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arxiv: 2607.00745 · v1 · pith:27QGRHU3new · submitted 2026-07-01 · 💻 cs.CV

Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation

Le Ou , Xiliang Zhu , Huanwen Liang , Wenxiong Pan , Yuhao Huang , Yuxiang Deng , Xuan Sheng , Hong Yin
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Juhua Xiao Xin Zhou Dong Ni
This is my paper

Pith reviewed 2026-07-02 14:29 UTC · model grok-4.3

classification 💻 cs.CV
keywords fetal birth weight estimationblind-sweep ultrasoundkey frame selectionvision-language foundation modelultrasound video analysisbirth weight regressionanatomy-guided selection
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The pith

A vision-language foundation model selects key anatomy frames from blind-sweep ultrasound videos to estimate fetal birth weight at 161-gram mean absolute error.

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

The paper develops a method to estimate fetal birth weight from blind-sweep ultrasound videos acquired without standard imaging planes or expert operator guidance. It introduces an Anatomy-Guided Frame Selection module that employs a vision-language foundation model to identify relevant anatomy frames in these unconstrained sweeps, together with a Redundancy-Aware Feature Compression module to handle video redundancy. The approach is evaluated on prospectively collected blind-sweep videos from 839 patients obtained within 48 hours before delivery, using post-delivery weight as ground truth. Reported performance reaches a mean absolute error of 161.3 grams, with 90.23 percent of cases within 10 percent absolute percentage error and all cases within 15 percent, exceeding results from the Hadlock formula and other compared methods.

Core claim

The authors establish that a foundation model-driven key anatomy frame selection framework enables accurate fetal birth weight regression directly from blind-sweep ultrasound videos, achieving a mean absolute error of 161.3 g on 839 patients with 90.23 percent of estimates falling within 10 percent absolute percentage error.

What carries the argument

Anatomy-Guided Frame Selection module equipped with a vision-language foundation model, which identifies key anatomy frames in unconstrained sweeps, paired with Redundancy-Aware Feature Compression to preserve task-relevant information.

If this is right

  • Enables fetal birth weight estimation from operator-independent blind-sweep videos without requiring standard ultrasound planes.
  • Outperforms the typical Hadlock estimation method and other strong competitors on the tested dataset.
  • Handles temporal redundancy in ultrasound videos while retaining information needed for accurate regression.
  • Provides the first reported results for birth weight estimation using blind-sweep videos with post-delivery ground truth.
  • Supports assessment in low-resource settings by reducing reliance on highly trained operators.

Where Pith is reading between the lines

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

  • The same frame-selection strategy could be tested on other fetal measurements such as head circumference or abdominal diameter from similar video sweeps.
  • Further gains may appear if the underlying vision-language model is updated with additional ultrasound-specific training data.
  • Deployment in clinics without plane-standardization protocols would require checking whether the reported error rates hold across different ultrasound machines.

Load-bearing premise

The vision-language foundation model can reliably identify key anatomy frames in unconstrained blind sweeps without plane constraints.

What would settle it

Direct comparison of automatically selected frames against frames chosen by expert sonographers on the same blind-sweep videos, followed by measurement of any resulting increase in birth-weight estimation error.

Figures

Figures reproduced from arXiv: 2607.00745 by Dong Ni, Hong Yin, Huanwen Liang, Juhua Xiao, Le Ou, Wenxiong Pan, Xiliang Zhu, Xin Zhou, Xuan Sheng, Yuhao Huang, Yuxiang Deng.

Figure 1
Figure 1. Figure 1: The overview of our proposed framework. dependency without requiring high-quality input data, providing reliable FBW estimation in resource-constrained settings. 2 Methodology Blind-sweep US videos exhibit variable temporal lengths, rapid viewpoint tran￾sitions, sparse standard anatomical planes, and substantial acquisition noise. Directly regressing FBW from all frames introduces significant redundancy an… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of frame selection for 3 representative cases using the AFS and RAFC modules. For each case, the line plot shows the selection score over the frame sequence (x-axis: frame index, y-axis: selection score). The top images display the video frames corresponding to the highest (red boxes) and lowest (green boxes) scores selected by each method. quality images are discarded. These results demonstr… view at source ↗
read the original abstract

