Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening

Chao Fan; Dongyang Jin; Fengwei An; Shiqi Yu; Zirui Zhou; Zizhao Peng

REVIEW 3 major objections 5 minor 22 references

Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Clinical pose prior lifts scoliosis screening to 86% accuracy

desk verdict Solid, well-ablated paper on pose-based scoliosis screening with a new dataset and a useful clinical-prior attention module; the headline gain over baselines needs re-verification because of an ambiguous normalization step and missing error bars. read the letter →

arxiv 2509.00872 v1 pith:Y2ZFB47F submitted 2025-08-31 cs.CV cs.AI

Zirui Zhou , Zizhao Peng , Dongyang Jin , Chao Fan , Fengwei An , Shiqi Yu This is my paper · ORCID

classification cs.CVcs.AI

keywords scoliosisscreening2DposeestimationposturalasymmetryclinicalpriorattentionmechanismViTPoseScoliosis1Kgaitanalysis

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that 2D pose keypoints, when enriched with explicit clinical asymmetry descriptors, can outperform silhouette-based methods for scoliosis screening from ordinary walking videos. The authors introduce Scoliosis1K-Pose, a 447,900-frame pose annotation set for 1,050 adolescents, and a Dual Representation Framework (DRF) that combines a continuous skeleton map with a discrete Postural Asymmetry Vector (PAV). A PAV-Guided Attention module uses the PAV as a clinical prior to direct feature extraction. On the Scoliosis1K benchmark, DRF reaches 86.0% accuracy and 80.8% macro F1, beating the silhouette-based ScoNet-MT (82.0% accuracy, 75.4 F1) and pose-coordinate baselines (67.0–67.5 F1). If the result holds, it would make privacy-preserving, interpretable scoliosis screening feasible using only natural gait videos.

What carries the argument

The Postural Asymmetry Vector (PAV) is a matrix over anatomically symmetric keypoint pairs (e.g., shoulders, hips, knees) with three metrics: vertical deviation, midline deviation, and angular deviation, refined by interquartile-range outlier removal, temporal mean aggregation, and min-max normalization. The PAV-Guided Attention (PGA) module takes the PAV as input and learns channel-wise and spatial-wise attention weights via a linear layer and a 1D convolution, then applies them to the skeleton-map features. The PAV supplies the clinical prior; the PGA enforces it on the learned representation.

What would settle it

Compute the PAV descriptors for the same adolescents and correlate them with Cobb angles from radiographs or with clinical Adam's forward bend test scores; if the PAV shows no correlation with curve severity, or if adding the PGA module to the skeleton-map baseline stops helping when keypoints are artificially corrupted, the clinical-prior claim would be undermined.

Watch

Extended reading notes

Core claim

The central claim is that modeling clinically defined postural asymmetries—vertical, midline, and angular deviations between paired keypoints—as an explicit prior improves pose-based scoliosis classification. The Dual Representation Framework converts per-frame 2D keypoints into a two-channel Gaussian skeleton map and a temporally aggregated PAV matrix. The PAV, rather than raw coordinates or learned features, guides the attention module: it generates channel and spatial weights that recalibrate the skeleton-map features before classification. Experiments show the full model outperforms the same architecture without attention, with only the skeleton map, or with alternative guidance sources

Load-bearing premise

The framework assumes the 2D pose keypoints produced by ViTPose are accurate enough across natural walking videos to capture subtle left-right asymmetries that survive temporal averaging, and the paper does not verify this by comparing pose-derived asymmetry with X-ray or clinical measurements.

Editorial extensions

If this is right

Pose data alone can serve as a sufficient input for scoliosis screening, avoiding the need for silhouettes or exposed-torso imagery.
The PAV's three asymmetry metrics capture clinically meaningful cues that can be computed cheaply from any 2D pose estimator, making the approach easy to deploy in school or community screening.
Adding the PAV-guided attention module to an existing skeleton-map pipeline yields consistent gains, suggesting explicit clinical priors can complement learned features in medical image analysis.
The Scoliosis1K-Pose dataset provides a ready benchmark for future pose-based scoliosis research, including methods that fuse silhouette and pose modalities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The PAV descriptions might transfer to other asymmetry-related conditions, such as Parkinson's disease gait or stroke-induced hemiparesis, where left-right deviations are diagnostic.
The framework's reliance on ViTPose keypoints means its gains could shrink in settings with poor pose estimation; testing the same pipeline with other detectors or with 3D pose could reveal the extent of that dependency.
Because the paper does not compare PAV descriptors with radiological measurements, an obvious next step is to regress Cobb angle or curve severity from the PAV, which would strengthen the clinical grounding of the method.
Combining the skeleton map and PAV with the original silhouette stream in a three-branch fusion might further improve accuracy, since silhouettes and skeletons carry complementary shape and asymmetry cues.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper does something real. It contributes a new dataset, Scoliosis1K-Pose (447,900 frames of ViTPose keypoints from 1,050 adolescents), and a clean framework, DRF, that combines continuous skeleton maps with a hand-crafted Postural Asymmetry Vector (PAV). The PAV-guided attention (PGA) module is the key piece: it uses clinical asymmetry descriptors to modulate feature extraction. The ablations are genuinely informative—PAV beats self-attention, all-ones, random, and learnable priors by solid margins (Table 2b). That is a good demonstration that the prior itself carries signal, not just the attention machinery. The visualizations also match the claim: the model attends to both shoulders and pelvis rather than one body side. I believe the central idea is sound and the dataset will be useful to the community.

