Automatic classification of levator ani muscle avulsion in 3D transperineal ultrasound images
Bibliographic record
Abstract
Introduction Levator ani muscle (LAM) avulsion is a common traumatic injury of the pelvic floor muscle occurring during vaginal childbirth and is linked to the development of pelvic organ prolapse (POP). POP is a pelvic floor disorder that affects up to 40% of women during their lifetime. Pelvic floor ultrasound imaging is used to diagnose LAM avulsion, but it requires trained experts and is time-consuming, leading to weeks-long delays in receiving diagnostic results and treatment. The purpose of this study is to demonstrate the feasibility of a deep learning system to automatically classify the degree of LAM avulsion from 3D transperineal ultrasound images (TPUS). Methods 3D TPUS images of the pelvic floor from 150 patients with and without POP-related LAM avulsion were collected. Out of these, 113 patients were included in the study. Over 650 key slices were extracted from the ultrasound volumes and cropped to a region of interest. A two-stage cascading ensemble architecture was developed, combining three convolutional neural networks (MobileNetV3-Small, EfficientNet- B0, and RegNetY-800MF) with a final decision layer. The system performs hierarchical classification: first distinguishing between normal and avulsion cases, then determining unilateral versus bilateral involvement, and finally classifying the degree of avulsion. Results In 5-fold cross-validation, the ensemble model demonstrated strong performance in both binary classification tasks. For avulsion detection, it achieved 86% accuracy, 88% sensitivity, 85% specificity, and an AUC of 0.94, consistently outperforming individual base classifiers (which achieved AUCs of 0.71 to 0.75). For bilateral/unilateral classification, the model achieved 80% accuracy, 82% sensitivity, 78% specificity, and an AUC of 0.87. When evaluated on a test set for final patient-level classification across five classes (normal, complete bilateral avulsion, complete unilateral avulsion, partial bilateral avulsion, and partial unilateral avulsion), the system achieved 46% accuracy. Conclusion This study demonstrates the feasibility of a deep learning classification system to automatically classify the degree of LAM avulsion from 3D TPUS images. While the system showed promising performance in binary classification tasks, its sequential decision-making design means that a single slice misclassification in the first classification stage can impact the final patient-level accuracy. Additionally, the limited dataset size can hinder the model’s ability to generalize effectively to unseen cases. Despite these limitations, the developed system shows the potential to expedite LAM avulsion diagnosis, overcoming the time constraints of manual diagnosis. This approach can broaden screening access, benefiting areas with limited healthcare resources, by reducing expert reliance and enabling timely treatment. Future work with larger and more diverse datasets could help address current limitations and further improve classification accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".