Pith. sign in

REVIEW 4 major objections 6 minor 19 references

Congenital Heart Disease recognition using Deep Learning/Transformer models

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Deep learning can recognise congenital heart disease from heart-sound spectrograms and chest X-rays, with 73.9% audio and 80.72% X-ray accuracy reported here.

desk verdict The headline accuracies are validation numbers with a missing test set; the benchmark is honest but not yet reliable. read the letter →

arxiv 2505.08242 v1 pith:BQLZBOHO submitted 2025-05-13 cs.CV

Aidar Amangeldi , Vladislav Yarovenko , Angsar Taigonyrov This is my paper · ORCID
classification cs.CV
keywords congenitalheartdiseasedeeplearningvisiontransformersoundclassificationchestX-raySTFTspectrogramlatefusionmedicalimagepreprocessing
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 tries to establish that deep learning can recognise congenital heart disease from two non-invasive inputs: heart-sound recordings converted into images and chest X-rays. On the ZCHSound heart-sound data it reports 73.9% accuracy with a ResNet trained on STFT spectrograms, and shows that late fusion by class F1 weights or a logistic-regression meta-ensemble improves that to 75.5% and 76.9%, respectively. On the DICOM chest X-ray data it reports 80.72% accuracy with ResNet-18 after Gaussian blurring, contrast enhancement, and augmentation, and it treats the application of modern vision transformers to this X-ray task as the novel contribution. The paper also reports that its preprocessing pipeline adds 6–7 percentage points of accuracy on the X-ray set, and that the audio results remain below the 93.4% reported by the original ZCHSound study.

What carries the argument

The mechanism on the audio side is the conversion of raw heart sounds into fixed-size 2D images, chiefly the short-time Fourier transform (STFT) spectrogram, which lets pretrained CNNs treat heartbeats as visual patterns; the paper tests mel-spectrogram and Gramian angular field alternatives and finds them weaker. On the image side, the mechanism is a fixed preprocessing pipeline, Gaussian blur with standard deviation 0.8 followed by histogram-equalisation contrast enhancement at factor 1.8, followed by small rotations, flips, brightness and contrast augmentation; the paper attributes a 6–7% accuracy gain to this pipeline. Finally, the late-fusion stage combines the audio model predictions by validation-accuracy weights, class-F1 weights, or a logistic-regression meta-ensemble, and the last two improve the base results.

What would settle it

Re-run both pipelines with repeated five-fold cross-validation in which all recordings or X-rays from one patient stay in the same fold; if the average accuracy falls substantially below the reported 73.9% audio and 80.72% X-ray figures, or if per-class sensitivity for the smaller classes drops far below the overall accuracy, the claim of usable CHD recognition would be falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that two complementary deep-learning pipelines can separate four congenital heart disease types, ASD, VSD, PDA and PFO, from normal controls, with 73.9% accuracy on heart sounds and 80.72% accuracy on chest X-rays. On the audio side, the claim is that the STFT representation is the strongest of the three tested 2D transforms, that ResNet-50v2 is the best single model for it, and that late fusion using class-F1 weights or a meta-ensemble beats the individual models. On the image side, the claim is that a preprocessing sequence of Gaussian blur, histogram-equalisation contrast enhancement, and light augmentation yields a 6–7 percentage point gain, and that ResNet-18, with 11.7M parameters, reaches the best accuracy and F1 while Swin-Tiny comes close and ViT-Tiny falls behind. A further claim is that applying state-of-the-art vision transformers to CHD chest X-rays is new here and that their competitive performance, despite higher parameter counts, makes model complexity an explicit consideration for deployment.

Load-bearing premise

The accuracy numbers stand or fall on the assumption that the train/validation/test data are split randomly, are stratified across the classes, and contain no overlapping patients between training and testing; the paper states only an 80/20 split for the X-ray data and gives no split details, stratification, or leakage check for the audio data.

