A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI
Open accessAbstract
Background: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS‐related signs are generally found along the utero‐placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue. Purpose: To develop a DL tool for antenatal diagnosis of PAS using T2‐weighted MR images. Study Type: Retrospective. Subjects: Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets. Field Strength/Sequence: Sagittal T2‐weighted fast spin echo sequence at 1.5 T and 3 T. Assessment: An nnU‐Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero‐placental boundary region. The UPB image was then fed into DenseNet‐PAS for PAS diagnosis. DenseNet‐PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard. Statistical Tests: Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann–Whitney U‐test or the chi‐squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs. Results: Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737–0.770). Data Conclusion: By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI. Level of Evidence: 3 Technical Efficacy: Stage 2
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Wang et al. concluded that ML provided more consistent diagnostic accuracy, reducing variability caused by differing levels of radiological expertise for PAS diagnosis.
“The study concluded that ML provided more consistent diagnostic accuracy, reducing variability caused by differing levels of radiological expertise.”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [31]
The Discussion (page not provided) says radiologists’ PAS interpretations vary with expertise and proposes the DL tool to homogenize diagnosis across regions. The paper reports strong model performance, but it does not establish that using the tool actually reduced variability in diagnostic accuracy across radiologists with different expertise. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn a 2024 retrospective study, Wang et al. found that their ML program outperformed radiologists in diagnosing PAS, with an AUC of 0.860 vs. 0.737/0.749/0.770.
“They discovered that the ML program outperformed the radiologists, with an area under the curve (AUC) of 0.860 vs. 0.737/0.749/0.770, respectively.”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [31]
Wang et al. concluded that their program could normalize the varying levels of manual diagnostic PAS expertise through the minimization of PAS diagnosis to their one ML program.
“Wang et al. [31] concluded that their program could normalize the varying levels of manual diagnostic PAS expertise through the minimization of PAS diagnosis to their one ML program.”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [31]
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- Authors
- Wang, H., Wang, Y., Zhang, H., Yin, X., Wang, C., Lu, Y., Song, Y., Zhu, H., & Yang, G.
- Affiliations
- East China Normal University (CN)
- Shanghai Medical College of Fudan University (CN)
- Tongji University (CN)
- Siemens (China) (CN)
- Corresponding author
- Zhu, Hao
- Yang, Guang
- ORCID
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2023-05-13
- Volume
- 59
- Issue
- 2
- Pages
- 483-493
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W4376504590
- Cited by
- 19
- References
- 41
- Citations by year
- 2026: 8 · 2025: 4 · 2024: 5 · 2023: 2
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61731009)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Maternal and fetal healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- placenta accreta spectrum, prenatal diagnosis, placenta segmentation, placental location, T2-weighted MRI, nnU-Net, DenseNet, radiologist experience, automated diagnosis
- MeSH terms
- Deep Learning, Female, Humans, Magnetic Resonance Imaging, Placenta, Placenta Accreta, Pregnancy, Retrospective Studies
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