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REVIEW 4 major objections 4 minor 33 references

The paper claims that a four-stage deep-learning pipeline, ARTSS, can score rheumatoid arthritis damage on hand X-rays with error below one Sharp point, and that its best model—a Vision Transformer—achieves a mean absolute error of 0.95 on

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2026-08-04 22:58 UTC pith:IVKJ4AR3

load-bearing objection The padding-plus-masking idea for variable joint counts is genuinely worth knowing, but the paper's central performance table is internally impossible (RMSE below MAE) and the clinical claim about reader variability is never measured. the 4 major comments →

arxiv 2509.06854 v1 pith:IVKJ4AR3 submitted 2025-09-08 cs.CV cs.AI

Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice

Hajar Moradmand , Lei Ren This is my paper
classification cs.CV cs.AI
keywords Automated Radiographic Sharp ScoringDeep LearningJoint IdentificationRheumatoid ArthritisX-ray imagesVision TransformerTotal Sharp/van der Heijde score
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper claims that a fully automated four-stage pipeline, ARTSS, can score rheumatoid arthritis damage on hand X-rays with enough accuracy and consistency to be clinically useful. Its goal is to replace or support the manual Total Sharp/van der Heijde score, a slow and reader-dependent measure experts use to track joint erosion and joint-space narrowing. The pipeline orients and segments the hand, identifies up to 11 joints per hand with 99% reported accuracy, and then regresses the two-radiologist average Sharp score. The best model, a Vision Transformer, is reported to have a mean absolute error of 0.95 points and a Huber loss of 0.87 on 291 unseen patients. The distinctive design choice is including severe cases where joints have vanished, by padding variable-length joint sequences and masking the padding during training.

Core claim

The paper's central claim is that a standardized, fully automated pipeline—ARTSS—can score structural damage in rheumatoid arthritis hand radiographs at a level useful for clinical practice. On 970 public hand X-rays scored by two radiologists, the pipeline aligns images, segments hands with U-Net, identifies 11 joints per hand with YOLOv7 (99% accuracy), and then regresses the average reader Total Sharp/van der Heijde score using a custom Vision Transformer. The authors report the ViT achieves a mean absolute error of 0.95, RMSE 0.93, and Huber loss 0.87 on 291 unseen subjects, with VGG16 and VGG19 the next-best alternatives. The framework is designed to include patients with deformed or di

What carries the argument

The central object is the Total Sharp/van der Heijde score (TSS), the standard radiographic measure of RA joint damage that combines erosion and joint-space narrowing across 16 hand joints. The mechanism that carries the argument is the variable-length joint-image sequence: detected joints are cropped, padded to a fixed maximum length, and masked during training so padded slots contribute no learning signal. That lets one fixed-size deep-learning regressor—a Vision Transformer built on image-patch attention—score patients whose hands have different numbers of visible joints, including the severe cases previous systems excluded. The Vision Transformer is what turns the padded joint sequence i

Load-bearing premise

The central claim rests on the reported test-set error metrics being computed and averaged correctly; the printed average RMSE of 0.93 is below the average MAE of 0.95 for the same model, which is impossible for any error distribution, so the headline numbers as printed are not self-consistent.

What would settle it

Re-run the Vision Transformer on the 291 test images from the public dataset and recompute MAE, RMSE, and Huber loss per fold. RMSE must be at least MAE for any error list, so a reproduced result with average RMSE below average MAE would show the published metrics cannot be genuine; matching the printed 0.95/0.93 pair would support them.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • If the reported 0.95 mean absolute error reproduces in other datasets, ARTSS could act as an automated pre-screen or second reader for hand X-ray Sharp scoring.
  • The pipeline would reduce dependence on expert radiologists for routine RA damage tracking, which matters in centers without specialty readers.
  • Patients with severe erosion and disappearing joints could remain in automated scoring workflows instead of being excluded, broadening the population the model covers.
  • Because the stages are modular, the same orientation-segmentation-detection-regression structure could be retrained for other joints or imaging protocols.
  • A reproducible mean error below one Sharp point would put automated scoring near or within typical reader agreement, supporting the paper's claim of reduced variability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The paper does not measure the two radiologists' disagreement on this dataset, so a direct comparison of ARTSS error to that measured inter-reader spread would test the headline claim of reduced variability.
  • The padding-and-masking trick should transfer to other anatomical scoring tasks where landmarks disappear with disease severity, such as RA foot joints or osteoarthritic knees.
  • A per-joint error analysis, rather than whole-hand TSS error, would show whether the model's remaining mistakes concentrate in eroded or vanished joints, indicating where human review still adds value.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The manuscript presents ARTSS, a four-stage deep-learning pipeline for automated Total Sharp/van der Heijde Score (TSS) assessment from hand X-rays: (I) orientation and preprocessing, (II) U-Net hand segmentation, (III) YOLOv7 joint identification, and (IV) TSS regression with several CNN/transformer backbones. The model is trained on 970 images using the average of two radiologists' TSS scores as ground truth, with 3-fold cross-validation and a 291-subject held-out test set. The authors report that a Vision Transformer achieves the best performance (Huber loss 0.87, MAE 0.95, RMSE 0.93). The paper's core clinical claim is that ARTSS reduces inter- and intra-reader variability and improves clinical practice.

