ArticleScientific reports2022
Developing a diagnosis model for dry eye disease in dogs using object detection.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- A novel method of evaluating the non-invasive tear film break-up time and progression of corneal opacification in dogs using imaging video.Frontiers in veterinary science · 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
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to develop an object detection method for the diagnosis of dry eye disease (DED) in dogs. To this end, a methodology was designed to evaluate ocular surface video images using the YOLOv5 model, which is an object detection algorithm that has been widely used because of its simple network structure and fast detection speed. Because the cornea is a transparent organ, an illuminator plate with grid squares was used to provide grid lines, which were analyzed as the reflected straight lines of the light source representing the precorneal tear film (PTF) stability. The original video consisted of the number of 12 normal images(normal, [Formula: see text] = 17) and the number of 15 abnormal images(abnormal, [Formula: see text] = 17), converted to JPEG images for labeling, learning, and model validation. The labeled image data were divided into a training image data set (normal, [Formula: see text] = 15,276; abnormal, [Formula: see text] = 26,196) to a validation image data set (normal, [Formula: see text] = 6546; abnormal, [Formula: see text] = 11,228). As a result of the experiment, the mean average precision ([Formula: see text]) achieved 0.995. This study proposes a method to effectively determine ocular surface status in dogs by using YOLOv5 and concludes that an object detection model can be used in the veterinary field.
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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.