The soft spots are real but not fatal. The stress-test concern about PAV min-max normalization "applied across the dataset" is legitimate: it is ambiguous whether the scaling constants were fitted on the training split only. If they include test sequences, that is transductive leakage, and the 3.5% accuracy gain over ScoNet-MTske could be inflated. The fix is simple—clarify and re-run with training-only statistics. Even without leakage, the absence of error bars in Tables 1 and 2 is a weakness for a medical screening claim. One run of a deep network can move by a couple of points; the reported differences are plausible but not yet backed by variance. Also, the comparative evaluation is thin: only three baselines, all on one dataset. That is acceptable for a new benchmark, but it means the "state-of-the-art" claim is really "best on this benchmark so far." Finally, the PAV relies entirely on ViTPose keypoints. The paper does not independently check whether the extracted asymmetries correlate with radiological measures. IQR filtering and temporal averaging mitigate noise, but a sanity check would strengthen the clinical-prior story.

Overall: this deserves a serious referee. I would accept with requested revisions, mainly the normalization clarification, error bars, and ideally a small keypoint-noise analysis. The paper is clearly written, the ablation design is above average, and the dataset alone is a contribution. My recommendation: send it to peer review and ask for those re-verifications before trusting the headline numbers.

Referee Report

3 major / 5 minor

Summary. The paper proposes a pose-based scoliosis screening framework. It introduces Scoliosis1K-Pose, a 2D keypoint annotation set for the Scoliosis1K dataset, and the Dual Representation Framework (DRF), which combines a continuous Gaussian skeleton map with a hand-crafted Postural Asymmetry Vector (PAV) that encodes vertical, midline, and angular asymmetries between paired keypoints. A PAV-Guided Attention (PGA) module uses the PAV as a clinical prior to recalibrate features extracted from the skeleton map. On the fixed 745/748 train/test split of Scoliosis1K, the method reports 86.0% accuracy and 80.8% macro F1, outperforming silhouette-based ScoNet-MT and pose-coordinate baselines. Ablations indicate that both channel and spatial attention branches contribute, and that PAV guidance outperforms self-attention, fixed vectors, and a learnable prior.

Significance. If the reported numbers are sound, the paper makes a useful contribution: it introduces a new pose dataset for a clinically relevant screening task, proposes a clinically interpretable prior that is not derived from labels, and provides public code and data. The PAV construction is transparent and the ablation design is thoughtful. The main risk is methodological: the normalization step in §3.1 may leak test information, and the reported improvements are not accompanied by any uncertainty quantification. Because the central SOTA claim and the conclusion that PAV guidance is effective rest on the Tables 1 and 2 comparisons, these issues are load-bearing.

major comments (3)

[§3.1 (Sequence-Level PAV Refinement, step 3) and §4.1 (Evaluation Protocol)] The PAV normalization is described as 'Min-Max scaling is applied across the dataset for all P × M dimensions.' The evaluation uses a fixed train/test split (745/748 sequences). If the min/max statistics are computed on the union of training and test PAVs, then the test PAVs input to the PGA module in §3.2 are not unseen; the normalization constants encode information about the test distribution. This can inflate the DRF versus ScoNet-MTske gains in Table 1 (accuracy +3.5%, F1 +4.2%) and the ablation comparisons in Table 2b. The paper must state explicitly that the normalization statistics were fitted on the training split only, and ideally report results with normalization fitted per split. Without this clarification, the central comparison is unverifiable.
[Tables 1 and 2] All reported numbers appear to come from a single run on a single split. The differences that support the main claims are modest: +3.5% accuracy and +4.2% F1 in Table 1, and similar gaps in the Table 2 ablations. No error bars, standard deviations, or multiple-seed results are provided. Since the test set is small and the class distribution is imbalanced, these gaps could fall within run-to-run variation. The authors should report mean ± std across at least 3–5 random seeds, or a statistical significance test (e.g., paired bootstrap over test sequences).
[§2 and §3.1 (ViTPose keypoint dependence)] The PAV is computed from ViTPose 2D keypoints, and the method assumes that these keypoints are sufficiently accurate to capture subtle left-right asymmetries in natural walking videos. The paper does not independently validate this premise. A concrete test would be to compare pose-derived asymmetries (e.g., shoulder height difference, pelvic tilt) with radiological or clinical measures on a subset of subjects, or to inject synthetic keypoint noise and measure DRF's performance sensitivity. If the PAV is dominated by pose-estimation noise, the reported improvement could be an artifact rather than a clinically meaningful prior.