Editorial extensions

If this is right

  • A simple 11.7M-parameter ResNet-18 with the described preprocessing is enough to reach 80.72% on the CHD-CXR X-ray data, so larger models are not automatically better for this four-class screening task.
  • Class-F1-weighted late fusion and a logistic-regression meta-ensemble each improve single-model audio accuracy, by 1.6 and 2.7 percentage points, suggesting fusion is worth keeping in the audio pipeline.
  • Among the tested audio representations, STFT clearly carries the classification: it reaches 73.9% with ResNet, while Mel and GAF stay between 51% and 62% depending on architecture, so future audio work should start from STFT or introduce new representations.
  • Swin-Tiny reaches 80.12% on X-rays, within 0.6 points of the best CNN, showing that hierarchical vision transformers are competitive for CHD detection even though ViT-Tiny is not.
  • If the reported gain of 6–7 points from preprocessing is real, then the same blur-and-contrast pipeline may transfer to other paediatric chest X-ray classification tasks where training data are limited.

Reading between the lines

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

  • A natural next step the paper leaves implicit is early fusion: instead of combining audio and X-ray predictions only at the score level, a single transformer could take the STFT spectrogram and a downsampled X-ray as two input tokens, which may capture cross-modal signs the late-fusion stage misses.
  • Because ZCHSound is class-imbalanced, macro-F1 and per-class sensitivity for ASD, PDA, and PFO are more informative than accuracy; the paper's reported late-fusion gains should be re-read against those metrics.
  • The paper itself notes it chose the blur and contrast settings without analysing the noise histograms, so the reported 6–7% gain is tied to those fixed settings rather than a general claim about preprocessing.
  • The audio claim that Mel and GAF underperform STFT could be tested cheaply by replacing the CNN backbones with a single vision transformer and seeing whether the representation ranking changes.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This manuscript investigates deep learning and transformer models for congenital heart disease (CHD) classification using two public datasets: the ZCHSound heart-sound recordings and the DICOM/CHD-CXR chest X-ray images. For audio, the authors compare STFT, Mel-spectrogram, and Gramian Angular Field representations with ResNet-50V2, EfficientNetB0, and InceptionV3, and then combine base-model predictions via three late-fusion strategies (validation-accuracy weights, class-F1 weights, and a logistic-regression meta-ensemble). For X-rays, they compare ResNet-18, Inception-v3, ViT-Tiny, and Swin-Tiny after Gaussian blurring, contrast enhancement, and augmentation. The paper reports 73.9% accuracy on ZCHSound and 80.72% accuracy on DICOM, and claims that applying state-of-the-art vision transformers to the X-ray task is a novelty.

Significance. If the reported numbers were reliable, this would be a modest empirical contribution: it provides a head-to-head comparison of standard CNNs and transformers on two CHD datasets, shows that late fusion can provide small gains over the best audio base model, and highlights that a lightweight ResNet-18 outperforms heavier transformer models on the small X-ray dataset. The paper's emphasis on model complexity and deployability is a useful framing, and the authors are transparent about several limitations (e.g., choosing preprocessing parameters by assumption rather than tuning). However, the lack of a held-out test set for the X-ray experiments, the absence of any split description for the audio experiments, and the inconsistent reporting of validation vs. test accuracy mean that the central accuracy claims are not currently supported as generalization estimates. The paper also provides no code or reproducibility statement, which further limits the value of the empirical comparisons.

