Duplicate Detection in FACS Plots

By  Markus | Aug 3, 2024
Duplicate Detection in FACS Plots

We just released a new AI-based algorithm to effectively detect duplications in fluorescence-activated cell sorting (FACS) plots. FACS plots are a common target for inappropriate image duplication. Hundreds of posts regarding FACS plots have emerged on PubPeer. While some duplications are honest mistakes caused by sloppy researchers, others are more severe and are used to fabricate data deliberately. Imagetwin now detects duplicates in FACS plots accurately, helping to identify problematic manuscripts.

Detection within and across publications

We detect two categories of duplicates: those appearing within the scanned manuscript and those appearing across publications. After uploading a manuscript, panels showing FACS plots are automatically extracted and compared to identify duplications. In addition to finding duplicates within the uploaded manuscript, the panels are compared against our image database, which initially contains 2.6 million FACS plots from previously published articles. We will constantly add new FACS plots to the database in future updates. The check against our database helps identify the reuse of FACS plots across publications.

Detection accuracy

We evaluated the FACS detection algorithm to measure accuracy, false positives, and runtime. We applied our algorithm to 152 FACS duplicates posted on PubPeer and correctly identified the duplication in 121 of the 152 cases (80% accuracy).

Cases where we could not find the duplication (i.e., false negatives) were blurry, low-resolution images, or cases where only a tiny fraction of the entire image was duplicated. To measure the false positive rate, we applied the algorithm to 3,000 FACS plots randomly sampled from papers published from 2010 to 2023. Across these 3,000 images, we found 5 false positive duplicates caused by similar labelling (i.e., lines, arrows, text descriptions). In addition to false positives, we detected more than 50 suspicious, probably inappropriate duplicates.

The new algorithm to detect duplicates in FACS plots is efficient: we measured a runtime of 30 milliseconds per image, so for checking 100 FACS plots, the results will be ready in just 3 seconds.

Evaluation results for FACS duplicate detection
Metric Result
Accuracy Detected 121 of 152 duplicates sampled from PubPeer
False positives 5 false positives in 3,000 checked FACS plots
Runtime 30 milliseconds per FACS plot

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Frequently asked questions

Imagetwin is software designed to detect integrity issues in figures of scientific articles. It helps identify inappropriate manipulations and duplications in various figure types, including western blots, microscopy images, and light photography.

Imagetwin is beneficial for researchers, peer reviewers, journal editors, and institutions aiming to uphold the quality and trustworthiness of scientific publications by ensuring the integrity of visual data.

Users can upload a PDF or multiple image files to Imagetwin. The software then scans the content using algorithms and vast databases of published scientific figures to detect potential integrity issues. Within seconds, results are presented through a web interface, highlighting any detected problems for review.

Yes, we prioritize data privacy and security, ensuring that all image indexing and exchanges are protected with industry-standard encryption and security best practices.

Create an account and start using Imagetwin immediately. We prepared a few example documents that you can scan free of charge.

Yes, Imagetwin is a powerful addition to the peer-review process. It automatically detects various integrity issues, which can then be quickly verified by a reviewer, enhancing the efficiency and accuracy of the review process. Imagetwin also partners with industry leaders in publishing and scholarly workflows, such as Morressier, TNQ Technologies and more, transforming how research is submitted, reviewed and published.

For more detailed guidance on using Imagetwin, contact our support team through our Contact Us page.