Novel, Markerless, Kinematic Tool Utilising a Deep Neural Network for Analysis of Joint Range of Motion and Lameness In Dogs
Bibliographic record
Abstract
Introduction: Kinematics is a widely accepted tool for gait assessment in dogs, however, its use requires specific equipment and knowledge which limits its clinical utility. Human research is now focused on markerless motion capture. Our study aimed to develop the first markerless kinematic tool to objectively measure the ROM of dogs’ joints. It also aims to identify changes associated with elbow osteoarthritis. Materials and Methods: Thirty-eight large breed dogs were enrolled—5 sound control dogs and 33 dogs with elbow osteoarthritis. The dogs were filmed trotting with a GoPro4. The videos were uploaded to a computer-learning program which utilized a deep neural network to autonomously map the joints and spine. The range of motion (ROM) of the shoulder, elbow, hip, and stifle were calculated in the control dogs and compared with previously reported kinematic values to validate the program. The analysis was repeated on dogs with elbow OA to identify any changes in the data. Results: The joint ROM in the control dogs were consistent with previously reported kinematic values. The program detected that elbow osteoarthritis in dogs caused a reduction in elbow ROM by 6.1 degrees. Discussion/Conclusion: This novel markerless kinematic tool can accurately and precisely track joints of large breed dogs at the trot and provide joint ROM consistent with more invasive kinematic tools. The program identified a reduction in elbow ROM in dogs affected by elbow OA consistent with previous studies. This research is an important step towards objective, accessible, and noninvasive tools for lameness assessment in dogs. Acknowledgment None. Publication History Article published online: 15 July 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".