major comments (4)
  1. [§IV.B, Table Ia, Figure 6] The DICOM accuracy headline (80.72%) is a validation accuracy, not a test accuracy. Section IV.B states that models were trained with an '80/20 train/validation split' and that early stopping 'monitored the validation accuracy to halt training,' and Table Ia labels the reported column 'Val acc.' Yet Figure 6 presents the same numbers as 'Test Accuracy Results.' Because the same 20% validation split is used for early stopping and for selecting ResNet-18 as the best model, the reported figure is an optimistically biased estimate with no independent test set. The authors must either introduce a proper three-way split (or nested cross-validation) and report test accuracy with confidence intervals, or explicitly re-label all figures/tables as validation accuracy and refrain from generalizing.
  2. [§V.A, Table Ib, Figure 5] No train/validation/test split is described for the ZCHSound audio dataset. The text repeatedly refers to 'testing accuracy' and Figure 5 is titled 'Test Accuracy Results,' but Section IV.A never specifies how the 941 recordings were partitioned, whether stratification by class was performed, or whether any patient-level separation was enforced. Without this information, the 73.9% base-model accuracy and the 75.5%/76.9% late-fusion accuracies cannot be reproduced or assessed for leakage/selection bias. The authors should specify the split (ideally with repeated stratified k-fold or bootstrapped confidence intervals) and report per-class performance.
  3. [Abstract vs. §V.A] The abstract reports 73.9% accuracy on ZCHSound, which corresponds to the STFT-ResNet base model in Table Ib, while the paper's own best late-fusion result (meta-ensemble, Figure 5) is 76.9%. This inconsistency in the headline number is confusing: if the abstract is meant to report the best achieved result, it should cite the meta-ensemble accuracy; if it intentionally reports the base model, the choice should be justified. As written, the abstract understates the paper's main audio result and does not reflect the fusion contribution described in the conclusions.
  4. [§IV.B, §V.B] The claimed 6-7% accuracy gain from Gaussian blurring and contrast enhancement is not substantiated by any controlled comparison. Section IV.B states that the authors 'gained around 2-3% increase in accuracy' from GBF and that combined preprocessing 'was 6-7%,' but no ablation table or figure is provided showing model performance without these steps. Because this quantitative claim is reported as a finding in the conclusion, it needs a supporting experiment (e.g., training the same architectures with and without each preprocessing step).
minor comments (6)
  1. [§IV.B heading] The heading 'Classifiication models for DICOM CHD-CXR images dataset' contains a typo ('Classifiication') and should read 'Classification models for the DICOM CHD-CXR image dataset.'
  2. [Figures 5 and 6] The bar charts in Figures 5 and 6 do not show numerical value labels on the bars, making it difficult to read exact accuracies from the figures; adding value labels would improve clarity. The 'Original Paper' bar in Figure 5 is also not identified in the caption.
  3. [Table I] The composite Table I places the caption 'TABLE I: Model results' after Table Ib; the (a) and (b) sub-tables are referenced in the text but the caption does not explain the split. Please use separate captions or add a parenthetical explanation.
  4. [Introduction, Reference [1]] Reference [1] is cited for the claim that CHD accounts for 33% of major birth defects, but [1] is a study on long-term outcomes after myocardial infarction in CHD patients, which does not support that statistic; please replace it with an appropriate epidemiology reference.
  5. [§V.B] The sentence 'ASD is has only 194 examples' contains a grammatical error, and the overfitting statement 'there was a little of overfit (around 20% difference)' is not quantified with train/validation accuracy values; please provide specific numbers or a learning curve.
  6. [§V.B novelty claim] The claim that 'there is no papers that applied SOTA ViT models on this problem' is contradicted by the manuscript's own citation of Jiang et al. [11] (FlashViT), a vision transformer for CHD detection; this novelty claim should be removed or qualified to apply only to the specific architectures/dataset combination used here.

Circularity Check

1 steps flagged · score 6.0 of 10

The DICOM 80.72% accuracy is labeled both as validation accuracy and as test accuracy, so the central image claim reduces by construction to the model-selection criterion.

  1. fitted input called prediction [Section IV.B (Training loop), Section V.B, Table Ia, and Figure 6.]
    "The models were trained using an 80/20 train/validation split with batch sizes of 32 for both loaders. Training proceeded for up to 100 epochs. Early stopping with a patience of 7-10 epochs monitored the validation accuracy to halt training... Table Ia: 'Model Val acc. F1 score Optim. #param (M)' ... ResNet18 ... 80.72% ... Figure 6: 'Test Accuracy Results of Models on DICOM dataset.'"