Accurate fetal birth weight (FBW) estimation shortly before delivery is clinically valuable yet challenging due to its reliance on operator expertise, particularly in low-resource settings. To reduce this reliance, we study near-term birth-weight regression from blind-sweep ultrasound (US) videos acquired within 48 hours prior to delivery, with post-delivery weighing as ground truth. Accordingly, we propose a foundation model-driven key anatomy frame selection framework that enables accurate FBW regression despite the absence of plane constraints in blind sweeps. Our highlights are as follows: (1) We believe this is the first work to estimate FBW using blind-sweep US videos, enabling operator-independent assessment. (2) An Anatomy-Guided Frame Selection module equipped with a vision-language foundation model is proposed for keyframe collection in unconstrained sweeps. (3) A Redundancy-Aware Feature Compression module is designed to compress frame features while preserving task-relevant information, alleviating temporal redundancy. Extensively validated on prospectively collected data from 839 patients, our method achieves an MAE of 161.3 g, with 90.23% and 100% of cases falling within 10% and 15% absolute percentage error, outperforming typical Hadlock estimation and strong competitors. Codes are available at https://github.com/ouleoule/BlindSweep-EBW.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript claims to introduce the first method for fetal birth weight estimation from unconstrained blind-sweep ultrasound videos acquired within 48 hours of delivery. It proposes an Anatomy-Guided Frame Selection module that uses a vision-language foundation model to identify key anatomy frames, combined with a Redundancy-Aware Feature Compression module, and reports an MAE of 161.3 g (with 90.23% of cases within 10% and 100% within 15% absolute percentage error) on a prospective cohort of 839 patients, outperforming Hadlock formulas and other competitors. Code is released at the provided GitHub link.

Significance. If the central performance numbers hold under proper validation, the work would be significant for enabling operator-independent birth-weight assessment in low-resource settings. The public code release is a clear strength that supports reproducibility.

major comments (3)
  1. [Abstract] Abstract: the central claim that the Anatomy-Guided Frame Selection module enables the reported MAE of 161.3 g rests on the VLM reliably identifying task-relevant frames, yet the text provides no quantitative frame-selection metrics (e.g., agreement with expert plane annotations), no ablation removing the VLM, and no description of the specific foundation model or any ultrasound-domain adaptation.
  2. [Abstract] Abstract: performance is reported on 839 patients without any information on train/test splits, cross-validation procedure, confidence intervals, or statistical comparison to baselines, so it is impossible to determine whether the improvement over Hadlock is robust.
  3. [Abstract] Abstract: the Redundancy-Aware Feature Compression module is presented as alleviating temporal redundancy, but no ablation isolating its contribution versus the frame-selection module is described, leaving the source of the performance gain unclear.
minor comments (1)
  1. [Abstract] Abstract: the statement 'we believe this is the first work' should be moved to the introduction and supported by a literature review rather than asserted in the abstract.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point-by-point below and commit to revisions that directly respond to the concerns.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the Anatomy-Guided Frame Selection module enables the reported MAE of 161.3 g rests on the VLM reliably identifying task-relevant frames, yet the text provides no quantitative frame-selection metrics (e.g., agreement with expert plane annotations), no ablation removing the VLM, and no description of the specific foundation model or any ultrasound-domain adaptation.

    Authors: We agree the abstract omits these elements. The revised manuscript will name the specific vision-language foundation model, describe any ultrasound-domain adaptation steps, report quantitative frame-selection metrics (e.g., agreement with expert plane annotations), and include an ablation that removes the VLM component while keeping the rest of the pipeline fixed. revision: yes

  2. Referee: [Abstract] Abstract: performance is reported on 839 patients without any information on train/test splits, cross-validation procedure, confidence intervals, or statistical comparison to baselines, so it is impossible to determine whether the improvement over Hadlock is robust.

    Authors: We acknowledge the abstract lacks these details. The full paper describes the prospective cohort and evaluation in the Methods. In revision we will add a concise statement of the train/test split, any cross-validation used, confidence intervals around the MAE, and statistical comparisons (e.g., paired tests) versus Hadlock and other baselines. revision: yes

  3. Referee: [Abstract] Abstract: the Redundancy-Aware Feature Compression module is presented as alleviating temporal redundancy, but no ablation isolating its contribution versus the frame-selection module is described, leaving the source of the performance gain unclear.