minor comments (5)

[§3.1, Eq. (1) and Eq. (2)] The Gaussian heatmap parameter σ is introduced but its numerical value is not given in the implementation details. Please specify σ (or state that it follows the skeleton-map literature).
[§3.1, step 3] The IQR outlier removal is described only as 'statistical filtering.' Please state the IQR multiplier (e.g., 1.5×) and what happens when a value is removed, for reproducibility.
[§3.2, Training Objective] The combined loss Ltotal = Lce + Ltriplet is mentioned, but the triplet loss setup (margin, sampling strategy, how anchors/positives/negatives are defined) is not described. Please add details or cite the exact protocol from ScoNet-MT.
[Figure 3] The feature response heatmaps would be more convincing if the scale or normalization of the heatmap was identical across all three panels. Please state the visualization protocol or ensure a common color scale.
[Table 1] OF-DDNet and GPGait are adapted to 2D pose inputs, but the adaptation details are not given. Please specify how the adaptation was performed (e.g., input format, network changes) so readers can assess fairness.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the PAV is a hand-crafted input feature and the central SOTA claim rests on held-out evaluation, not on a derivation that reduces to its own inputs.

full rationale

The paper's central claim is that DRF, using a hand-crafted Postural Asymmetry Vector (PAV) as a clinical prior to guide attention on a skeleton map, achieves state-of-the-art scoliosis screening accuracy. Nothing in the derivation chain makes the prediction equivalent to the PAV or the PAV equivalent to the label. The PAV is computed from pose keypoints via fixed geometric formulas (vertical, midline, and angular deviations) defined in Section 3.1, without using the class labels or the model's output. The PGA module then uses this vector as an attention prior, and the classifier is trained end-to-end with a cross-entropy plus triplet loss on a fixed train/test split (Section 4.1). There is no equation in which the output logits are defined as the PAV, nor is any parameter fitted to the test labels and then renamed as a prediction. Self-citations to ScoNet-MT [21] and SkeletonGait [6] are used for the dataset, encoder architecture, skeleton-map construction, and training protocol; these are external components, not results derived from the present paper's claims. The only notable concern is the sentence in Section 3.1 that 'Min-Max scaling is applied across the dataset,' which is ambiguous and could imply use of test-set statistics. That is a potential experimental-leakage issue, not a circularity step, because it does not reduce the central derivation to its inputs by construction. No circular step can be exhibited from the paper's own equations, so the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim depends on hand-crafted preprocessing choices (sigma, normalization, IQR) and on the clinical validity of the PAV as a prior, neither of which is independently verified.

free parameters (3)

Heatmap sigma (σ) for Gaussian rendering
Hand-chosen hyperparameter in Eqs. (1) and (2) controlling the spread of joint and limb heatmaps; not stated or tuned in the paper.
PAV min-max normalization bounds
Min and max per dimension computed 'across the dataset'; if computed on the full dataset including test, they are fitted on test data; the paper does not clarify training-only.
IQR outlier removal threshold
Interquartile range filtering used in PAV sequence refinement; standard 1.5xIQR is assumed but not stated.

assumptions (3)

domain assumption ViTPose 2D keypoints accurately localize anatomical landmarks in walking adolescents
Used to construct the skeleton map and PAV (Section 2); if keypoint noise is high, the asymmetry metrics are corrupted.
domain assumption The three asymmetry metrics (vertical, midline, angular deviations) capture clinically relevant postural asymmetries for scoliosis
The PAV is the clinical prior; this assumption is asserted without validation against clinical measures (Section 3.1).
ad hoc to paper The specific set of P=8 symmetric keypoint pairs is sufficient
The paper selects COCO pairs (eyes, ears, shoulders, elbows, wrists, hips, knees, ankles) without justifying why these pairs or why other pairs are excluded.

