ArticleEBioMedicine2019
Deep learning only by normal brain PET identify unheralded brain anomalies.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Unsupervised anomaly detection with deep generative models: an experimental analysis of model variability and mitigation strategies.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Real-world comparison of brain [EClinicalMedicine · 2026Article
- Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.Frontiers in artificial intelligence · 2026Article
- Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT.Scientific reports · 2025Article
- Benchmarking 3D generative autoencoders for pseudo-healthy reconstruction of brainJournal of medical imaging (Bellingham, Wash.) · 2025Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis.Current neuropharmacology · 2024Review
- Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy.Epilepsia open · 2023Article
- Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.Cognitive neurodynamics · 2023Article
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- Unsupervised abnormality detection in neonatal MRI brain scans using deep learning.Scientific reports · 2023Article
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.Scientific reports · 2022Article
- Emergency triage of brain computed tomography via anomaly detection with a deep generative model.Nature communications · 2022Article
- Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint.Sensors (Basel, Switzerland) · 2022Article
- Deep learning-based detection of parathyroid adenoma byAnnals of nuclear medicine · 2022Article
- A zero-dose synthetic baseline for the personalized analysis of [Frontiers in neuroscience · 2022Article
- A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea.Nuclear medicine and molecular imaging · 2021Review
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
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Authors and funding
6 authors.
Funding
Abstract
backgroundRecent 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 real-world 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.
methodsUsing variational autoencoder, a type of unsupervised learning, Abnormality Score was defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimer's disease (AD) and mild 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.
findingsThe AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores from baseline 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 the model.
interpretationWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.