Validity of a fast automated 3d spine reconstruction measurements for biplanar radiographs: SOSORT 2024 award winner
Bibliographic record
Abstract
Abstract Purpose To validate a fast 3D biplanar spinal radiograph reconstruction method with automatic extract curvature parameters using artificial intelligence (AI). Methods Three-hundred eighty paired, posteroanterior and lateral, radiographs from the EOS X-ray system of children with adolescent idiopathic scoliosis were randomly selected from the database. For the AI model development, 304 paired images were used for training; 76 pairs were employed for testing. The validation was evaluated by comparing curvature parameters, including Cobb angles (CA), apical axial vertebral rotation (AVR), kyphotic angle (T1–T12 KA), and lordotic angle (L1–L5 LA), to manual measurements from a rater with 8 years of scoliosis experience. The mean absolute differences ± standard deviation (MAD ± SD), the percentage of measurements within the clinically acceptable errors, the standard error of measurement (SEM), and the inter-method intraclass correlation coefficient ICC [2,1] were calculated. The average reconstruction speed of the 76 test images was recorded. Results Among the 76 test images, 134 and 128 CA were exported automatically and measured manually, respectively. The MAD ± SD for CA, AVR at apex, KA, and LA were 3.3° ± 3.5°, 1.5° ± 1.5°, 3.3° ± 2.6° and 3.5° ± 2.5°, respectively, and 98% of these measurements were within the clinical acceptance errors. The SEMs and the ICC [2,1] for the compared parameters were all less than 0.7° and > 0.94, respectively. The average time to display the 3D spine and report the measurements was 5.2 ± 1.3 s. Conclusion The developed AI algorithm could reconstruct a 3D scoliotic spine within 6 s, and the automatic curvature parameters were accurately and reliably extracted from the reconstructed images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 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".