Automatic Interpretation of F-18-FDG Brain PET Using Artificial Neural Network: Discrimination of Medial and Lateral Temporal Lobe Epilepsy.
- Author:
Jae Sung LEE
1
;
Dong Soo LEE
;
Seok Ki KIM
;
Kwang Suk PARK
;
Sang Kun LEE
;
June Key CHUNG
;
Myung Chul LEE
Author Information
1. Department of Nuclear Medicine, Seoul National University, College of Medicine, Seoul, Korea. dsl@plaza.snu.ac.kr
- Publication Type:Original Article
- Keywords:
Artificial Intelligence;
Artificial Neural Network;
Epilepsy;
F-18-FDG PET
- MeSH:
Artificial Intelligence;
Brain*;
Diagnosis;
Diagnosis, Differential;
Discrimination (Psychology)*;
Epilepsy;
Epilepsy, Temporal Lobe*;
Humans;
Neural Networks (Computer);
Nuclear Medicine;
Temporal Lobe*
- From:Korean Journal of Nuclear Medicine
2004;38(3):233-240
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: We developed a computer-aided classifier using artificial neural network (ANN) to discriminate the cerebral metabolic pattern of medial and lateral temporal lobe epilepsy (TLE). Materials and METHODS: We studied brain F-18-FDG PET images of 113 epilepsy patients sugically and pathologically proven as medial TLE (left 41, right 42) or lateral TLE (left 14, right 16). PET images were spatially transformed onto a standard template and normalized to the mean counts of cortical regions. Asymmetry indices for predefined 17 mirrored regions to hemispheric midline and those for medial and lateral temporal lobes were used as input features for ANN. ANN classifier was composed of 3 independent multi-layered perceptrons (1 for left/right lateralization and 2 for medial/lateral discrimination) and trained to interpret metabolic patterns and produce one of 4 diagnoses (L/R medial TLE or L/R lateral TLE). Randomly selected 8 images from each group were used to train the ANN classifier and remaining 81 images were used as test sets. The accuracy of the diagnosis with ANN was estimated by averaging the agreement rates of independent 50 trials and compared to that of nuclear medicine experts. RESULTS: The accuracy in lateralization was 89% by the human experts and 90% by the ANN classifier. Overall accuracy in localization of epileptogenic zones by the ANN classifier was 69%, which was comparable to that by the human experts (72%). CONCLUSION: We conclude that ANN classifier performed as well as human experts and could be potentially useful supporting tool for the differential diagnosis of TLE.
