인공신경회로망을 이용한 뇌 F-18-FDG PET 자동 해석:내,외측 측두엽간질의 감별 (Automatic Interpretation of F-18-FDG Brain PET Using Artificial Neural Network:Discrimination of Medial and Lateral Temporal Lobe Epilepsy) |
Author |
이재성1,2,이동수1,김석기1,박광석1,2,이상건3,정준기1,이명철1, |
Jae Sung Lee, Ph.D.1,2, Dong Soo Lee, M.D., Ph.D.1, Seok-Ki Kim, M.D.1, Kwang Suk Park, Ph.D.1,2,Sang Kun Lee, M.D., Ph.D.3, June-Key Chung, M.D., Ph.D.1, and Myung Chul Lee, M.D., Ph.D.1 |
Affiliation |
서울대학교 의과대학 핵의학교실1, 의공학교실2, 신경과학교실3 Departments of Nuclear Medicine1, Biomedical Engineering2, and Neurology3, Seoul National UniversityCollege of Medicine, Seoul, Korea |
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. (Korean J Nucl Med 38(3):233-240, 2004) |
Keyword |
Artificial Intelligence, Artificial Neural Network, Epilepsy, F-18-FDG PET |
Full text Article |
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