How much can AI see in early pregnancy: A multi‐center study of fetus head characterization in week 10–14 in ultrasound using deep learning

Lin, Qi; Zhou, Yi; Shi, Siyuan; Zhang, Yujuan; Yin, Shaoli; Liu, Xuye; Peng, Qihui; Huang, Shaoting; Jiang, Yitao; She, Ruilian; Xu, Jinfeng; Dong, Fajin

doi:10.1016/j.cmpb.2022.107170

Cited by 9 publications

(8 citation statements)

References 37 publications

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“…These challenges can be mitigated through the incorporation of techniques such as regularization parameter tuning and the strategic use of skip connections, as exemplified by ResNets. The inclusion of skip connections not only alleviates the vanishing gradient problem but also [58,[95][96][97][98][99][100][101][102][103]107,109,[115][116][117]120,[124][125][126][127][128][129][130][135][136][137]141,142,145,150,153,157,160,161,169]. Each study's color code reflects its relevance to a certain organ, including the heart, brain, lung, or analyzing for chromosomal abnormalities.…”

Section: Discussionmentioning

confidence: 99%

“…Lin et al made a model that was trained on 1842 2D sagittal-view US images. It was made to find nine intracranial structures of the fetus, including the thalami, midbrain, palate, fourth ventricle, cisterna magna, NT, nasal tip, nasal skin, and nasal bone [126]. The study used both standard and non-standard sagittal-view ultrasound images.…”

Section: Head and Neck Anomaliesmentioning

confidence: 99%

“…According to the authors, the accuracy of their model achieved 98.64% precision for classifying anomalies linked with NT thickness [157]. The previously mentioned Lin et al model was also capable of NT identification [126].…”

mentioning

confidence: 93%

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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: Discussionmentioning

confidence: 99%

Section: Head and Neck Anomaliesmentioning

confidence: 99%

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

Yousefpour Shahrivar,

Karami,

Karami

2023

Biomimetics

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“…Xu et al presented an AI method for authentically simulating third-trimester images from second-trimester images for deep-learning researchers with restricted access to third-trimester images [84]. The automated detection of brain structures and malformations was described by Lin et al [85,86], Alansary et al [87], and Gofer et al [88] in 2D images and videos, and in 3D volumes by Hesse et al and Huang et al [79,89]. The image quality assessment of whether a standard plane was correctly acquired, either by human operators or by automated extraction from 3D images, was effectively performed by models developed by the research groups of Lin et al [90], Yaqub et al [91], and Skelton et al [92].…”

Section: Fetal Neurosonographymentioning

confidence: 99%

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

Jost,

Kosian,

Jimenez Cruz

et al. 2023

JCM

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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.

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“…AI and ML can also help with Down syndrome prenatal diagnosis [49]. Artificial intelligence (AI) systems can spot possible indicators of Down syndrome in a growing fetus by examining ultrasound images [81] or blood test data. Because of the early detection, parents and medical professionals can better anticipate and support the child's requirements.…”

Section: Support For Prenatal Diagnosismentioning

confidence: 99%

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Koul,

Ahmad,

Bhat

et al. 2023

Biomedicines

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Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.

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How much can AI see in early pregnancy: A multi‐center study of fetus head characterization in week 10–14 in ultrasound using deep learning

Cited by 9 publications

References 37 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

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

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

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