    Authors: We agree an isolating ablation is required. The revised manuscript will add an ablation that disables only the Redundancy-Aware Feature Compression module (keeping frame selection unchanged) and reports the resulting change in MAE and percentage-error metrics to quantify its specific contribution. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical validation on external dataset

full rationale

The paper reports an empirical method for fetal birth weight regression from blind-sweep ultrasound videos, with performance (MAE 161.3 g on 839 patients) presented as measured outcomes on prospectively collected data with post-delivery ground truth. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains are described that would make results tautological by construction. The architecture (Anatomy-Guided Frame Selection + Redundancy-Aware Compression) is validated externally rather than reducing to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no explicit free parameters, axioms, or invented entities are described; the framework relies on an off-the-shelf vision-language foundation model whose internal assumptions are not audited here.

pith-pipeline@v0.9.1-grok · 5800 in / 1095 out tokens · 21405 ms · 2026-07-02T14:29:35.880058+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

21 extracted references

  1. [1]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Akumu, T., Elbatel, M., Campello, V.M., Osuala, R., Martin-Isla, C., Valenzuela, I., Li, X., Khanal, B., Lekadir, K.: Adaptive frame selection for gestational age es- timation from blind sweep fetal ultrasound videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 3–12. Springer (2025)

    2025

  2. [2]

    Com- munications medicine2(1), 128 (2022)

    Gomes, R.G., Vwalika, B., Lee, C., Willis, A., Sieniek, M., Price, J.T., Chen, C., Kasaro, M.P., Taylor, J.A., Stringer, E.M., et al.: A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment. Com- munications medicine2(1), 128 (2022)

    2022

  3. [3]

    American journal of obstetrics and gynecology151(3), 333–337 (1985)

    Hadlock, F.P., Harrist, R., Sharman, R.S., Deter, R.L., Park, S.K.: Estimation of fetal weight with the use of head, body, and femur measurements—a prospective study. American journal of obstetrics and gynecology151(3), 333–337 (1985)

    1985

  4. [4]

    In: International Conference on Medical Image Com- puting and Computer-Assisted Intervention

    Huang, Y., Xu, Y., Dou, H., Deng, J., Yang, X., Zheng, H., Ni, D.: Uncertainty- aware diffusion and reinforcement learning for joint plane localization and anomaly diagnosis in 3d ultrasound. In: International Conference on Medical Image Com- puting and Computer-Assisted Intervention. pp. 650–660. Springer (2025)

    2025

  5. [5]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Huang, Y., Yang, X., Huang, X., Liang, J., Zhou, X., Chen, C., Dou, H., Hu, X., Cao, Y., Ni, D.: Online reflective learning for robust medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 652–662. Springer (2022)

    2022

  6. [6]

    Medical image analysis 96, 103202 (2024)

    Jiao,J.,Zhou,J.,Li,X.,Xia,M.,Huang,Y.,Huang,L.,Wang,N.,Zhang,X.,Zhou, S., Wang, Y., et al.: Usfm: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Medical image analysis 96, 103202 (2024)

    2024

  7. [7]

    npj Digital Medicine (2026)

    Maani, F., Saeed, N., Saleem, T.J., Farooq, Z., Alasmawi, H., Diehl, W., Moham- mad, A., Waring, G., Valappil, S., Bricker, L., et al.: Fetalclip: A visual-language foundation model for fetal ultrasound image analysis. npj Digital Medicine (2026)

    2026

  8. [8]

    In: NeurIPS ML4H Workshop: Vancouver, BC, Canada

    Ouyang, D., He, B., Ghorbani, A., Lungren, M.P., Ashley, E.A., Liang, D.H., Zou, J.Y.: Echonet-dynamic: a large new cardiac motion video data resource for medical machine learning. In: NeurIPS ML4H Workshop: Vancouver, BC, Canada. vol. 5, p. 2 (2019) 10 L. Ou et al

    2019

  9. [9]

    American Journal of Obstetrics & Gynecology MFM7(4) (2025)

    Płotka, S., Pustelnik, K., Szenejko, P., Żebrowska, K., Rzucidło-Szymańska, I., Szymecka-Samaha,N.,Łęgowik,T.,Kosińska-Kaczyńska,K.,Korzeniowski,P.,Bil- iński, P., et al.: Direct estimation of fetal biometry measurements from ultrasound video scans through deep learning. American Journal of Obstetrics & Gynecology MFM7(4) (2025)

    2025

  10. [10]

    American journal of obstetrics & gynecology MFM5(12), 101182 (2023)

