Abstract:

Objective

To explore the application value of deep learning algorithms in quality control of the 11 standard fetal cardiac views.

Methods

Images of fetal echocardiography were collected from seven hospitals, of which a total of 35331 images were selected from fetuses between 20 and 34 weeks of gestation.Based on the 11 standard fetal cardiac views recommended by fetal echocardiography guidelines, a novel automatic quality control method integrating transformer-based techniques was proposed to assess image quality.Using expert evaluation as the reference standard, the collected images were divided into two datasets: dataset A (24 000 images) for model training, and dataset B (11 331 images) for both deep learning-based prediction and manual quality assessment by two physicians with five years of clinical experience.Average precision (AP) was used as the primary metric to evaluate model performance.

Results

The transformer-based automatic quality control method achieved an AP of 0.885 in recognizing anatomical structures in fetal cardiac views, demonstrating accurate identification of key anatomical features required in standard fetal echocardiography.The deep learning model processed each image in approximately 0.028 seconds, while the two experienced physicians took an average of 3.77 seconds per image.Thus, the deep learning-based approach was 134.6 times faster than manual evaluation.

Conclusion

The application of deep learning models for quality control of fetal echocardiographic views can achieve expert-level performance while significantly reducing the time required for manual quality assessment.

Key words: Artificial intelligence, Fetal cardiac, Quality control