Placenta Accreta Spectrum Detection using Multimodal Deep Learning

TL;DR

This study develops a multimodal deep learning framework combining MRI and ultrasound imaging to improve the prenatal detection of Placenta Accreta Spectrum, achieving higher accuracy than single-modality models.

Contribution

It introduces a novel intermediate feature-level fusion architecture integrating MRI and US data for PAS detection, validated on large datasets.

Findings

Multimodal model achieved 92.5% accuracy and 0.927 AUC.

Multimodal fusion outperformed unimodal MRI and US models.

Integrating MRI and US features enhances diagnostic performance.

Abstract

Placenta Accreta Spectrum (PAS) is a life-threatening obstetric complication involving abnormal placental invasion into the uterine wall. Early and accurate prenatal diagnosis is essential to reduce maternal and neonatal risks. This study aimed to develop and validate a deep learning framework that enhances PAS detection by integrating multiple imaging modalities. A multimodal deep learning model was designed using an intermediate feature-level fusion architecture combining 3D Magnetic Resonance Imaging (MRI) and 2D Ultrasound (US) scans. Unimodal feature extractors, a 3D DenseNet121-Vision Transformer for MRI and a 2D ResNet50 for US, were selected after systematic comparative analysis. Curated datasets comprising 1,293 MRI and 1,143 US scans were used to train the unimodal models and paired samples of patient-matched MRI-US scans was isolated for multimodal model development and evaluation. On an independent test set, the multimodal fusion model achieved superior performance, with an accuracy of 92.5% and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.927, outperforming the MRI-only (82.5%, AUC 0.825) and US-only (87.5%, AUC 0.879) models. Integrating MRI and US features provides complementary diagnostic information, demonstrating strong potential to enhance prenatal risk assessment and improve patient outcomes.