Significance. If the reported accuracy is accurate and the pipeline is reproducible, automated TSS scoring would be valuable for RA monitoring, especially in resource-limited settings. The paper addresses a real clinical need, uses a relatively large public dataset, and introduces a plausible strategy for variable-length joint sequences. However, the quantitative claims are undermined by a mathematical impossibility in the reported metrics and the central clinical claim is asserted without measurement. The authors are transparent about some limitations (single center, hand-only images), but the internal metric inconsistency is a more fundamental issue. No code or evaluation scripts are provided, only a data link, so the reader cannot verify the reported numbers.

major comments (4)
  1. [Table 1 (ViT rows)] For any error vector, RMSE is always at least MAE. Table 1 reports ViT fold-1 RMSE=0.89 with MAE=0.99, and average RMSE=0.93 below average MAE=0.95. These values are mathematically impossible, indicating a bug in metric computation or inconsistent test sets. Since the abstract and discussion highlight the ViT MAE and Huber loss, the reliability of all metrics in Table 1 is now in question. Recompute all metrics and verify with code before resubmission.
  2. [Abstract; Discussion (first paragraph)] The central claim that ARTSS 'reduces inter- and intra-reader variability' is never measured. No ICC, kappa, per-reader difference, or comparison between model error and the two-reader disagreement is reported. Using the average of two radiologists as the training target does not demonstrate a variability reduction. The existing two independent readings should be used to compute inter-reader agreement (e.g., MAE or ICC) and compare it against the model's error; otherwise the clinical claim remains unsupported.
  3. [Methods (TSS target); Table 1] The manuscript never states the range or normalization of the TSS target. The hand-only TSS can reach hundreds of points, so an MAE of 0.95 is uninterpretable without knowing the scale. If predictions are on a normalized scale, the transformation must be specified; if raw points, the near-perfect MAE is not contextualized against human reader variability. This is required to evaluate the clinical relevance of the reported errors.
  4. [Results (Joint identification); Table 2] The '99% accuracy' for joint identification is unsubstantiated. The Results section mentions Figure 4 and MAP but reports no numeric value for accuracy or MAP. Table 2 lists 'This Study ... 99%' but no supporting result or metric definition appears in the text. For object detection, accuracy is ambiguous; the authors should report MAP and recall and define what 'accuracy' means.
minor comments (4)
  1. [Throughout] Typos: 'Total Sharo scoring' (Table 1 caption), 'Statical analysis' (Methods header), 'KED curves' (Figure 2 caption should be KDE), and duplicate 'proximal interphalangeal (PI), proximal interphalangeal (PIP)' in Joint Identification.
  2. [Methods (Dataset)] Dataset counts are inconsistent: 970 images are described, but later '1080 images' are used for joint annotation and '582 images' were processed to generate masks with '382 ground truth masks'. Reconcile these numbers.
  3. [Methods (Cross-validation)] The relation between the 3-fold cross-validation (452 training, 227 validation per fold) and the 291-subject external test is unclear. Clarify whether the 291 are excluded from all folds and how the folds are constructed.
  4. [Data and Code Availability] Only a data link is provided; no evaluation code is available. Given the reported metric inconsistencies, releasing the metric-computation script and predictions would substantially increase confidence.

Circularity Check

0 steps flagged

No significant circularity: TSS predictions are genuine held-out outputs; the only self-citation is minor and non-load-bearing, while the unsupported reader-variability claim is a missing-baseline issue, not circularity.

full rationale

The paper's central quantitative chain is a supervised pipeline: preprocess -> segment -> detect joints -> regress TSS. The TSS regression target is the average of two radiologists' scores, and the test metrics in Table 1 are computed on 291 unseen subjects, so the ViT MAE (0.95) and Huber loss (0.87) are genuine held-out predictions rather than fitted values. Nothing in the method defines the TSS prediction in terms of the reported test values; the models are trained on fold splits and evaluated on held-out data. Therefore the empirical core is self-contained and not circular. The abstract/discussion claim that ARTSS 'reduces inter- and intra-reader variability' is not derived from any measured reader-variability baseline; the only operationalization is that the ground truth itself is the two-reader average, so any smoothing is in the labels, not demonstrated by the system. This is an unsupported clinical claim / missing-baseline issue, not a circular step under the strict definitions. The only author self-citation is [33], a radiomics reproducibility paper cited in a forward-looking sentence about integrating radiomics; it is not load-bearing for any method or result. Separately, Table 1 contains an internal inconsistency (ViT fold-1 RMSE 0.89 < MAE 0.99 and average RMSE 0.93 < average MAE 0.95, impossible because RMSE>=MAE), which is a correctness/evaluation-pipeline concern, not a circularity. Under the required evidence standard, no step can be shown to reduce by construction to its input, so the score is low.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical entities. Its contribution rests on standard deep learning components plus hand-chosen parameters (Huber delta, augmentation settings, padding length, detection thresholds) and on the unverified premise that the two-reader average is a clean target. The free-parameter count is moderate for an empirical ML paper, but non-reporting of several of them (delta, thresholds, padding length) weakens reproducibility.

