the LRCN classifier achieved an accuracy of 93.40%, while the CNN achieved an accuracy of 88.17% Wei et al.16 Moreover, the LRCN classifier's runtime was shorter; however, it was trained on EEG data from 15 epilepsy patients, and this insisted on fusing experimental image data from different centers.

Fergus et al.17 used EEG data from the CHB-MIT dataset of 22 patients for seizure detection by a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point by finding the k most similar EEG records to a new EEG record KNNC the models achieved a sensitivity of 93% and AUC, 0.98; similar study was conducted by almost same investigators using KNNC on 342 individuals divided into two groups seizure and no seizure, the models achieved a good result by a sensitivity of 88% and AUC, 0.93. The data fed to the model had already been processed and filtered, so it needs to be trained on real-time EEG and MRI data.17 Body and face key points detectors were applied on patients with hyperkinetic seizures to detect the presence or absence of emotional features and dystonia,18 designed a deep learning multi-stream model with appearance and skeletal key points, face and body information, using graph convolutional neural networks (neural networks that can learn from graph data, which is data that is structured as a network of nodes and edge), the model was applied on EEG of 19 patients, it achieved accuracy of 81%, 78% for detection of dystonia and emotion respectively. Detecting key points in the body and face is important not only for diagnosing this type of disease but also for aiding in the diagnosis of many other diseases. However, it needs to be trained on other different features, such as abnormal gait and movement.

Differentiated normal subjects from those with hippocampus epilepsy by using diffusion kurtosis imaging (DKI) with kurtosis tensor19 fed an SVM algorithm with a kurtosis tensor obtained from DKI of 59 pediatrics hippocampus epilepsy and 70 normal subjects, SVM witch work to find a hyperplane in the input space that separates the data points into two classes by classifying participants as either having epilepsy or not having epilepsy based on the kurtosis tensor features extracted from their DKI images, the classier accuracy in differentiating between normal and the effected subject was 95.2% and AUC, 0.96 compared to studies mentioned by Smolyansky et al.20 using SVM fed with clinical and EEG data and almost was achieved similar AUC, 0.96 and accuracy of 90%. Functional seizure also has been studied in the AI era. Asadi-Pooya et al.21 used different types of ML to classify patients with functional seizures with comorbid epilepsy and functional seizures without comorbid epilepsy. SVM, random forests, and DT have been used and achieved an accuracy of 82.5%, 81.3%, and 78.7%; respectively.

This study dives into the application of AI and ML algorithms in the context of epilepsy treatment, specifically focusing on their capacity to identify potential surgical candidates. The natural language processing algorithm was trained on free-text physicians’ notes. It’s encouraging to see that the surgical candidacy scores weren't influenced by patient demographics, suggesting a level of fairness in the algorithm.22 It is also interesting how factors such as travel from outside the local area, continuation of care past 18, and socioeconomic variables played a role in the scores. This illustrates the significance of unbiased surgical candidacy scores, highlighting their potential as a valuable tool for clinicians. In another study by Wissel et al.,22 the use of ML algorithms to identify potential candidates for resective epilepsy surgery seems promising. The predictive capabilities for both pediatric and adult surgical patients, especially with AUC scores of 0.93 and 0.95, are quite impressive. It is very important and promising how the early identification of surgical candidates could significantly impact treatment planning and potentially lead to better outcomes. Emphasizing the lead time provided by the ML algorithms-2.0 years for pediatric patients and 1.0 years for adults-could highlight the potential for timely intervention and improved patient care.

Predicting the response to anti-seizure medications (ASMs) is crucial for optimizing epilepsy treatment. AI has shown promise in this area as well. One study, using an SVM classifier, achieved an accuracy of 87.5% in predicting ASM response in focal epilepsy patients.23 This suggests that AI could guide personalized medication selection, reducing trial-and-error approaches and improving seizure control.

Several studies have explored the use of AI in predicting the outcome of epilepsy surgery. These studies have employed various AI techniques, ML, SVM, and random forest algorithms. The data used for AI training has included clinical characteristics, EEG recordings, MRI images, and structural connectome data. A study employing a neural network achieved a remarkable success rate of 88% in forecasting seizure remission following surgery for MTLE patients, as evidenced by its positive predictive value of 88% and mean negative predictive value of 79%. This significantly surpassed the performance of a traditional classification model relying solely on clinical variables, which yielded an accuracy of less than 50%.24

Another study, using SVMs, demonstrated an accuracy of 95% in predicting surgical outcomes in complicated cases of MTLE Asadi-Pooya et al.21 One more study found that a random forest algorithm could accurately predict seizure freedom after surgery for MTLE patients in 80% of cases (Sinclair et al.25). SVMs have shown promise in predicting treatment outcomes in patients with refractory epilepsy; one study using SVMs achieved a positive predictive value of 90%, a negative predictive value of 70%, and an accuracy of 80% in predicting the surgical treatment outcomes of patients with temporal lobe epilepsy.26 These findings suggest that AI could be a valuable tool in surgical planning and improving patient outcomes.

Despite the promising results, AI in epilepsy management faces challenges, including small sample sizes, retrospective study designs, and the need for further validation in larger, prospective studies. Additionally, integrating AI into clinical practice requires collaboration between clinicians and data scientists to ensure the interpretability and applicability of AI tools.

Future research directions include developing AI-powered tools for real-time seizure prediction and monitoring treatment efficacy. Combining AI with other emerging technologies, such as wearable devices and genomics, holds the potential for further enhancing epilepsy treatment.

Multiple studies conducted on patients with epilepsy using ML algorithms were able to aid in the diagnosis, treatment, and prognosis of epilepsy patients with great accuracy and specificity. Although initial studies show promise for ML in epilepsy, its clinical adoption is hampered by limited sample sizes and a lack of external validation. Large-scale collaborative research and prospective outcome evidence are necessary before ML models can become part of daily clinical workflows and positively impact the lives of epilepsy patients.