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A filamentary soft robotic probe for multimodal in utero monitoring of fetal health

Nature Biomedical Engineering (2026)Cite this article

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Abstract

Fetal surgery offers valuable opportunities to address severe congenital disabilities, yet accurate evaluation of fetal physiological changes during in utero procedures to mitigate the risk of operative complications remains an unmet need. Conventional unimodal approaches lack predictive value, specificity and compatibility with minimally invasive interventions. Here we present a bioelectronic system featuring a multimodal, steerable filamentary probe that interfaces directly with the fetus in utero, enabling reliable and minimally invasive monitoring of various physiological parameters. Integrated soft robotic actuators ensure consistent contact through controlled navigation and force delivery, creating a gentle and secure interface with delicate fetal surfaces. In a sheep fetal surgery model, the multifunctional probe effectively monitored in utero conditions during fetoscopic surgeries, detecting fetal bradycardia, hypoxia and hypothermia, potentially informing for early intervention. Experimental results on rodents and large animal fetuses demonstrate potential for direct translation to human use. This system offers continuous, comprehensive fetal monitoring, addressing gaps in current clinical practices, and provides real-time insights during fetal surgeries.

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Fig. 1: Filamentary, soft robotic probe for multimodal in utero monitoring of fetal health.
Fig. 2: Numerical and experimental characterization of the soft robotic component of the system.
Fig. 3: Numerical and experimental characterization of the physiological sensors.
Fig. 4: In utero real-time fetal physiological sensing during fetoscopic lamb surgery.
Fig. 5: Multimodal continuous fetal lamb monitoring during open fetal surgery reveals fetal physiologic derangement in experimentally controlled scenarios.
Fig. 6: Pulse wave analysis and emergency intervention for critical fetal physiologic derangement of fetal lambs during open fetal surgery.

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Data availability

Raw data generated during the current study are available from the corresponding authors on reasonable request. The analysed data are available at https://doi.org/10.5281/zenodo.17371614 (ref. 71). Source data that support the findings of this study are included within this paper and its Supplementary Information files. Source data are provided with this paper.

Code availability

All computer code and customized software generated during and/or used in the current study are available at https://doi.org/10.5281/zenodo.17371614 (ref. 71).

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Acknowledgements

This work was funded by the Querrey-Simpson Institute for Bioelectronics. In vivo studies were supported by the Ann & Robert H. Lurie Children’s Hospital Foundation. This work made use of the NUFAB facility of Northwestern University’s NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the IIN and Northwestern’s MRSEC programme (NSF DMR-2308691). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Author notes

  1. These authors contributed equally: Hedan Bai, Jianlin Zhou, Mingzheng Wu, Steven Papastefan, Xiuyuan Li.

Authors and Affiliations

  1. Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA

    Hedan Bai, Jianlin Zhou, Mingzheng Wu, Kaiyu Zhao, Zhuoran Zhang, Wei Ouyang, Heyang Wang, Yibo Zhou, Kenneth Madsen, Shuo Li, Andrew I. Efimov, Katelyn Ma, Lisa Kovacs, Sahdev Patel, Yonggang Huang & John A. Rogers

  2. Department of Materials, ETH Zurich, Zurich, Switzerland

    Hedan Bai

  3. The Chicago Institute for Fetal Health, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA

    Steven Papastefan, Catherine R. Redden, Amir M. Alhajjat, Daniel R. Liesman, Katherine C. Ott & Aimen F. Shaaban

  4. Division of Pediatric Surgery, Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Steven Papastefan, Amir M. Alhajjat, Daniel R. Liesman, Katherine C. Ott & Aimen F. Shaaban

  5. Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA

    Xiuyuan Li, Haohui Zhang & Yonggang Huang

  6. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xiuyuan Li & Wenming Zhang

  7. Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA

    Kaiyu Zhao, Zhuoran Zhang, Yonggang Huang & John A. Rogers

  8. Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA

    Heyang Wang, Lisa Kovacs, Yonggang Huang & John A. Rogers

  9. Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA

    Yibo Zhou

  10. Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kenneth Madsen

  11. Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA

    Andrew I. Efimov, Katelyn Ma & John A. Rogers

  12. Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Sahdev Patel

  13. Department of Systems Engineering and Industrial Technologies, University of Parma, Parma, Italy

    Rinaldo Garziera

  14. Department of Radiology, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA

    Steffen Sammet

  15. Center for Bio-Integrated Electronics, Northwestern University, Evanston, IL, USA

    John A. Rogers

  16. Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    John A. Rogers

Authors
  1. Hedan Bai
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  2. Jianlin Zhou
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  3. Mingzheng Wu
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  4. Steven Papastefan
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  5. Xiuyuan Li
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  6. Haohui Zhang
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  7. Kaiyu Zhao
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  8. Zhuoran Zhang
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  9. Wei Ouyang
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Contributions

Conceptualization: H.B., J.Z., A.F.S. and J.A.R. Methodology: H.B., J.Z., S. Papastefan, X.L., A.F.S. and J.A.R. Theoretical simulations: X.L., H.Z. and Y.H. Investigation: H.B., J.Z., M.W., S. Papastefan, X.L., K.Z., Z.Z., W.O., C.R.R., A.M.A., H.W., Y.Z., K. Madsen, S.L., A.I.E., K. Ma, L.K., S. Patel, D.R.L., K.C.O., R.G., S.S., W.Z. and A.F.S. Software: J.Z. and W.O. Formal analysis: H.B., J.Z. and S. Papastefan. Validation: H.B., J.Z. and S. Papastefan. Data curation: H.B., J.Z., M.W. and S. Papastefan. Visualization: M.W., H.B., J.Z., S. Papastefan and X.L. Supervision: Y.H., A.F.S. and J.A.R. Funding acquisition: A.F.S. and J.A.R. Writing (original draft): H.B., J.Z., S. Papastefan and X.L. Writing (review and editing): J.Z., M.W., H.B., S. Papastefan, X.L., A.F.S. and J.A.R. H.B., J.Z., M.W., S. Papastefan and X.L. contributed equally to this work.

