ArticleEuropean radiology2021
The role of the deep convolutional neural network as an aid to interpreting brain [
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.Cell reports. Medicine · 2025Article
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Combined with multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease.Frontiers in neurology · 2025Article
- Predicting Parkinson's Disease Using a Deep-Learning Algorithm to Analyze Prodromal Medical and Prescription Data.Journal of clinical neurology (Seoul, Korea) · 2025Article
- Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database.Scientific reports · 2024Article
- Automatic Transcranial Sonography-Based Classification of Parkinson's Disease Using a Novel Dual-Channel CNXV2-DANet.Bioengineering (Basel, Switzerland) · 2024Article
- Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.European journal of nuclear medicine and molecular imaging · 2024Article
- Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.NPJ digital medicine · 2024Article
- Impact of brain segmentation methods on regional metabolism quantification inEJNMMI research · 2023Article
- Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.Physical and engineering sciences in medicine · 2023Article
- Relevance ofEJNMMI research · 2023Article
- Molecular Imaging in Parkinsonian Disorders-What's New and Hot?Brain sciences · 2022Review
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo test the performance of a 3D convolutional neural network (CNN) in analysing brain [
methodsWe analyzed patients who had undergone [
resultsNinety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5
conclusions3D CNNs are able to interpret [ KEY POINTS: • [
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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.