ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
From Scanner to Science: Reusing Clinically Acquired Medical Images for Research.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. 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.
- Systematic protocol to identify 'clinical controls' for paediatric neuroimaging research from clinically acquired brain MRIs.BMJ open · 2025Article
- Article
- A systematic protocol to identify "clinical controls" for pediatric neuroimaging research from clinically acquired brain MRIs.bioRxiv : the preprint server for biology · 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
11 authors.
Funding
Abstract
Growth in the field of medical imaging research has revealed a need for larger volume and variety in available data. This need could be met using curated clinically acquired data, but the process for getting this data from the scanners to the scientists is complex and lengthy. We present a manifest-driven modular Extract, Transform, and Load (ETL) process named Locutus designed to appropriately handle difficulties present in the process of reusing clinically acquired medical imaging data. The design of Locutus was based on four foundational assumptions about medical data, research data, and communication. All parts of a workflow must communicate with each other and be adaptable to unique data delivery requests. In addition, the workflow must be robust to possible errors and uncertainties in clinically-acquired data, which may require human intervention to resolve. With these assumptions in mind,Locutus presents a five-phase workflow for downloading, deidentifying, and delivering unique requests for imaging data. The phases include initialization, data preparation, extraction of data from the research server to a pre-deidentification data warehouse, transformation into deidentified space, and loading into post-deidentification data warehouse. To date, this workflow has been used to process 32,962 imaging accessions for research use. This number is expected to grow as technical challenges are addressed and the role of humans is expected to shift from frequent intervention to regular monitoring.
Identifiers
40502267PMC12150695What 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.