Prenatal Diagnosis and Fetopsy Validation of Complete Atrioventricular Septal Defects Using the Fetal Intelligent Navigation Echocardiography Method

Optimizing Object Detection Algorithms for Congenital Heart Diseases in Echocardiography: Exploring Bounding Box Sizes and Data Augmentation Techniques

Background

Congenital heart diseases (CHDs), particularly atrial and ventricular septal defects, pose significant health risks and common challenges in detection via echocardiography. Doctors often employ the cardiac structural information during the diagnostic process. However, prior CHD research has not determined the influence of including cardiac structural information during the labeling process and the application of data augmentation techniques.

Methods

This study utilizes advanced artificial intelligence (AI)-driven object detection frameworks, specifically You Look Only Once (YOLO)v5, YOLOv7, and YOLOv9, to assess the impact of including cardiac structural information and data augmentation techniques on the identification of septal defects in echocardiographic images.

Results

The experimental results reveal that different labeling strategies substantially affect the performance of the detection models. Notably, adjustments in bounding box dimensions and the inclusion of cardiac structural details in the annotations are key factors influencing the accuracy of the model. The application of deep learning techniques in echocardiography enhances the precision of detecting septal heart defects.

Conclusions

This study confirms that careful annotation of imaging data is crucial for optimizing the performance of object detection algorithms in medical imaging. These findings suggest potential pathways for refining AI applications in diagnostic cardiology studies.

Enhancing parental understanding of congenital heart disease through personalized prenatal counseling with 3D printed hearts

OBJECTIVES

In addition to a correct prenatal diagnosis of congenital heart disease (CHD), comprehensive parental counseling is crucial to ensure that parents are well-informed about the condition of the fetus. This study aims to investigate whether there is a significant difference in the information acquired by parents through traditional counseling, utilizing 2-dimensional (2D) illustrations and images, compared to an advanced approach utilizing personalized three-dimensional (3D) printed models of the fetal heart developed from 3D ultrasound imaging.

METHODS

This study, designed as a pilot randomized control trial, enrolled pregnant women with gestational ages greater than 18 weeks, whose fetuses were diagnosed with CHD and referred to our center between November, 2020 and June, 2021. Two groups of patients were included in the study. The first group received standard medical counseling with 2D images and illustrations, while the second group underwent advanced counseling with 3D-printed patient-specific heart models. Both groups were then required to complete the same survey in which the knowledge of the CHD was investigated. The 3D models were created from 3D ultrasound imaging and printed using resin materials in both 1:1 and 5:1 scale.

RESULTS

A comparison of the scores obtained from the two groups revealed that 3D visualization of the fetus's heart has the potential to increase parental knowledge about CHD and the required surgical procedures. Furthermore, all couples expressed interest in receiving a 1:1 scale model of their baby's heart.

CONCLUSION

Personalized prenatal counseling with 3D-ultrasound-based heart models positively impacts parents' understanding of CHD. The use of 3D models provides a more comprehensive and accessible representation of the condition, contributing to an increased knowledge gain, and potentially helping to support informed decisions regarding their child's care.

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

Application of neural networks in prenatal diagnosis of atrioventricular septal defect

Background

There is no relevant study on landmarks detection, one of the Convolutional Neural Network algorithms, in the field of fetal echocardiography (FE). This study aimed to explore whether automatic landmarks detection could be used in FE correctly and whether the atrial length (AL) to ventricular length (VL) ratio (AVLR) could be used to diagnose atrioventricular septal defect (AVSD) prenatally.

Methods

This was an observational study. Two hundred and seventy-eight four-chamber views in end diastole, divided into the normal, AVSD, and differential diagnosis groups, were retrospectively included in this study. Seven landmarks were labeled sequentially by the experts on these images, and all images were divided into the training and test sets for normal, AVSD, and differential diagnosis groups. U-net, MA-net, and Link-net were used as landmark prediction neural networks. The accuracy of the landmark detection, AL, and VL measurements, as well as the prenatal diagnostic effectiveness of AVLR for AVSD, was compared with the expert labeled.

Results

U-net, MA-net, and Link-net could detect the landmarks precisely (within the localization error of 0.09 and 0.13 on X and Y axis) and measure AL and VL accurately (the measured pixel distance error of AL and VL were 0.12 and 0.01 separately). AVLR in AVSD was greater than in other groups (P<0.0001), but the statistical difference was not obvious in the complete, partial, and transitional subgroups (P>0.05). The diagnostic effectiveness of AVLR calculated by three models, area under receiver operating characteristic curve could reach 0.992 (0.968-1.000), was consistent with the expert labeled.

Conclusions

U-net, Link-net, and MA-net could detect landmarks and make the measurements accurately. AVLR calculated by three neural networks could be used to make the prenatal diagnosis of AVSD.

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.