Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging

2025-01-01

AI Summary

To address low detection accuracy, high diagnostic burden on clinicians, and elevated misdiagnosis rates in prenatal ultrasound screening for fetal central nervous system (CNS) malformations, this study develops the first multi-center deep learning model covering the entire gestational period for automated detection and classification of four canonical CNS anomalies: anencephaly, encephalocele, holoprosencephaly, and spina bifida. Methodologically, we integrate a ResNet-based architecture, multi-center collaborative training, and class activation mapping (CAM) for interpretable lesion localization. Our contribution is the first demonstration of robust, gestational-week-agnostic CNS anomaly classification with intrinsic interpretability. The model achieves 94.5% patient-level accuracy and 99.3% AUROC. A retrospective reader study demonstrates significant improvements in radiologists’ diagnostic accuracy and efficiency, alongside substantial reduction in misdiagnosis rates.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect.

Problem

• Prenatal Ultrasound
• Fetal Brain Abnormalities
• Diagnostic Accuracy

Innovation

• Ultrasound Image Analysis
• Fetal Brain Abnormalities
• AI-assisted Diagnosis