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REVIEW 5 major objections 7 minor 2 cited by

ArthroPhase: A Novel Dataset and Method for Phase Recognition in Arthroscopic Video

T0 review · 5 major / 7 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A new 27-video ACL surgery dataset and a transformer model with a continuous progress index establish the first benchmark for phase recognition in arthroscopy.

desk verdict ACL27 is the real contribution—a first public arthroscopic phase-recognition dataset—and the model is a competent but incremental transformer variant; the SPI is normalized time, and the central accuracy claim needs label-reliability evidence before it anchors a benchmark. read the letter →

arxiv 2502.07431 v2 pith:CZKRLNDO submitted 2025-02-11 cs.CV

Ali Bahari Malayeri , Matthias Seibold , Nicola Cavalcanti , Jonas Hein , Sascha Jecklin , Lazaros Vlachopoulos , Sandro Fucentese , Sandro Hodel
show 1 more author
Philipp Furnstahl
This is my paper · ORCID
classification cs.CV
keywords surgicalphaserecognitionarthroscopyACLreconstructiontransformerProgressIndexspatio-temporalfeaturesACL27datasetvideoanalysis
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

The paper aims to show that surgical phase recognition works for arthroscopy, a domain with blurry frames, fluid occlusion, and narrow fields of view. To do this it introduces ACL27, a dataset of 27 ACL reconstruction videos labeled into five phases at one label per second, and trains a transformer-based model that combines ResNet-50 spatial features with temporal context and an auxiliary Surgical Progress Index (SPI). The model reaches 72.91% accuracy on ACL27, ahead of adapted laparoscopic methods, and 92.4% accuracy on Cholec80, matching recent state-of-the-art. The SPI predicts surgery progression with 10.6% error on ACL27 and 9.86% on Cholec80. If these results hold, arthroscopy becomes a measurable, trainable domain for automated surgical workflow analysis.

What carries the argument

The load-bearing mechanism is temporal-aware frame-wise feature extraction: a ResNet-50 turns each 240x240 frame into a 2048-dimensional vector, a two-head transformer layer refines these vectors using neighboring frames, and a Spatial Feature Encoder plus Temporal Context Encoder turn the sequence into spatio-temporal features for an 80-second transformer branch. Parallel to phase classification, the Surgical Progress Index (SPI) regresses t/T_k, the fraction of surgery elapsed, with missing phases repaired by average transition points from complete videos. The SPI gives the model global temporal context without processing the whole video, which is what lets a discrete phase classifier suppress noisy frame-level errors.

What would settle it

Have several independent surgeons label a subset of ACL27 frames and measure agreement on phase boundaries; if agreement is poor, or if a model retrained on SPI targets computed without the missing-phase imputation loses most of its advantage, the central benchmark claim is overturned. A simpler check: restrict evaluation to the twelve videos containing all five phases and see whether 72.91% accuracy holds.

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Extended reading notes

Core claim

The paper's central claim is that spatio-temporal features plus a continuous progress signal make arthroscopic phase recognition tractable despite the domain's visual noise. The evidence is the ACL27 benchmark and the ablations: with neither spatio-temporal features nor SPI the model scores 66.38% accuracy; adding either helps, and adding both reaches 76.71% under five-fold cross-validation. The authors also claim that the model is the first benchmark for arthroscopic phase recognition and that its SPI output, computed as the elapsed fraction t/T_k of the surgery and adjusted for missing phases, gives a clinically useful estimate of remaining procedure time.

Load-bearing premise

The ACL27 ground truth is one expert's per-second labeling with no second rater, and for videos with missing phases the SPI targets are filled in using average transition points from the twelve complete videos, so the benchmark numbers depend on those labels and that imputation being accurate.

Editorial extensions

If this is right

  • ACL27 gives the arthroscopy community a fixed 27-video benchmark with per-second phase labels, so future methods can be compared on the same data.
  • SPI predictions with roughly 10% error make automated remaining-duration estimation possible, which could help operating room scheduling and patient flow.
  • The ablation results imply that future arthroscopic phase models should include both temporal feature fusion and a continuous progress head.
  • The Cholec80 result (92.4% accuracy) suggests the same architecture transfers to laparoscopy without retraining for a new instrument set.

