عنوان انگلیسی مقاله:
Deep learning only by normal brain PET identify unheralded brain anomalies
ترجمه فارسی عنوان مقاله:
یادگیری عمیق فقط با PET مغز نرمال ناهنجاریهای مغزی هدایت نشده را شناسایی می کند
Sciencedirect - Elsevier - EBioMedicine 43 (2019) 447–453
Hongyoon Choi a,b,1, Seunggyun Ha b,1, Hyejin Kang b, Hyekyoung Lee b, Dong Soo Lee a,b,c,⁎, for the Alzheimers Disease Neuroimaging Initiative
Background: Recent deep learning models have shown remarkable accuracy for the diagnostic classification.
However, they have limitations in clinical application due to the gap between the training cohorts and realworld
data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner
to identify an abnormality in various disorders as imaging data of the clinical routine.
Methods: Using variational autoencoder, a type of unsupervised learning, Abnormality Scorewas defined as how
far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimers disease
(AD) andmild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality
and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic
(ROC) curve.We investigated whether deep learning has additional benefits with experts visual interpretation
to identify abnormal patterns.
Findings: The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores frombaseline
to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve
for discriminating patients with various disorders from controls was 0.74. Experts visual interpretation was
helped by the deep learning model to identify abnormal patterns in 60% of cases initially not identified without
Interpretation:We suggest that deep learning model trained only by normal data was applicable for identifying
wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting
real-world clinical data.
Keywords: PET | Deep learning | Variational autoencoder | Alzheimer | Anomaly detection