ArticleJournal of digital imaging2018
Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks.
Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review.Journal of medical Internet research · 2020Pooled it
- Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation.Journal of imaging informatics in medicine · 2026Article
- Improving non-expert performance in musculoskeletal MRI protocoling through a large language model.Scientific reports · 2026Article
- Automated Protocol Suggestions for Cranial MRI Examinations Using Locally Fine-tuned BERT Models.Clinical neuroradiology · 2026Article
- Large language models as cost-conscious decision aids in emergency medicine: protocol support for imaging in lower back pain.Emergency radiology · 2026Article
- Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System.Journal of imaging informatics in medicine · 2025Article
- A Machine Learning System to Automate Body Computed Tomography Protocoling.Journal of imaging informatics in medicine · 2025Article
- Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.Journal of imaging informatics in medicine · 2025Article
- Role of Artificial Intelligence in Reducing Error Rates in Radiology: A Scoping Review.Cureus · 2025Review
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- Automated MRI protocoling in neuroradiology in the era of large language models.La Radiologia medica · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence in musculoskeletal applications: a primer for radiologists.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.BJR open · 2025Review
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- Clinical applications of artificial intelligence in radiology.The British journal of radiology · 2023Review
- Automation of Protocoling Advanced MSK Examinations Using Natural Language Processing Techniques.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purposes of this study are to evaluate the feasibility of protocol determination with a convolutional neural networks (CNN) classifier based on short-text classification and to evaluate the agreements by comparing protocols determined by CNN with those determined by musculoskeletal radiologists. Following institutional review board approval, the database of a hospital information system (HIS) was queried for lists of MRI examinations, referring department, patient age, and patient gender. These were exported to a local workstation for analyses: 5258 and 1018 consecutive musculoskeletal MRI examinations were used for the training and test datasets, respectively. The subjects for pre-processing were routine or tumor protocols and the contents were word combinations of the referring department, region, contrast media (or not), gender, and age. The CNN Embedded vector classifier was used with Word2Vec Google news vectors. The test set was tested with each classification model and results were output as routine or tumor protocols. The CNN determinations were evaluated using the receiver operating characteristic (ROC) curves. The accuracies were evaluated by a radiologist-confirmed protocol as the reference protocols. The optimal cut-off values for protocol determination between routine protocols and tumor protocols was 0.5067 with a sensitivity of 92.10%, a specificity of 95.76%, and an area under curve (AUC) of 0.977. The overall accuracy was 94.2% for the ConvNet model. All MRI protocols were correct in the pelvic bone, upper arm, wrist, and lower leg MRIs. Deep-learning-based convolutional neural networks were clinically utilized to determine musculoskeletal MRI protocols. CNN-based text learning and applications could be extended to other radiologic tasks besides image interpretations, improving the work performance of the radiologist.
Indexed as
Identifiers
What OpenQuestion holds
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.