Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning
Bibliographic record
Abstract
Purpose To develop an end-to-end pipeline to localize and identify cervical spine hardware brands on routine cervical spine radiographs. Materials and Methods In this single-center retrospective study, patients who received cervical spine implants between 2014 and 2018 were identified. Information on the implant model was retrieved from the surgical notes. The dataset was filtered for implants present in at least three patients, which yielded five anterior and five posterior hardware models for classification. Images for training were manually annotated with bounding boxes for anterior and posterior hardware. An object detection model was trained and implemented to localize hardware on the remaining images. An image classification model was then trained to differentiate between five anterior and five posterior hardware models. Model performance was evaluated on a holdout test set with 1000 iterations of bootstrapping. Results A total of 984 patients (mean age, 62 years ± 12 [standard deviation]; 525 women) were included for model training, validation, and testing. The hardware localization model achieved an intersection over union of 86.8% and an F1 score of 94.9%. For brand classification, an F1 score, sensitivity, and specificity of 98.7% ± 0.5, 98.7% ± 0.5, and 99.2% ± 0.3, respectively, were attained for anterior hardware, with values of 93.5% ± 2.0, 92.6% ± 2.0, and 96.1% ± 2.0, respectively, attained for posterior hardware. Conclusion The developed pipeline was able to accurately localize and classify brands of hardware implants using a weakly supervised learning framework. Keywords: Spine, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms, Prostheses, Semisupervised Learning Supplemental material is available for this article. © RSNA, 2022 See also commentary by Huisman and Lessmann in this issue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.003 |
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".