The use of Prototypical and Siamese Networks in the determination of lower extremity injuries in professional football players with thermographic data
Bibliographic record
Abstract
Early diagnosis of lower extremity injuries in professional football players is crucial for maintaining performance and minimising long-term risks. Despite the growing use of thermographic imaging as a non-invasive tool for detecting musculoskeletal disorders, its integration into automated injury detection systems remains limited, particularly under data-scarce conditions. Given the need for effective early detection methods and the potential of thermography in sports medicine, this study investigates the applicability of deep learning models for classifying lower extremity injuries. Specifically, it evaluates the performance of Prototypical Network and Siamese Network models using thermographic data collected from professional athletes. The original dataset consists of images from 16 healthy and 9 injured individuals, and through augmentation it was expanded to 360 healthy and 180 injured samples. The Prototypical Network achieved an accuracy of 97.78%, while the Siamese Network attained 94%. These findings indicate that both models are capable of accurate injury detection, despite challenges posed by class imbalance and limited data availability. In conclusion, the study highlights the effectiveness of thermographic imaging combined with deep metric learning in identifying injuries in professional football players and suggests that reliable results can be achieved even in constrained data environments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".