ArticleFrontiers in veterinary science2022
A deep learning model for CT-based kidney volume determination in dogs and normal reference definition.
Article in Frontiers in veterinary science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- Improving Reproducibility of Volumetric Evaluation Using Computed Tomography in Pediatric Patients with Congenital Heart Disease.Pediatric cardiology · 2025Article
- Automated segmentation of canine pulmonary masses in CT imaging using AI.The veterinary quarterly · 2025Article
- Automated AI-based segmentation of canine hepatic focal lesions from CT studies.Frontiers in veterinary science · 2025Article
- Deep-learning-based automatic liver segmentation using computed tomography images in dogs.Frontiers in veterinary science · 2025Article
- Ultrasonographic evaluation of adrenal glands in clinically healthy Korean raccoon dogs (Frontiers in veterinary science · 2025Article
- Kidney/Aorta Ratio for Renal Morphometric Determination in Swine Subjected to Acute Kidney Injury Using an Optimized Surgical Model.Comparative medicine · 2024Article
- Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network.Frontiers in veterinary science · 2024Article
- Deep learning-based ultrasonographic classification of canine chronic kidney disease.Frontiers in veterinary science · 2024Article
- A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs.Frontiers in veterinary science · 2023Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kidney volume is associated with renal function and the severity of renal diseases, thus accurate assessment of the kidney is important. Although the voxel count method is reported to be more accurate than several methods, its laborious and time-consuming process is considered as a main limitation. In need of a new technology that is fast and as accurate as the manual voxel count method, the aim of this study was to develop the first deep learning model for automatic kidney detection and volume estimation from computed tomography (CT) images of dogs. A total of 182,974 image slices from 386 CT scans of 211 dogs were used to develop this deep learning model. Owing to the variance of kidney size and location in dogs compared to humans, several processing methods and an architecture based on UNEt Transformers which is known to show promising results for various medical image segmentation tasks including this study. Combined loss function and data augmentation were applied to elevate the performance of the model. The Dice similarity coefficient (DSC) which shows the similarity between manual segmentation and automated segmentation by deep-learning model was 0.915 ± 0.054 (mean ± SD) with post-processing. Kidney volume agreement analysis assessing the similarity between the kidney volume estimated by manual voxel count method and the deep-learning model was r = 0.960 (
Indexed as
Identifiers
What 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.