Evaluating the impact of sex bias on AI models in musculoskeletal ultrasound of joint recess distension
Bibliographic record
Abstract
With the increasing integration of artificial intelligence (AI) in healthcare, concerns about bias in AI models have emerged, particularly regarding demographic factors. In medical imaging, biases in training datasets can significantly impact diagnostic accuracy, leading to unequal healthcare outcomes. This study assessed the impact of sex bias on AI models for diagnosing knee joint recess distension using ultrasound imaging. We utilized a retrospective dataset from community clinics across Canada, comprising 5,000 de-identified MSKUS images categorized by sex and clinical findings. Two binary convolutional neural network (BCNN) classifiers were developed to detect synovial recess distension and determine patient sex. The dataset was balanced across sex and joint recess distension, with models trained using advanced data augmentation and validated through both individual and mixed demographic scenarios using a 5-fold cross-validation strategy. Our BCNN classifiers showed that AI performance varied significantly based on the training data's demographic characteristics. Models trained exclusively on female datasets achieved higher sensitivity and accuracy but exhibited decreased specificity when applied to male images, suggesting a tendency to overfit female-specific features. Conversely, classifiers trained on balanced datasets displayed enhanced generalizability. This was evident from the classification heatmaps, which varied less between sexes, aligning more closely with clinically relevant features. The study highlights the critical influence of demographic biases on the diagnostic accuracy of AI models in medical imaging. Our results demonstrate the necessity for thorough cross-demographic validation and training on diverse datasets to mitigate biases. These findings are based on a supervised CNN model; evaluating whether they extend to other architectures, such as self-supervised learning (SSL) methods, foundation models, and Vision Transformers (ViTs), remains a direction for future research.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".