    The 80.72% accuracy highlighted in the abstract and Section V.B is labeled 'Val acc.' in Table Ia, and the same numeric value is presented as 'Test Accuracy' in Figure 6. Because training used only an 80/20 train/validation split with early stopping and model selection on validation accuracy, and no separate test split is described for DICOM, the 'test accuracy' is identical by construction to the validation score that determined the checkpoint and the winning model. Therefore the central DICOM claim is not an independent generalization prediction; it is the fitted selection criterion reported under a different name.

full rationale

The paper's audio chain is not circular: base models are compared on test accuracy, the late-fusion weighting rules are fit on validation predictions and then re-evaluated on the test split, and the reported 73.9% result is an independent empirical benchmark against the ZCHSound dataset; no equation defines the output in terms of its inputs. The only reduction-by-construction I can exhibit concerns the DICOM image result. Section IV.B describes an 80/20 train/validation split and early stopping that 'monitored the validation accuracy,' and Table Ia labels the same column 'Val acc.' with ResNet-18 at 80.72%; Section V.B's Figure 6 then labels the identical numbers 'Test Accuracy Results.' Since no separate held-out test set is described for DICOM, the 80.72% 'test accuracy' is by construction the validation score used for early stopping and model selection, so the central DICOM claim is statistically forced rather than an estimate on unseen data. The novelty claim about ViT models is inconsistent with the cited FlashViT paper [11], but that inconsistency is not circular. There is no load-bearing self-citation chain, so the overall circularity is partial and confined to the DICOM accuracy claim.

Assumptions & free parameters 7 free parameters · 3 assumptions · 0 invented entities

No new entities, forces, or conserved quantities are introduced; the paper only applies existing model architectures and datasets. The free parameters are hand-chosen preprocessing and training hyperparameters plus validation-derived fusion weights.

free parameters (7)
  • Gaussian blur sigma = 0.8
    Chosen by hand in Section IV.B; no tuning analysis is provided.
  • Contrast factor = 1.8
    Chosen by hand in Section IV.B; no tuning analysis is provided.
  • Augmentation rotation range = ±5 degrees
    Set in Section IV.B; no ablation is performed.
  • Early stopping patience = 7-10 epochs
    Set in Section IV.B for DICOM; not specified for audio.
  • Batch size = 32
    Used in Section IV.B; no justification or variation is reported.
  • Class fusion weights = per-class validation F1 scores
    Used in late fusion (Section V.A); these are derived from validation data and are fitted parameters for the fusion step.
  • Meta-ensemble logistic regression weights = trained on validation predictions
    The logistic regression meta-model is fit to validation outputs (Section V.A).
assumptions (3)
  • domain assumption The ground-truth labels in ZCHSound and DICOM are accurate.
    The paper relies entirely on the correctness of these labels; no independent verification is provided.
  • domain assumption Train/validation/test splits are free of patient overlap and are representative of the population.
    For DICOM an 80/20 split is stated but no stratification or leakage check is described; for ZCHSound no split details are given (Section IV.B, V).
  • domain assumption Augmentation is applied only to the training set, and no data leakage occurs during preprocessing.
    Standard practice, but the paper does not verify that augmented images do not appear in the validation/test set (Section IV.B).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Congenital Heart Disease recognition using Deep Learning/Transformer models." pith.science (2026). https://pith.science/paper/BQLZBOHO

@misc{pith2026250508242,
  author       = {Pith},
  title        = {Pith review of: Congenital Heart Disease recognition using Deep Learning/Transformer models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BQLZBOHO}},
  note         = {Machine review of arXiv:2505.08242}
}
read the original abstract

Congenital Heart Disease (CHD) remains a leading cause of infant morbidity and mortality, yet non-invasive screening methods often yield false negatives. Deep learning models, with their ability to automatically extract features, can assist doctors in detecting CHD more effectively. In this work, we investigate the use of dual-modality (sound and image) deep learning methods for CHD diagnosis. We achieve 73.9% accuracy on the ZCHSound dataset and 80.72% accuracy on the DICOM Chest X-ray dataset.

