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Meta-Analysis
. 2025 Dec;57(1):2523558.
doi: 10.1080/07853890.2025.2523558. Epub 2025 Jun 30.

Accuracy of multimodal vaginal ultrasound in the detection and assessment of scar healing after caesarean section: a correlational meta-analysis

Li Qiao  1 , Jingjing Lu  2 , Bihong Zhang  1 , Aiqun Liu  3 , Xiaohua Wang  1 , Mei Wang  4 , Jian Li  1 , Jiabao Meng  1
Affiliations
Meta-Analysis

Accuracy of multimodal vaginal ultrasound in the detection and assessment of scar healing after caesarean section: a correlational meta-analysis

Li Qiao et al. Ann Med. 2025 Dec.

Abstract

Objective: To evaluate the accuracy of multimodal vaginal ultrasound in assessing post-caesarean scar healing.

Methods: A systematic review and meta-analysis were conducted on Chinese and English studies using multimodal vaginal ultrasound to diagnose poor scar healing post-CS. Two researchers screened literature based on inclusion/exclusion criteria. Quality assessment and meta-analysis (RevMan 5.4, Stata14.0, Meta-DiSc1.4) were performed for various diagnostic indicators.

Results: Twenty-five high-quality studies identified key ultrasound markers: hypoechoic/anechoic scars (sensitivity 92%, specificity 91%), thinning/discontinuity of the myotomy layer (sensitivity 95%, specificity 90%), blurred incision contour (sensitivity 99%, specificity 91%), absent blood flow (sensitivity 92%, specificity 91%), irregular lesion morphology (sensitivity 95%, specificity 90%), uneven myometrial echogenicity (sensitivity 94%, specificity 91%), lower uterine segment thickness ≤3.73 mm (sensitivity 90%, specificity 88%), and myometrial lining ≤1.5 mm (sensitivity 90%, specificity 92%).

Conclusion: Multimodal vaginal ultrasound is highly accurate in detecting poor CS scar healing, aiding early intervention to prevent complications.

Keywords: META; Multimodal vaginal ultrasound; caesarean section; diagnosis; scar healing.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Literature inclusion process.
Figure 2.
Figure 2.
Literature quality assessment.
Figure 3.
Figure 3.
Bivariate box diagram.
Figure 4.
Figure 4.
Hypoechoic or non-echoic diagnostic Forest map.
Figure 5.
Figure 5.
Diagnostic forest plot of muscle thinning at incisions.
Figure 6.
Figure 6.
Forest Map for fuzzy diagnosis of incision contour.
Figure 7.
Figure 7.
Diagnostic forest plot of peripheral no blood flow signals.
Figure 8.
Figure 8.
Diagnostic forest plot of irregular lesion morphology.
Figure 9.
Figure 9.
Diagnostic forest plot of partial enhancement of lower segmental muscle echoes.
Figure 10.
Figure 10.
Diagnostic forestart of lower uterine segment thickness ≤3.73 mm.
Figure 11.
Figure 11.
Diagnostic forest plot of myometrial endometrial thickness ≤1.5 mmThe different ultrasound signs were summarised and combined SROC curves are shown in Figure 12.
Figure 12.
Figure 12.
Summary combined SROC curves for different ultrasound signs.
Figure 13.
Figure 13.
Funnel plot. Funnel plots of 25 included literature.
Figure 14.
Figure 14.
Heterogeneity analyzed by one-by-one exclusion method.
See this image and copyright information in PMC

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