free parameters (5)
  • Huber loss delta (sigma)
    Threshold in Equation (3) controls the quadratic/linear transition; its value is never reported.
  • Augmentation hyperparameters = rotation 10 deg, shifts 0.2, brightness 0.7-1.2, horizontal flip
    Chosen by hand; these are the only augmentation settings reported in Methods.
  • Padding length
    Set to the maximum number of detected joints across the dataset; the number itself is not reported.
  • Joint detection thresholds
    YOLOv7 confidence/IoU thresholds and the threshold used to report '99% accuracy' are not given.
  • Reorientation model outputs
    ResNet50 reorientation classes/bins for the stated 0-180 degree constraint are not described.
axioms (4)
  • domain assumption The average of two radiologists' TSS readings is an accurate, low-noise ground truth for joint damage.
    Load-bearing: every MAE/Huber number is measured against this target, yet no inter-reader agreement statistics are reported (Methods, Study population).
  • domain assumption The 382 manually selected segmentation masks correctly delineate the hand region.
    U-Net is trained and scored against masks chosen 'based on visual evaluation' with no stated criteria (Methods, Hand Segmentation).
  • domain assumption The 291 external test subjects are patient-level independent from the training folds and drawn from the same distribution.
    The split sizes (452+227 per fold and 291 test) sum to 970, suggesting a same-center held-out split, described in the abstract as 'external'.
  • domain assumption The van der Heijde scoring rules were applied correctly by both readers, including to joints that have disappeared.
    The entire label set depends on this; no calibration or audit against a reference reader is described.

pith-pipeline@v1.3.0-alltime-deepseek · 8982 in / 16428 out tokens · 162315 ms · 2026-08-04T22:58:53.835143+00:00 · methodology

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Cite this review

Pith. "Pith review of Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice." pith.science (2026). https://pith.science/paper/IVKJ4AR3

@misc{pith2026250906854,
  author       = {Pith},
  title        = {Pith review of: Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IVKJ4AR3}},
  note         = {Machine review of arXiv:2509.06854}
}
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read the original abstract

Assessing the severity of rheumatoid arthritis (RA) using the Total Sharp/Van Der Heijde Score (TSS) is crucial, but manual scoring is often time-consuming and subjective. This study introduces an Automated Radiographic Sharp Scoring (ARTSS) framework that leverages deep learning to analyze full-hand X-ray images, aiming to reduce inter- and intra-observer variability. The research uniquely accommodates patients with joint disappearance and variable-length image sequences. We developed ARTSS using data from 970 patients, structured into four stages: I) Image pre-processing and re-orientation using ResNet50, II) Hand segmentation using UNet.3, III) Joint identification using YOLOv7, and IV) TSS prediction using models such as VGG16, VGG19, ResNet50, DenseNet201, EfficientNetB0, and Vision Transformer (ViT). We evaluated model performance with Intersection over Union (IoU), Mean Average Precision (MAP), mean absolute error (MAE), Root Mean Squared Error (RMSE), and Huber loss. The average TSS from two radiologists was used as the ground truth. Model training employed 3-fold cross-validation, with each fold consisting of 452 training and 227 validation samples, and external testing included 291 unseen subjects. Our joint identification model achieved 99% accuracy. The best-performing model, ViT, achieved a notably low Huber loss of 0.87 for TSS prediction. Our results demonstrate the potential of deep learning to automate RA scoring, which can significantly enhance clinical practice. Our approach addresses the challenge of joint disappearance and variable joint numbers, offers timesaving benefits, reduces inter- and intra-reader variability, improves radiologist accuracy, and aids rheumatologists in making more informed decisions.

Figures

Figures reproduced from arXiv: 2509.06854 by Hajar Moradmand, Lei Ren.

Figure 1
Figure 1. Figure 1: Overview of the workflow. The ARTSS (automated radiographic total Sharp scoring) framework consists of four [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of Age and Sharp Score over Gender. A) Histogram of age by gender with KED curves shows a peak of [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance Analysis and Sample Results of Joint Identification Using YOLOv7. A) The performance metrics of [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

discussion (0)

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Reference graph

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