Reading between the lines

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

  • If a second expert labeled the same ACL27 videos, low inter-rater agreement on phase boundaries would put an upper bound on what any model can learn from these labels, so the 72.91% number should be read with that caveat.
  • Because SPI is derived from time, the model may learn to lean on elapsed time rather than visual content; a test that shuffles or jitters frame order would reveal how much of the accuracy is time-based.
  • The missing-phase imputation using average transition points could inflate SPI accuracy on videos that start or end mid-procedure; evaluating only the twelve complete videos would give a stricter estimate.
  • The same dataset could support boundary-level evaluation (time to phase transitions) and semi-automated labeling tools, both of which the paper names as future work but does not test.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 7 minor

Summary. The paper introduces ACL27, a new dataset of 27 arthroscopic anterior cruciate ligament reconstruction videos with five surgical phases labeled at one label per second, and proposes a transformer-based phase recognition model with temporal-aware frame-wise features and a continuous Surgical Progress Index (SPI). The authors report 72.91% accuracy, 72.86% precision, and 57.39% Jaccard Index on ACL27, and 92.4% accuracy on the Cholec80 benchmark. They also report SPI errors of 10.6% and 9.86% on the two datasets. The paper positions the model as the first benchmark for arthroscopic phase recognition and claims generalizability via the Cholec80 experiments.

Significance. If the ACL27 labels are reliable, the dataset fills a genuine gap: arthroscopic phase recognition is essentially unexplored compared with laparoscopy, and a public dataset with baselines would be a useful community resource. The Cholec80 comparison is a sensible external validity check and the reported accuracy is competitive with published methods. The central accuracy number is not circular, since the phase labels are expert annotations independent of the model. The SPI is a lightweight auxiliary supervision idea that is easy to adopt. However, the paper's load-bearing claims currently rest on unvalidated single-pass labels, an SPI target that is partly constructed from average phase statistics, and reimplemented baselines without release details. The dataset and code are promised but not yet available, so independent verification is not possible at this stage.

major comments (5)
  1. [Section 3.1, Table 3] The ACL27 benchmark is computed entirely against labels that are described only as created 'in close collaboration with clinical experts' at one label per second. The paper reports no number of annotators, no inter-rater reliability statistic, no adjudication rule, and no quality-control measure. Given the paper's own emphasis on blur, occlusion, and fluid artifacts, label noise is a real risk; the reported 72.91% accuracy and the roughly six-point margin over Trans_SVNet could partly reflect agreement with a noisy reference. Please add an inter-rater reliability study on a subset (e.g., Cohen's kappa or Krippendorff's alpha) and a description of how ambiguous frames were resolved.
  2. [Section 3.3, Eq. (2), Section 4.2] The SPI target for videos with missing phases is constructed using average transition points computed from the 12 complete ACL27 videos, and the reported SPI error of 10.6% is measured against this adjusted target. The SPI error is therefore not an independent validation of the model's ability to estimate true surgical progress: for missing-phase videos, the target already incorporates dataset-level phase statistics. Please report SPI error separately on complete videos only, and/or validate the imputation by hiding phases in complete videos and comparing the adjusted SPI against the true SPI. The error metric (apparently MAE) should also be stated explicitly.
  3. [Section 3.4, Section 4.3] The evaluation protocol for the 5-round cross-validation is ambiguous. The text says the datasets were randomly divided into five equal partitions, but for ACL27 it also says 18 videos were used for training and 9 for validation; 27 videos cannot be divided into five equal partitions of this size. This ambiguity affects the interpretation of the ablation results in Table 4, including the headline robust configuration accuracy of 76.71 ± 2.44. Please specify exactly how the folds were generated, whether the 18/9 split refers only to the non-cross-validation experiments, which random seeds were used, and how the reported mean and standard deviation were computed.
  4. [Section 4.2, Table 3] The comparative baselines (Trans_SVNet, MTRCNet, TeCNO, Opera) are author reimplementations, but the paper provides no hyperparameters, training schedules, or code for these baselines, and no indication of whether published checkpoints or official implementations were used. The central claim that the proposed model outperforms existing methods on ACL27 depends on these reimplementations being faithful. Please provide the full experimental configuration for each baseline and, ideally, release the code used for the comparisons. There is also an internal inconsistency: the text says MTRCNet achieved 58.75%, while Table 3 lists 60.68% for the Seq=10s row and Section 6 cites 60.68%.
  5. [Sections 5 and 6] The paper reports '72.91 ± 11.76%' for ACL27 accuracy, but Table 3 lists 72.91% as a non-cross-validation result with no standard deviation. It is unclear whether the ±11.76 is a per-video standard deviation, a standard deviation across non-CV runs, or something else. This is a load-bearing number in the abstract and conclusion, and the large spread deserves explicit explanation. Please state which split the 72.91% figure comes from and define the uncertainty interval in all places where it appears.
minor comments (7)
  1. [Section 4.3, Table 4] The text misassigns the ablation rows: it says 'when spatio-temporal features are added (second row)', but the second row is the SPI-only configuration, and the third row is the spatio-temporal-features-only configuration. Please correct the row references.
  2. [Figure 1] The phase label 'ACL Reko' in the figure appears to be a truncated version of 'ACL Reconstruction'; please use the full phase name for clarity.
  3. [Sections 5 and 6] Both Section 5 and Section 6 are titled 'Discussion', which is confusing. Consider merging them or giving distinct titles.
  4. [Section 1, Contributions] There is a typo in 'benchmark the proposed method with tate-of-the-art approaches'; it should read 'state-of-the-art'.
  5. [Section 3.3, Eqs. (1) and (2)] The notation is inconsistent: the SPI is initially defined for surgery k, but the adjusted formula uses k in the first term and then switches to 'video n' in the surrounding text. Please use a single video index throughout.
  6. [Section 3.3] The phrase 'we use only videos that contain all predefined surgical phases for baseline computation (12 videos in ACL27 and 65 in Cholec80)' should be clarified: are these the videos used to compute the average SPI transitions in Table 2, and are the remaining videos used only with the adjusted SPI target? The current wording is easy to misread as applying to the whole experimental benchmark.
  7. [Section 3.2, Figure 2] The architecture description says a transformer layer with 2 heads is used for temporal-aware feature refinement, but the number of layers, hidden dimensions, and the exact roles of the SFE and TCE are not specified. Please add a short architectural table or precise configuration so the method is reproducible.

