Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model
This work addresses the problem of inconsistent radiologist interpretations for PAS diagnosis, offering a potential computer-aided tool to improve diagnostic consistency, though it is incremental as it builds on existing hybrid CNN-Transformer methods.
The study tackled the challenge of diagnosing Placenta Accreta Spectrum (PAS) from MRI scans by proposing a hybrid 3D deep learning model, which achieved an average accuracy of 84.3% on an independent test set.
Placenta Accreta Spectrum (PAS) is a serious obstetric condition that can be challenging to diagnose with Magnetic Resonance Imaging (MRI) due to variability in radiologists' interpretations. To overcome this challenge, a hybrid 3D deep learning model for automated PAS detection from volumetric MRI scans is proposed in this study. The model integrates a 3D DenseNet121 to capture local features and a 3D Vision Transformer (ViT) to model global spatial context. It was developed and evaluated on a retrospective dataset of 1,133 MRI volumes. Multiple 3D deep learning architectures were also evaluated for comparison. On an independent test set, the DenseNet121-ViT model achieved the highest performance with a five-run average accuracy of 84.3%. These results highlight the strength of hybrid CNN-Transformer models as a computer-aided diagnosis tool. The model's performance demonstrates a clear potential to assist radiologists by providing a robust decision support to improve diagnostic consistency across interpretations, and ultimately enhance the accuracy and timeliness of PAS diagnosis.