Corresponding authors

Correspondence to Hedan Bai, Yonggang Huang, Aimen F. Shaaban or John A. Rogers.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Anna David, Ellen Roche and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

Fabrication procedures of the 3D soft robotic actuator.

Extended Data Fig. 2 Finite element analysis of strain distribution on actuator layers.

(a) Overall strain distribution. (b) Strain distribution on the outer outer tear-resistant platinum-cure silicone elastomer (for example, DragonSkin-type, Smooth-On, Inc) layer. (c) Strain distribution on the low-modulus platinum-cure silicone (for example, Ecoflex-type, Smooth-On, Inc) chamber with enlarged views of critical edge leading to failure. (d) Strain distribution on polyimide strain limiting patterns. (e) Strain distribution on the copper layer of the probe.

Extended Data Fig. 3 Maximum strain on soft robotic structures during actuation.

(a) Left, FEA results showing cross-sectional strain distribution profile of silicone layers before and after flexion actuation. Scale bar, 1 mm. Arrows indicate the maximum strain points at the outer layer (orange) and chamber layer (blue). Right, maximum strain on different structures relative to the flexion angle. (b) Same as (a) but for torsion. (c) Same as (a) but for balloon inflation and inflation height.

Extended Data Fig. 4 Optimization of the outer layer thickness of the soft robotic actuator.

(a) Strain contour of flexion actuator with different outer layer thickness at a 180° flexion angle. (b) Simulation and experimental results of flexion angle with different outer layer thicknesses. (c) Strain contour of torsion actuator with different outer layer thickness at a 180° torsion angle. (d) Simulation and experiment result of torsion angle with different outer layer thickness. 120 µm, same data as load in Fig. 2d.

Source data

Extended Data Fig. 5 Numerical and experimental characterization of multimodal soft robotic actuator patterns.

(a) Strain contour of flexion actuator with different pattern gap widths at a 180° flexion angle. (b) Simulation and experimental results of flexion angle with different pattern gap width. (c) Strain contour of torsion actuator with different pattern angles at a 180° torsion angle. (d) Simulation and experimental results of torsion angle with different pattern angles.

Source data

Extended Data Fig. 6 ECG characteristics with different deployment approaches.

(a) Waveform and frequency spectrogram of ECG signal recorded from an adult mouse using SRFP with an epidermal approach. (b) Same as (a), but for the esophageal approach. (c) Same as (a), but for the rectal approach.

Source data

Extended Data Fig. 7 Validation of ECG signal detection in response to isoflurane euthanasia.

(a) Raw continuous ECG signals recorded during isoflurane euthanasia of an adult mouse using SRFP. (b) Frequency spectrum of recorded ECG signals during isoflurane euthanasia. (c) Calculated heart rate during isoflurane euthanasia.

Source data

Extended Data Fig. 8 Esophageal approach for physiological monitoring in a mouse model.

(a) Raw µ-IPD (Red and IR) signals showing esophageal PPG waveform recorded from an adult mouse. (b) Raw electrical signals showing esophageal ECG waveform recorded from an adult mouse.

Source data

Extended Data Fig. 9 Calibration of fetal SpO2 with cord blood gas analysis.

(a-b) Raw continuous µ-IPD signals during an ex-utero sheep fetal surgery. (c) The ratio R between the perfusion indices of red and IR µ-IPD signals. (d) Calibration of fetal SpO2 using linear regression between the perfusion index ratio R and arterial blood gas SaO2 values. (e) Computed physiological parameters, including calibrated SpO2, heart rate, and core temperature, during the same period of the experiment.

Source data

Extended Data Fig. 10 Esophageal approach for physiological monitoring during ex utero intrapartum treatment.

(a) Raw µ-IPD (Red and IR) signals showing the esophageal PPG waveform recorded from a sheep fetus. (b) PPG spectrogram (Red and IR) showing the esophageal PPG frequency recorded from a sheep fetus. (c) Calculated heart rate from esophageal PPG recording of a sheep fetus.

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Supplementary information

Supplementary Information

Supplementary Notes 1–5, Table 1, Figs. 1–13 and Videos 1–6.

Reporting Summary

Supplementary Video 1

Injectable multimodal soft robotic fetal probe.

Supplementary Video 2

Probe securement with actuator expansion.

Supplementary Video 3

Soft robotic actuation in a tubular agarose gel phantom.

Supplementary Video 4

Probe steering in a branching agarose gel phantom.

Supplementary Video 5

Multimodal in utero monitoring during fetoscopic surgery.

Supplementary Video 6

Single-port soft robotic fetal probe deployment in a simulated human fetus.

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Bai, H., Zhou, J., Wu, M. et al. A filamentary soft robotic probe for multimodal in utero monitoring of fetal health. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-025-01605-3

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