ArticleData in brief2024
Surface seal image dataset of sterile barrier packaging.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Surface seal image dataset of sterile barrier packaging.Data in brief · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This manuscript delineates the constitution of a dataset engineered to bolster research endeavours in the realm of automated inspection of seals pertinent to Medical Device Packaging. The compendium encompasses a total of 1200 images of medical device pouches, with an equitable distribution between intact seals and those exhibiting defects. Each image boasts dimensions of 3008 by 4110 by 3 pixels. Accompanying the visual data is a suite of metadata essential for the importation of images into processing software. The defects within the dataset were meticulously crafted through a process of analysis and replication of imperfections commonly encountered on manufacturing lines, with the invaluable insights of seasoned inspectors and the support of Boston Scientific. The acquisition of images was conducted within a bespoke rig, meticulously designed to mirror the conditions prevalent in manufacturing environments. In addition to the imagery, the dataset contains binary labels and segmentation masks that delineate the defects, thereby facilitating the seamless utilization of the dataset for the training and validation of Machine Learning algorithms.
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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.