    Płotka, S.S., Grzeszczyk, M.K., Szenejko, P.I., Żebrowska, K., Szymecka-Samaha, N.A., Łęgowik, T., Lipa, M.A., Kosińska-Kaczyńska, K., Brawura-Biskupski- Samaha, R., Išgum, I., et al.: Deep learning for estimation of fetal weight through- out the pregnancy from fetal abdominal ultrasound. American journal of obstetrics & gynecology MFM5(12), 101182 (2023)

    2023

  11. [11]

    NEJM evidence1(5), EVIDoa2100058 (2022)

    Pokaprakarn, T., Prieto, J.C., Price, J.T., Kasaro, M.P., Sindano, N., Shah, H.R., Peterson,M.,Akapelwa,M.M.,Kapilya,F.M.,Sebastião,Y.V.,etal.:Aiestimation of gestational age from blind ultrasound sweeps in low-resource settings. NEJM evidence1(5), EVIDoa2100058 (2022)

    2022

  12. [12]

    Ultrasound in Obstetrics & Gynecology62(2), 185–194 (2023)

    Ramirez Zegarra, R., Ghi, T.: Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound in Obstetrics & Gynecology62(2), 185–194 (2023)

    2023

  13. [13]

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

    Rasheed, H., Khattak, M.U., Maaz, M., Khan, S., Khan, F.S.: Fine-tuned clip models are efficient video learners. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6545–6554 (2023)

    2023

  14. [14]

    Ultrasound in obstetrics & gynecology53(6), 715–723 (2019)

    Salomon, L., Alfirevic, Z., Da Silva Costa, F., Deter, R., Figueras, F., Ghi, T.a., Glanc, P., Khalil, A., Lee, W., Napolitano, R., et al.: Isuog practice guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound in obstetrics & gynecology53(6), 715–723 (2019)

    2019

  15. [15]

    Ultrasound in Obstetrics & Gynecology49(4), 478–486 (2017)

    Stirnemann, J., Villar, J., Salomon, L., Ohuma, E., Ruyan, P., Altman, D., Nos- ten, F., Craik, R., Munim, S., Cheikh Ismail, L., et al.: International estimated fetal weight standards of the intergrowth-21st project. Ultrasound in Obstetrics & Gynecology49(4), 478–486 (2017)

    2017

  16. [16]

    Jama332(8), 649–657 (2024)

    Stringer, J.S., Pokaprakarn, T., Prieto, J.C., Vwalika, B., Chari, S.V., Sindano, N., Freeman, B.L., Sikapande, B., Davis, N.M., Sebastião, Y.V., et al.: Diagnostic accuracy of an integrated ai tool to estimate gestational age from blind ultrasound sweeps. Jama332(8), 649–657 (2024)

    2024

  17. [17]

    NPJ Digital Medicine8(1), 22 (2025)

    Venturini, L., Budd, S., Farruggia, A., Wright, R., Matthew, J., Day, T.G., Kainz, B., Razavi, R., Hajnal, J.V.: Whole examination ai estimation of fetal biometrics from 20-week ultrasound scans. NPJ Digital Medicine8(1), 22 (2025)

    2025

  18. [18]

    International Journal of Gynecology & Obstetrics 165(3), 1013–1021 (2024)

    Viswanathan, A.V., Pokaprakarn, T., Kasaro, M.P., Shah, H.R., Prieto, J.C., Ben- abdelkader, C., Sebastião, Y.V., Sindano, N., Stringer, E., Stringer, J.S.: Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control. International Journal of Gynecology & Obstetrics 165(3), 1013–1021 (2024)

    2024

  19. [19]

    Wang, J., Ni, Q., Yu, H., Yao, R., Ying, J., Zhang, B., Yang, X., Peng, J., Chen, J., Yu, J., et al.: Accurate and efficient fetal birth weight estimation from 3d ultrasound (2025)

    2025

  20. [20]

    Medical Image Analysis72, 102119 (2021)

    Yang, X., Huang, Y., Huang, R., Dou, H., Li, R., Qian, J., Huang, X., Shi, W., Chen, C., Zhang, Y., et al.: Searching collaborative agents for multi-plane local- ization in 3d ultrasound. Medical Image Analysis72, 102119 (2021)

    2021

  21. [21]

    Nature Communications (2026)

    Zhang, Y., Huang, Y., Dou, H., Zhu, X., Ling, C., Yang, Z., Liang, L., Li, J., Liang, S., Li, R., et al.: Artificial intelligence for detecting fetal orofacial clefts and advancing medical education. Nature Communications (2026)

    2026