Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment
Bibliographic record
Abstract
Abstract Purpose: An efficient physics-informed deep learning approach for extracting spinopelvic measures from X-ray images is introduced and its performance is evaluated against manual annotations. Methods: Two datasets, comprising a total of 1470 images, were collected to evaluate the model’s performance. We propose a novel method of detecting landmarks as objects, incorporating their relationships as constraints ( LanDet ). Using this approach, we trained our deep learning model to extract five spine and pelvis measures: Sacrum Slope (SS), Pelvic Tilt (PT), Pelvic Incidence (PI), Lumbar Lordosis (LL), and Sagittal Vertical Axis (SVA). The results were compared to manually labelled test dataset (GT) as well as measures annotated separately by three surgeons. Results: The LanDet model was evaluated on the two datasets separately and on an extended dataset combining both. The final accuracy for each measure is reported in terms of Mean Absolute Error (MAE), Standard Deviation (SD), and R Pearson correlation coefficient as follows: $$[SS^\circ : 3.7 (2.7), R = 0.89]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>S</mml:mi> <mml:msup> <mml:mi>S</mml:mi> <mml:mo>∘</mml:mo> </mml:msup> <mml:mo>:</mml:mo> <mml:mn>3.7</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>2.7</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.89</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> , $$[PT^\circ : 1.3 (1.1), R = 0.98], [PI^\circ : 4.2 (3.1), R = 0.93], [LL^\circ : 5.1 (6.4), R=0.83], [SVA(mm): 2.1 (1.9), R = 0.96]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>P</mml:mi> <mml:msup> <mml:mi>T</mml:mi> <mml:mo>∘</mml:mo> </mml:msup> <mml:mo>:</mml:mo> <mml:mn>1.3</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1.1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.98</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>P</mml:mi> <mml:msup> <mml:mi>I</mml:mi> <mml:mo>∘</mml:mo> </mml:msup> <mml:mo>:</mml:mo> <mml:mn>4.2</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>3.1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.93</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>L</mml:mi> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>∘</mml:mo> </mml:msup> <mml:mo>:</mml:mo> <mml:mn>5.1</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>6.4</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.83</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>S</mml:mi> <mml:mi>V</mml:mi> <mml:mi>A</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>m</mml:mi> <mml:mi>m</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>:</mml:mo> <mml:mn>2.1</mml:mn> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1.9</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>,</mml:mo> <mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.96</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . To assess model reliability and compare it against surgeons, the intraclass correlation coefficient (ICC) metric is used. The model demonstrated better consistency with surgeons with all values over 0.88 compared to what was previously reported in the literature. Conclusion: The LanDet model exhibits competitive performance compared to existing literature. The effectiveness of the physics-informed constraint method, utilized in our landmark detection as object algorithm, is highlighted. Furthermore, we addressed the limitations of heatmap-based methods for anatomical landmark detection and tackled issues related to mis-identifying of similar or adjacent landmarks instead of intended landmark using this novel approach.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".