ArticleRadiology. Artificial intelligence2022
Automatic Diagnosis Labeling of Cardiovascular MRI by Using Semisupervised Natural Language Processing of Text Reports.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for myocarditis diagnosis using cardiovascular magnetic resonance: a systematic review, diagnostic test accuracy meta-analysis, and comparison with human physicians.The international journal of cardiovascular imaging · 2025Pooled it
- Artificial intelligence and myocarditis-a systematic review of current applications.Heart failure reviews · 2024Pooled it
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Utilizing large language models and natural language processing to classify ischemia status from cardiac stress tests in a large multicenter healthcare system.BMC research notes · 2025Article
- In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.Healthcare informatics research · 2025Article
- Cardiac magnetic resonance imaging-large language model Meta AI: a finetuned large language model for identifying findings and associated attributes in cardiac magnetic resonance imaging reports.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Uncertainty-aware automatic TNM staging classification for [BMC medical informatics and decision making · 2024Article
- Social Phenotyping for Cardiovascular Risk Stratification in Electronic Health Registries.Current atherosclerosis reports · 2024Review
- A scoping review of large language model based approaches for information extraction from radiology reports.NPJ digital medicine · 2024Article
- Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area.Yearbook of medical informatics · 2023Article
- Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI.Journal of medical artificial intelligence · 2023Article
- BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports.Radiology. Artificial intelligence · 2023Article
- One Clinician Is All You Need-Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development.JMIR medical informatics · 2022Article
- Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT).BMC medical informatics and decision making · 2022Article
- Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.Radiology. Artificial intelligence · 2022Article
- Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors.BMC bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
purposeTo assess whether the semisupervised natural language processing (NLP) of text from clinical radiology reports could provide useful automated diagnosis categorization for ground truth labeling to overcome manual labeling bottlenecks in the machine learning pipeline. MATERIALS AND
methodsIn this retrospective study, 1503 text cardiac MRI reports from 2016 to 2019 were manually annotated for five diagnoses by clinicians: normal, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, myocardial infarction (MI), and myocarditis. A semisupervised method that uses bidirectional encoder representations from transformers (BERT) pretrained on 1.14 million scientific publications was fine-tuned by using the manually extracted labels, with a report dataset split into groups of 801 for training, 302 for validation, and 400 for testing. The model's performance was compared with two traditional NLP models: a rule-based model and a support vector machine (SVM) model. The models' F1 scores and receiver operating characteristic curves were used to analyze performance.
resultsAfter 15 epochs, the F1 scores on the test set of 400 reports were as follows: normal, 84%; DCM, 79%; hypertrophic cardiomyopathy, 86%; MI, 91%; and myocarditis, 86%. The pooled F1 score and area under the receiver operating curve were 86% and 0.96, respectively. On the same test set, the BERT model had a higher performance than the rule-based model (F1 score, 42%) and SVM model (F1 score, 82%). Diagnosis categories classified by using the BERT model performed the labeling of 1000 MR images in 0.2 second.
conclusionThe developed model used labels extracted from radiology reports to provide automated diagnosis categorization of MR images with a high level of performance.
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.