ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Impact of Automated Transfer of Semi-Automated Segmentation and Structured Report Rule Requirements on Cardiac MRI Report Quality, Standardization, and Efficiency.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Contrastive language image pretraining for a cardiac magnetic resonance image embedding with zero-shot capabilities.Nature communications · 2026Article
- Hybrid rule-based and on-premises LLM pipeline for extracting CMR and CPET metrics from free-text reports in repaired tetralogy of Fallot.medRxiv : the preprint server for health sciences · 2026Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical reporting of cardiac magnetic resonance (CMR) imaging exams is commonly performed with a dictation approach which requires great care to capture both consistent and comprehensive data. We sought to transform the reporting process by utilizing a structured report framework for reporting standardization, by incorporating automated transfer of data semi-automated segmentation tools for efficiency, and rule-based reporting requirements to improve quality and standardization. Interfaces between the applications used to schedule and protocol exams and to analyze the acquired images were created to bring the source information directly into the structured reporting environment. The physicians reporting CMR were surveyed to determine satisfaction and improved efficiency with the new process through self-reported reporting time. Quality improvement was assessed by examining the consistency of reported parameters with the inclusion of rule-based requirements. The designed structured reporting process with automated measurements and rule-based requirements resulted in significant improvement in report efficiency and quality.
Indexed as
Identifiers
40417532PMC12099410What 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.