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Cite this review

Pith. "Pith review of Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening." pith.science (2026). https://pith.science/paper/Y2ZFB47F

@misc{pith2026250900872,
  author       = {Pith},
  title        = {Pith review of: Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y2ZFB47F}},
  note         = {Machine review of arXiv:2509.00872}
}

read the original abstract

Recent AI-based scoliosis screening methods primarily rely on large-scale silhouette datasets, often neglecting clinically relevant postural asymmetries-key indicators in traditional screening. In contrast, pose data provide an intuitive skeletal representation, enhancing clinical interpretability across various medical applications. However, pose-based scoliosis screening remains underexplored due to two main challenges: (1) the scarcity of large-scale, annotated pose datasets; and (2) the discrete and noise-sensitive nature of raw pose coordinates, which hinders the modeling of subtle asymmetries. To address these limitations, we introduce Scoliosis1K-Pose, a 2D human pose annotation set that extends the original Scoliosis1K dataset, comprising 447,900 frames of 2D keypoints from 1,050 adolescents. Building on this dataset, we introduce the Dual Representation Framework (DRF), which integrates a continuous skeleton map to preserve spatial structure with a discrete Postural Asymmetry Vector (PAV) that encodes clinically relevant asymmetry descriptors. A novel PAV-Guided Attention (PGA) module further uses the PAV as clinical prior to direct feature extraction from the skeleton map, focusing on clinically meaningful asymmetries. Extensive experiments demonstrate that DRF achieves state-of-the-art performance. Visualizations further confirm that the model leverages clinical asymmetry cues to guide feature extraction and promote synergy between its dual representations. The dataset and code are publicly available at https://zhouzi180.github.io/Scoliosis1K/.

Figures

Figures reproduced from arXiv: 2509.00872 by the authors.

**Figure 2.** Overview of the proposed Dual Representation Framework (DRF). (a) The over [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Visualization of feature response heatmaps. (a) ScoNet-MT. (b) ScoNet-MTske. (c) Our DRF. 4.2 Comparison with State-of-the-Art Methods We perform comprehensive comparisons with state-of-the-art methods using various input representations. For silhouette-based methods, we compare our model with ScoNetMT [21]. For pose coordinate-based approaches, we include OF-DDNet [12] and GPGait [7]. OF-DDNet is adapted to pro… view at source ↗

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Reference graph

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

IEEE Transactions on Biomedical Engineering 60(8), 2262–2270 (2013)

Adankon, M.M., Chihab, N., Dansereau, J., Labelle, H., Cheriet, F.: Scoliosis follow-up us- ing noninvasive trunk surface acquisition. IEEE Transactions on Biomedical Engineering 60(8), 2262–2270 (2013)

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[2] [2]

Artificial intelligence in medicine 56(2), 99–107 (2012)

Adankon, M.M., Dansereau, J., Labelle, H., Cheriet, F.: Non invasive classification system of scoliosis curve types using least-squares support vector machines. Artificial intelligence in medicine 56(2), 99–107 (2012)

work page 2012

[3] [3]

Chinese Journal of Orthopaedics 40(23), 1574–1582 (2020)

of the Chinese Orthopaedic Association, S.S.G.: Clinical practice guideline and pathway for screening adolescent idiopathic scoliosis in china. Chinese Journal of Orthopaedics 40(23), 1574–1582 (2020)

work page 2020

[4] [4]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Duan, H., Zhao, Y ., Chen, K., Lin, D., Dai, B.: Revisiting skeleton-based action recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2969–2978 (2022)

work page 2022

[5] [5]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Fan, C., Liang, J., Shen, C., Hou, S., Huang, Y ., Yu, S.: Opengait: Revisiting gait recogni- tion towards better practicality. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9707–9716 (2023)

work page 2023

[6] [6]

In: Proceedings of the AAAI conference on artificial intelligence

Fan, C., Ma, J., Jin, D., Shen, C., Yu, S.: Skeletongait: Gait recognition using skeleton maps. In: Proceedings of the AAAI conference on artificial intelligence. vol. 38, pp. 1662–1669 (2024)

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In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Fu, Y ., Meng, S., Hou, S., Hu, X., Huang, Y .: Gpgait: Generalized pose-based gait recog- nition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19595–19604 (2023) 10 Z. Zhou et al

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[8] [8]

NPJ digital medicine 6(1), 156 (2023)

Islam, M.S., Rahman, W., Abdelkader, A., Lee, S., Yang, P.T., Purks, J.L., Adams, J.L., Schneider, R.B., Dorsey, E.R., Hoque, E.: Using ai to measure parkinson’s disease severity at home. NPJ digital medicine 6(1), 156 (2023)

work page 2023