Improving 3D Ultrasound Scan Adequacy Classification Using a Three-Slice Convolutional Neural Network Architecture
Bibliographic record
Abstract
Accurately detecting and diagnosing developmental dysplasia of the hip (DDH), a common hip instability condition among infants, requires ultrasound (US) image data that captures the relevant anatomical structures. Our group has recently introduced a technique for automatically processing 3D US scans of the neonatal hip that significantly reduces dysplasia metric measurement variability, but it can be challenging for a clinician to know at the time of acquisition if they have acquired a volume suitable for analysis. We have previously introduced a single-slice-based approach for assessing adequacy of acquired volumes that worked reasonably well, but we believe that more explicitly three-dimensional approaches would be more robust and reliable. Here, we propose a new technique based on a convolutional neural network (CNN) architecture that incorporates inter-slice information and transfer learning. Our classifier labels volumes as adequate or inadequate for subsequent interpretation based on detecting the presence of key hip anatomical structures needed for DDH diagnosis. We validate our approach on 40 datasets from 15 pediatric patients and demonstrate a slice classification rate of 93% (improving on our previous implementation by 3%) with average processing time of 2 seconds per US volume. We expect automatic US scan adequacy assessment to have significant clinical impact with the potential to help in imaging standardization, improving efficiency of measuring DDH metrics, and improving accuracy of clinical decision making.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".