Locating Multiple Standard Planes in First-Trimester Ultrasound Videos via the Detection and Scoring of Key Anatomical Structures

Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing

BACKGROUND

Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI-IQA) system to audit images from the four key planes used in first-trimester scanning.

METHODS

The AI-IQA system was developed based on the YOLOv7 structure detection network and a multi-branch image quality regression network using a large multicenter internal dataset. Clinical validation was performed using 567 cases scanned by four radiologists with different experience levels, of which 349 were performed without AI-IQA feedback (clinical test set 1) and 218 were performed after 2-3 rounds of AI-IQA feedback (clinical test set 2). The proportion of standard images obtained and detailed expert audit results were compared to verify whether AI-IQA could objectively and accurately provide feedback on deficiencies in nonstandard images to assist radiologists at different experience levels in improving image quality.

RESULTS

In the internal test set, the AI-IQA system achieved high average accuracy precision, recall and F1-score in auditing the overall plane quality (0.881, 0.833, 0.842 and 0.837, respectively) and structure quality (0.906, 0.861, 0.857 and 0.859, respectively). In clinical test sets 1 and 2, AI-IQA results showed strong consistency with expert assessment results, with the average Cohen's Kappa coefficient exceeding 0.8 for all four planes. In addition, following AI-IQA feedback, the proportion of standard images obtained by junior and mid-level radiologists increased by 7.7% and 5.1%, respectively. AI-IQA takes only 0.05 s to assess each image, while experts require more than 20 s (p < 0.001).

CONCLUSIONS

The proposed AI-IQA system proved to be a highly accurate and efficient method of automatically auditing first-trimester scanning image quality, providing precise and rapid key plane quality control. This tool can also assist radiologists with different levels of experience to improve the image quality.

Research advances and trends in anatomy from 2013 to 2023: A visual analysis based on CiteSpace and VOSviewer

As the cornerstone of medicine, the development of anatomy is related to many disciplines and fields and has received extensive attention from researchers. How to integrate and grasp the cutting-edge information in this field quickly is a challenge for researchers, so the aim of this study is to analyze research in anatomy using CiteSpace and VOSviewer in order to identify research hotspots and future directions. To offer a fresh viewpoint for assessing the academic influences of researchers, nations, or institutions on anatomy, and to examine the development of hotspots in anatomical study and to forecast future trends. A total of 4637 anatomy-related publications from 2013 to 2023 were collected from Web of Science Core Collection databases. Their temporal distribution, spatial distribution, cited authors, co-cited journals, keywords, and disciplinary connections in the literature were analyzed using CiteSpace and VOSviewer, and a knowledge graph was constructed. The temporal distribution shows a general fluctuation in the amount of literature published from 2013 to 2023. In spatial distribution, the total number of published articles was highest in the United States, the United Kingdom, and China, the United States leading. Tubbs, Rhoton, Iwanaga, and LaPrade are important authors in anatomy. Clinical Anatomy, Surgical and Radiologic Anatomy, and Journal of Anatomy were the most highly cited journals. Analysis of keywords and citation emergence showed that the research hotspots and trends in anatomy focused mainly on anatomy education, digital technology, and surgical management. At the same time, anatomy showed a trend toward multidisciplinary crossover, developing closer relationships with molecular biology, immunology, and clinical medicine. Current research in anatomy focuses on innovative reform of the educational model and the application and promotion of digital technology. Also, multidisciplinary cross-fertilization is an inevitable trend for the future development of anatomy.

SEG-LUS: A novel ultrasound segmentation method for liver and its accessory structures based on muti-head self-attention

Although liver ultrasound (US) is quick and convenient, it presents challenges due to patient variations. Previous research has predominantly focused on computer-aided diagnosis (CAD), particularly for disease analysis. However, characterizing liver US images is complex due to structural diversity and a limited number of samples. Normal liver US images are crucial, especially for standard section diagnosis. This study explicitly addresses Liver US standard sections (LUSS) and involves detailed labeling of eight anatomical structures. We propose SEG-LUS, a US image segmentation model for the liver and its accessory structures. In SEG-LUS, we have adopted the shifted windows feature encoder combined with the cross-attention mechanism to adapt to capturing image information at different scales and resolutions and address context mismatch and sample imbalance in the segmentation task. By introducing the UUF module, we achieve the perfect fusion of shallow and deep information, making the information retained by the network in the feature extraction process more comprehensive. We have improved the Focal Loss to tackle the imbalance of pixel-level distribution. The results show that the SEG-LUS model exhibits significant performance improvement, with mPA, mDice, mIOU, and mASD reaching 85.05%, 82.60%, 74.92%, and 0.31, respectively. Compared with seven state-of-the-art semantic segmentation methods, the mPA improves by 5.32%. SEG-LUS is positioned to serve as a crucial reference for research in computer-aided modeling using liver US images, thereby advancing the field of US medicine research.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.