ArticleJournal of digital imaging2020
Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
25 citing papers in PubMed, 39 citations in OpenAlex.
- Operational Ontology for Radiology: A Framework for Profiling and Characterizing CT Imaging Data.Journal of imaging informatics in medicine · 2026Article
- A Multi-Model, Pixel-Native Framework for Automated Computed Tomography Series Labeling and Characterization: Proof-of-Concept Study.JMIR medical informatics · 2026Article
- Machine Learning-Based Privacy Preserving via CT/MRI and Organ Metadata Prediction.Journal of imaging informatics in medicine · 2026Article
- Clinical Metadata-Guided Limited-Angle CT Image Reconstruction.IEEE transactions on medical imaging · 2026Article
- Automated identification of MRI series using a hierarchical modular machine-learning pipeline.European radiology experimental · 2026Article
- Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Real-world brain imaging in a population-based cohort enables accurate markers for dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts.PLOS digital health · 2025Article
- Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.Insights into imaging · 2025Article
- Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.Abdominal radiology (New York) · 2024Article
- Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.European radiology · 2024Article
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.Neuroinformatics · 2024Article
- A holistic approach to implementing artificial intelligence in radiology.Insights into imaging · 2024Article
- Sequence-Type Classification of Brain MRI for Acute Stroke Using a Self-Supervised Machine Learning Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Duke Liver Dataset: A Publicly Available Liver MRI Dataset with Liver Segmentation Masks and Series Labels.Radiology. Artificial intelligence · 2023Article
- Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning.Journal of digital imaging · 2023Article
- Head CT deep learning model is highly accurate for early infarct estimation.Scientific reports · 2023Article
- Deep multi-task learning and random forest for series classification by pulse sequence type and orientation.Neuroradiology · 2023Article
- Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.Journal of digital imaging · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging.
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Registered trials
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.