Circularity Check

1 steps flagged · score 2.0 of 10

Phase-recognition benchmark is independent, but the SPI 'progress' validation is self-referential because its target is constructed from the paper's own phase statistics.

  1. fitted input called prediction [Section 3.3 (SPI adjusted formula) and Results 4.2]
    "For surgeries with missing phases, we establish an average progression percentage for each phase using the complete videos. ... SPI(t,k,i)adjusted = t/Tk + sum_{i in missing phases} avg_SPI(i) · I(phase i is missing) ... The SPI, which quantifies the progression of the surgery on a scale from 0 to 1, demonstrated an output error of 10.6%."

    The SPI ground truth for videos with missing phases is not an independent measurement of surgical progress: it is defined as normalized elapsed time plus avg_SPI(i), where avg_SPI(i) is computed from the phase statistics of the 12 complete videos. The reported 10.6% output error is therefore a fit-quality measure against a target the authors constructed, not evidence that the SPI represents externally validated progression. In the ablation study, the accuracy gain credited to SPI may partly reflect that this auxiliary target encodes average phase-transition statistics derived from the same phase labels, so the SPI contribution is not an independent confirmation. The main phase-recognition accuracy remains evaluated on expert labels, so this circularity is secondary.

full rationale

The central ACL27 phase-recognition claim is not circular: accuracy, precision, recall, and Jaccard Index are computed against expert-provided phase labels, and the Cholec80 comparison is an external benchmark with published state-of-the-art baselines. The paper contains no load-bearing self-citation chain: references to prior work are standard and not authored by the present group. The only partially circular element is the Surgical Progress Index (SPI): for videos with missing phases, the adjusted SPI target is built from average transition statistics computed from the complete videos, and the reported SPI error is measured against this same self-constructed target. This affects the secondary claim that the SPI is a reliable, clinically meaningful measure of progression and slightly weakens the ablation credit given to SPI, since the auxiliary target can carry phase-statistics information into the model. It does not reduce the primary phase-recognition benchmark or the Cholec80 result, so the overall circularity score is low.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of the ACL27 labels and on the SPI construction. The only fitted values are the average transition points used for missing-phase imputation and the hand-chosen loss weight. The five-phase taxonomy and the transfer from Cholec80 are domain assumptions. No new physical or architectural entities are introduced; SPI is a defined target, not an entity.

