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

Zhen, Chaojiong; Wang, Hongzhang; Cheng, Jun; Chen, Chao‐Yu; Hu, Xindi; Zhang, Yuanji; Cao, Yan; Ni, Dong; Huang, Weijun; Wang, Ping

doi:10.1016/j.ultrasmedbio.2023.05.005

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…Thus, the automatic recognition of standard planes in fetal US images is an effective method for diagnosing fetal anomalies. According to the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guidelines, there are several types of fetal standard planes (Table 3) [63][64][65].…”

Section: Ultrasound Imagingmentioning

confidence: 99%

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

Yousefpour Shahrivar,

Karami,

Karami

2023

Biomimetics

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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.

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Section: Ultrasound Imagingmentioning

confidence: 99%

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

Yousefpour Shahrivar,

Karami,

Karami

2023

Biomimetics

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

Du,

Cai,

Zheng

et al. 2024

Clinical Anatomy

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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.

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Automatic Facial Axes Standardization of 3D Fetal Ultrasound Images

Alomar,

Rubio,

Salort

et al. 2024

Lecture Notes in Computer Science

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

Cited by 6 publications

References 24 publications

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

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

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

Automatic Facial Axes Standardization of 3D Fetal Ultrasound Images

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