ArticleFrontiers in neurology2013
Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET.
Article in Frontiers in neurology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Nuclear imaging for localization and surgical outcome prediction in epilepsy: A review of latest discoveries and future perspectives.Frontiers in neurology · 2022Pooled it
- Identification of nonconvulsive status epilepticus in the ictal-interictal continuum using artificial intelligence: a prospective observational cohort study.Encephalitis (Seoul, Korea) · 2026Article
- Comparison of clinical features and cerebral glucose metabolism between patients with major depressive disorder and Parkinson's with depression.BMC psychiatry · 2026Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.BMC medical informatics and decision making · 2024Article
- Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.Brain sciences · 2022Article
- Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy.Epilepsia · 2022Article
- Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps.Magma (New York, N.Y.) · 2022Article
- Common functional connectivity alterations in focal epilepsies identified by machine learning.Epilepsia · 2022Article
- [Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review.Seminars in pediatric neurology · 2021Review
- Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review.Frontiers in neuroscience · 2021Review
- A narrative review of machine learning as promising revolution in clinical practice of scoliosis.Annals of translational medicine · 2021Review
- Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy.Frontiers in neurology · 2021Article
- Machine learning applications in epilepsy.Epilepsia · 2019Review
- Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning.Brain connectivity · 2019Article
- Quantitative Research ofFrontiers in neuroscience · 2019Article
- An objective score to identify psychogenic seizures based on age of onset and history.Epilepsy & behavior : E&B · 2018Article
- Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy.PloS one · 2018Article
- Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.NeuroImage. Clinical · 2018Article
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Authors and funding
12 authors.
Funding
Abstract
Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69-90%] or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement - not replace - expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.
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