Figures

Figures reproduced from arXiv: 2505.08242 by the authors.

Figure 2
Figure 2. STFT (a), Mel (b), and GAF (c) Transformations of [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 1
Figure 1. DICOM: ASD, Normal, PDA and VSD types of CHD. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. An Overview of the Audio Processing Methodology. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: An Overview of the Image Processing Methodology. [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]
Figure 5
Figure 5. Figure 5: Test Accuracy Results of Selected Base Model and [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Test Accuracy Results of Models on DICOM dataset. [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

  1. [11]

    Classification of heart sound signal using curve fitting and fractal dimension,

    M. Hamidi, H. Ghassemian, and M. Imani, “Classification of heart sound signal using curve fitting and fractal dimension,” Biomedical Signal Processing and Control, vol. 39, p. 351–359, 01 2018

    work page 2018

  2. [1]

    The heart sound dataset consists of 941 participants and 941 audio recordings, each approximately 20 seconds long, totaling over 5 hours in duration

    ZCHSound: ZCHSound was collected at the Children’s Hospital of Zhejiang University where the auscultations were gathered utilizing a stethoscope with an 8000 Hz frequency range [3]. The heart sound dataset consists of 941 participants and 941 audio recordings, each approximately 20 seconds long, totaling over 5 hours in duration. This dataset includes 473...

    work page

  3. [2]

    Originally the storage format of the files was DICOM

    DICOM: DICOM [10] is chest x-ray dataset images col- lected from Qingdao Women and Children Hospital between 2021 and 2022. Originally the storage format of the files was DICOM. As it contains sensitive patient information such as age, name, and execution time, the format of the x-ray files was converted to JPEG by the de-identification program to ensure ...

    work page arXiv 2021

  4. [3]

    Long-term outcomes after myocardial infarction in middle-aged and older patients with congenital heart disease—a nationwide study,

    M. Fedchenko, Z. Mandalenakis, K. W. Giang, A. Rosengren, P. Eriksson, and M. Dellborg, “Long-term outcomes after myocardial infarction in middle-aged and older patients with congenital heart disease—a nationwide study,” European Heart Journal , vol. 42, no. 26, pp. 2577–2586, 11 2020. [Online]. Available: https: //doi.org/10.1093/eurheartj/ehaa874

    work page doi:10.1093/eurheartj/ehaa874 2020

  5. [4]

    Diagnostic value of fetal echocardiography for congenital heart disease: A systematic review and meta-analysis,

    Y .-F. Zhang, X.-L. Zeng, E.-F. Zhao, and H.-W. Lu, “Diagnostic value of fetal echocardiography for congenital heart disease: A systematic review and meta-analysis,” Medicine, vol. 94, no. 42, p. e1759, October 2015

    work page 2015

  6. [5]

    The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights

    K. Khan, F. Ullah, I. Syed, and I. Ullah, “The role of machine learning in congenital heart disease diagnosis: Datasets, algorithms, and insights,” 2025. [Online]. Available: https://arxiv.org/abs/2501.04493

    work page Pith review arXiv 2025

  7. [6]

    Detection and diagnosis of congenital heart disease from chest x-rays with deep learning models,

    S. Sharifi and A. Donyadadi, “Detection and diagnosis of congenital heart disease from chest x-rays with deep learning models,”International Journal of Applied Data Science in Engineering and Health , vol. 1, no. 1, pp. 1–9, 2025

    work page 2025

  8. [7]

    Zchsound: Open-source zju paediatric heart sound database with congenital heart disease,

    W. Jia, Y . Wang, R. Chen, J. Ye, D. Li, F. Yin, J. Yu, J. Chen, Q. Shu, and W. Xu, “Zchsound: Open-source zju paediatric heart sound database with congenital heart disease,” IEEE Transactions on Biomedical Engineering, vol. 71, no. 8, pp. 2278–2286, 2024

    work page 2024

Show all 19 references
  1. [8]

    Assisting Heart Valve Diseases Diagnosis via Transformer-Based Classification of Heart Sound Signals,

    D. Yang, Y . Lin, J. Wei, X. Lin, X. Zhao, Y . Yao, T. Tao, B. Liang, and S.-G. Lu, “Assisting Heart Valve Diseases Diagnosis via Transformer-Based Classification of Heart Sound Signals,” Electronics, vol. 12, no. 10, p. 2221, 2023. [Online]. Available: https://doi.org/10.3390...

