An Automated System for Osteoarthritis Severity Scoring Using Residual Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Osteoarthritis (OA) is a chronic disease, characterized by progressive deterioration of cartilage tissue and consequent thinning of the cartilage layer within joints.This degradation leads to an increased likelihood of bone collision during movement, typically manifesting in patients as joint pain, knee swelling, stiffness, and difficulties in executing daily activities.The diagnosis of OA often involves the analysis of physical examination results, patient anamnesis, and additional supportive examinations, which are predominantly conducted manually.Addressing these challenges, this study harnesses Convolutional Neural Network (CNN) algorithms, specifically the Residual Neural Network and Mobile Neural Network architectures, to develop an automated system for classifying OA severity.Utilizing a knee image dataset comprised of 8260 records procured from NDA OAI, the model is trained and tested with a data split of 80% and 20% respectively.The Residual Neural Network (ResNet-101) architecture is employed for model training, utilizing Adam optimization with a learning rate set at 0.0001 over 50 epochs.The resulting model yields a training accuracy of 67.65%, and a validation accuracy of 57.06%.This study demonstrates the potential of CNN methods for automated, accurate classification of OA severity using knee imagery, thus offering a promising avenue for enhancing diagnostic efficiency and precision.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it