The utility of Multicentre Epilepsy Lesion Detection (MELD) algorithm in identifying epileptic activity and predicting seizure freedom in MRI lesion-negative paediatric patients.
Aimee GoelStefano SeriShakti AgrawalRatna KumarAnnapurna SudarsanamBryony CarrAndrew LawleyLesley MacphersonAdam J OatesHelen WilliamsA Richard WalshWilliam B LoJoshua PepperPublished in: Epilepsy research (2024)
In our paediatric cohort of MRI-negative patients with drug-resistant focal epilepsy, the MELD algorithm identified abnormal clusters or lesions in half of cases, and identified one radiologically occult focal cortical dysplasia. Machine-learning-based lesion detection is a promising area of research with the potential to improve seizure outcomes in this challenging cohort of radiologically occult FCD cases. However, its application should be approached with caution, especially with regards to its specificity in detecting FCD lesions, and there is still work to be done before it adds to diagnostic utility.
Keyphrases
- drug resistant
- machine learning
- multidrug resistant
- end stage renal disease
- magnetic resonance imaging
- deep learning
- intensive care unit
- emergency department
- contrast enhanced
- temporal lobe epilepsy
- acinetobacter baumannii
- newly diagnosed
- ejection fraction
- peritoneal dialysis
- loop mediated isothermal amplification
- diffusion weighted imaging
- clinical trial
- prognostic factors
- artificial intelligence
- real time pcr
- computed tomography
- study protocol
- risk assessment
- pseudomonas aeruginosa
- insulin resistance
- double blind
- adipose tissue
- glycemic control