Research Appraisalother

Recent advances in multimodal approaches for investigating placental development and related diseases

PlacentaWang, Hao, Zhang, Tao, Ruan, Jing et al.12 June 2026DOI

Clinical Snapshot

25CEBM
Evidence: Weakother

PICO Framework

P — PopulationPregnant women and fetuses with focus on placental development and disease
I — InterventionMultimodal approaches including advanced imaging, single-cell omics, spatial profiling, and AI-based models
C — ComparatorTraditional placental research approaches
O — OutcomesDiagnostic accuracy, molecular understanding, real-time placental function assessment, and clinical management of placenta-related disorders

Bottom Line

This narrative review highlights promising multimodal approaches combining advanced imaging, single-cell omics, and artificial intelligence for placental research and clinical care. While the technologies show potential for improving diagnosis and understanding of conditions like preeclampsia and fetal growth restriction, the review lacks systematic methodology and critical appraisal of evidence quality. The absence of quantitative synthesis, formal bias assessment, and clear practice recommendations limits its utility for evidence-based decision making. Clinicians should await more rigorous systematic reviews and primary studies before implementing these approaches in routine practice. The emphasis on Chinese research contributions, while valuable, may not reflect global applicability. Current evidence is insufficient to support major practice changes, though continued research and development in this area appears warranted.

Evidence: Weak

Key Findings

  • Effect Size: Not quantified - narrative synthesis only

  • Primary Outcome: Multimodal approaches show promise for improving placental research and clinical care

  • Nnt Or Sensitivity: Improved diagnostic accuracy mentioned but not quantified

  • Confidence Interval: Not provided

Clinical Application

Limited by technology availability, cost, and need for specialized expertise Relevant to Australian maternal-fetal medicine units with advanced imaging capabilities; integration with existing RANZCOG guidelines needed Pregnant women at risk for placental disorders including preeclampsia, FGR, and placenta accreta spectrum

Abstract

The placenta is a vital organ that supports fetal growth and pregnancy maintenance. Its dysfunction is associated with severe complications, including preeclampsia, fetal growth restriction, and placenta accreta spectrum disorders. Traditional approaches to placental research have provided valuable insights but are limited in capturing the complexity of this dynamic organ. In recent years, multimodal approaches-integrating advanced imaging, single-cell and spatial omics, and artificial intelligence (AI)-have enabled comprehensive analyses of placental development and disease. These strategies offer improved diagnostic accuracy, deeper molecular understanding, and real-time assessment of placental function. This review summarizes recent advances in multimodal placental research, highlighting key technologies such as ultrasound and MRI, single-cell transcriptomics, spatial profiling, and AI-based prediction models. Particular emphasis is placed on contributions from Chinese research teams, who have developed novel platforms, atlases, and clinically relevant tools. We also discuss ongoing challenges, including data standardization, interpretability of AI models, and ethical considerations. Looking ahead, the integration of multimodal data with AI and wearable technologies holds promise for precision obstetrics and individualized pregnancy care. Together, these innovations are advancing both scientific understanding and clinical management of placenta-related disorders.

References

  1. 1.Wang, H., Zhang, T., Ruan, J., Zheng, X., Zheng, S., Liu, Q., He, F., Sun, B., Zhang, Q., Zhu, Y., & Chen, X. (2026). Recent advances in multimodal approaches for investigating placental development and related diseases. Placenta. https://doi.org/10.1016/j.placenta.2025.05.004
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