    2023 doi

  2. [9]

    Heart sounds classification with a fuzzy neural network method with structure learning,

    L. Jia, D. Song, L. Tao, and Y . lu, “Heart sounds classification with a fuzzy neural network method with structure learning,” 07 2012, pp. 130–140

    2012

  3. [10]

    Heart sound classification based on scaled spectrogram and partial least squares regression,

    W. Zhang, J. Han, and S. Deng, “Heart sound classification based on scaled spectrogram and partial least squares regression,” Biomedical Signal Processing and Control , vol. 32, pp. 20–28, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S1746809416301616

    2017

  4. [12]

    CHD- CXR: A De-identified Publicly Available Dataset of Chest X- ray for Congenital Heart Disease,

    L. Zhixin, L. Gang, J. Zhixian, W. Sibao, and P. Silin, “CHD- CXR: A De-identified Publicly Available Dataset of Chest X- ray for Congenital Heart Disease,” Frontiers in Cardiovascular Medicine, vol. 11, p. 1351965, 2024. [Online]. Available: https: //doi.org/10.3389/fcvm.2024.1351965

    2024

  5. [13]

    Jiang, J

    L. Jiang, J. Cheng, J. Chen, M. Gu, M. Zhu, P. Han, K. Li, and Z. Yang, FlashViT: A Flash Vision Transformer with Large-Scale Token Merging for Congenital Heart Disease Detection , 12 2023, pp. 136–148

    2023

  6. [14]

    Genetic contribution to congenital heart disease (chd),

    N. Shabana, S. U. Shahid, and U. Irfan, “Genetic contribution to congenital heart disease (chd),” Pediatric Cardiology, vol. 41, pp. 12–23, 2020

    2020

  7. [15]

    Theory of edge detection,

    D. Marr and E. Hildreth, “Theory of edge detection,” Proceedings of the Royal Society of London. Series B. Biological Sciences , vol. 207, no. 1167, pp. 187–217, 1980

    1980

  8. [16]

    Gaussian blurring technique for detecting and classifying acute lymphoblastic leukemia cancer cells from microscopic biopsy images,

    T. G. Devi, N. Patil, S. Rai, and C. S. Philipose, “Gaussian blurring technique for detecting and classifying acute lymphoblastic leukemia cancer cells from microscopic biopsy images,” Life, vol. 13, no. 2, p. 348, 2023

    2023

  9. [17]

    Can ai help in screening viral and covid-19 pneumonia?

    M. E. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, Z. Mahbub, M. M. Islam, M. S. I. Khan, A. Iqbal, N. Emadi, M. B. I. Reaz et al., “Can ai help in screening viral and covid-19 pneumonia?” in IEEE Access, vol. 9. IEEE, 2021, pp. 33 630–33 652

    2021

  10. [18]

    Automated abnormality classification of chest radiographs using deep convolutional neural networks,

    Y .-X. Tang, Y .-H. Tang, Y . Peng, K. Yan, J. Xiao, and R. M. Summers, “Automated abnormality classification of chest radiographs using deep convolutional neural networks,” NPJ Digital Medicine , vol. 3, no. 1, p. 70, 2020

    2020

  11. [19]

    Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization,

    F. Pasa, V . Golkov, F. Pfeiffer, D. Cremers, and D. Pfeiffer, “Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization,” Scientific Reports, vol. 11, no. 1, p. 4885, 2021

    2021

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.