free parameters (2)
  • lambda (loss weight) = 0.5
    Hand-chosen balance between phase classification cross-entropy and SPI MAE in Section 3.4; not tuned or justified.
  • avg_SPI transition points for missing-phase imputation = ACL27: 0.073, 0.309, 0.534, 0.765, 1.000; Cholec80: 0.051, 0.452, 0.530, 0.847, 0.885, 0.964, 1.000
    Estimated from the 12 complete ACL videos and 65 complete Cholec80 videos, then used to construct SPI ground truth for videos with missing phases (Section 3.3, Table 2). These are data-derived constants that affect the SPI evaluation.
assumptions (4)
  • domain assumption The five phases Preparation, Diagnosis, Femoral Tunnel Creation, Tibial Tunnel Creation, and ACL Reconstruction form an exhaustive and clinically valid segmentation of ACL reconstruction.
    Section 3.1; labels are assigned by clinical experts, but no inter-rater reliability or validation protocol is reported.
  • domain assumption The inclusion criteria (standard ACL procedures without additional pathologies) yield a representative sample for a general arthroscopic phase-recognition benchmark.
    Section 3.1; excluding complex cases limits generalizability, and the paper acknowledges dataset size as a limitation.
  • ad hoc to paper Average transition points from complete videos can validly impute the Surgical Progress Index for videos with missing phases.
    Section 3.3; the adjusted SPI formula adds avg_SPI(i) for missing phases, a rule introduced by the authors without independent validation.
  • domain assumption Performance on Cholec80 (laparoscopic cholecystectomy) transfers as evidence that the model generalizes to arthroscopy.
    Section 4.4; the two domains have different visual characteristics, so Cholec80 is a proxy rather than direct validation.

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Cite this review

Pith. "Pith review of ArthroPhase: A Novel Dataset and Method for Phase Recognition in Arthroscopic Video." pith.science (2026). https://pith.science/paper/CZKRLNDO

@misc{pith2026250207431,
  author       = {Pith},
  title        = {Pith review of: ArthroPhase: A Novel Dataset and Method for Phase Recognition in Arthroscopic Video},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CZKRLNDO}},
  note         = {Machine review of arXiv:2502.07431}
}
read the original abstract

This study aims to advance surgical phase recognition in arthroscopic procedures, specifically Anterior Cruciate Ligament (ACL) reconstruction, by introducing the first arthroscopy dataset and developing a novel transformer-based model. We aim to establish a benchmark for arthroscopic surgical phase recognition by leveraging spatio-temporal features to address the specific challenges of arthroscopic videos including limited field of view, occlusions, and visual distortions. We developed the ACL27 dataset, comprising 27 videos of ACL surgeries, each labeled with surgical phases. Our model employs a transformer-based architecture, utilizing temporal-aware frame-wise feature extraction through a ResNet-50 and transformer layers. This approach integrates spatio-temporal features and introduces a Surgical Progress Index (SPI) to quantify surgery progression. The model's performance was evaluated using accuracy, precision, recall, and Jaccard Index on the ACL27 and Cholec80 datasets. The proposed model achieved an overall accuracy of 72.91% on the ACL27 dataset. On the Cholec80 dataset, the model achieved a comparable performance with the state-of-the-art methods with an accuracy of 92.4%. The SPI demonstrated an output error of 10.6% and 9.86% on ACL27 and Cholec80 datasets respectively, indicating reliable surgery progression estimation. This study introduces a significant advancement in surgical phase recognition for arthroscopy, providing a comprehensive dataset and a robust transformer-based model. The results validate the model's effectiveness and generalizability, highlighting its potential to improve surgical training, real-time assistance, and operational efficiency in orthopedic surgery. The publicly available dataset and code will facilitate future research and development in this critical field.

Figures

Figures reproduced from arXiv: 2502.07431 by the authors.

Figure 1
Figure 1. Top row: Clear images from each phase of ACL surgery, showcasing identifiable stages of the procedure. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Architecture of our spatio-temporal feature extractor showcasing the process from initial feature extraction [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Overview of the final model for surgical phase recognition. The architecture has a transformer branch which [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding

    cs.CV 2025-05 conditional novelty 7.0 of 10

    EgoExOR is a new multimodal, multi-perspective OR dataset with 84,553 annotated frames, plus a benchmark showing that fusing egocentric and exocentric signals improves surgical scene graph generation.

  2. CPKD: Clinical Prior Knowledge-Constrained Diffusion Models for Surgical Phase Recognition in Endoscopic Submucosal Dissection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A diffusion-based generative model with training-time masking and clinical logic constraints achieves state-of-the-art surgical phase recognition on ESD videos and a small gain on cholecystectomy videos.

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Reviewed August 8, 2026 · model on record in the stance chip above.