The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by aggregating automatically extracted biometrics from every frame across an entire scan, with no need for operator intervention. We use a convolutional neural network to classify each frame of an ultrasound video recording. We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. We performed a retrospective experiment on 1457 recordings (comprising 48 million frames) of 20-week ultrasound scans, estimated fetal biometrics in those scans and compared our estimates to the measurements sonographers took during the scan. Our method achieves human-level performance in estimating fetal biometrics and estimates well-calibrated credible intervals in which the true biometric value is expected to lie.
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
Sano Centre for Computational Medicine（萨诺计算医学中心）；Warsaw University of Technology（华沙理工大学）；Fetai Health Ltd.（Fetai健康有限公司）；Medical University of Warsaw（华沙医科大学）；Tooploox（Tooploox公司）
AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes
University College London（伦敦大学学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College London（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）
Determining Fetal Orientations From Blind Sweep Ultrasound Video
Technical University of Denmark（丹麦技术大学）；University of Copenhagen（哥本哈根大学）；Slagelse Hospital（斯劳厄尔瑟医院）；CAMES Rigshospitalet（CAMES 哥本哈根大学医院）；Pioneer Centre for AI（人工智能先锋中心）
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
Sano Centre for Computational Medicine（萨诺计算医学中心）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；The Medical Centre of Postgraduate Education（研究生教育医学中心）；Poznan University of Medical Sciences（波兹南医科大学）；Medical University of Warsaw（华沙医科大学）；Warsaw University of Technology（华沙理工大学）
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Department of Computer Vision（计算机视觉系）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫比兹人工智能大学）；Department of Machine Learning（机器学习系）；Corniche Hospital, Abu Dhabi Health Services Company (SEHA)（阿布扎赫尔医院，阿布扎赫健康服务公司（SEHA））
AI system for fetal ultrasound in low-resource settings
Google Health（谷歌健康）；University of Zambia School of Medicine（赞比亚大学医学院）；University of North Carolina School of Medicine（北卡罗来纳大学医学院）；UNC Global Projects – Zambia, LLC（北卡罗来纳大学全球项目赞比亚有限责任公司）
Learning to learn skill assessment for fetal ultrasound scanning
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Department of Medical Physics and Biomedical Engineering, University College London（医学物理与生物医学工程系，伦敦大学学院）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（妇女与生殖健康系，牛津大学）；Rabin-Beilinson Medical Center, Tel-Aviv University Faculty of Medicine（拉宾-贝利松医疗中心，特拉维夫大学医学院）
EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography
Peng Cheng Laboratory（鹏城实验室）；Beihang University（北京航空航天大学）；Capital Medical University（首都医科大学）；Beijing Anzhen Hospital（北京安贞医院）；Beijing Advanced Innovation Center for Big Data-Based Precision Medicine（北京大数据精准医疗先进创新中心）
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；City, University of London（伦敦城市大学）；Technical University of Munich（慕尼黑工业大学）
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Harvard Medical School（哈佛医学院）；Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；University of Copenhagen（哥本哈根大学）；Copenhagen Academy of Medical Education and Simulation (CAMES), Rigshospitalet（哥本哈根医学教育与模拟学院（CAMES），国立医院（Rigshospitalet））；Pioneer Centre for AI, Copenhagen（哥本哈根人工智能先锋中心）
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究培训机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Veterinary Medicine, University College Dublin（都柏林大学兽医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
University of Copenhagen（哥本哈根大学）；Technical University of Denmark（丹麦技术大学）；Copenhagen Academy of Medical Education and Simulation (CAMES)（哥本哈根医学教育与模拟学院）；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院拉格比医院）；Brown University（布朗大学）；Pioneer Centre for AI（先锋人工智能中心）
Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection
Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）；AIM for Health Lab, Faculty of Information Technology, Monash University（健康AIM实验室，信息技术学院，莫纳什大学）；School of Biomedical Engineering, University of Sydney（悉尼大学生物医学工程学院）；Department of Chemical & Biological Engineering, Monash University（莫纳什大学化学与生物工程系）；Department of Obstetrics & Gynaecology, Monash University（莫纳什大学妇产科系）
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
Hunan University（湖南大学）；The Hong Kong University of Science and Technology（香港科技大学）；Anhui University（安徽大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳市妇幼保健院）；Sichuan Provincial Maternity and Child Health Care Hospital（四川省妇幼保健院）；National University of Singapore（新加坡国立大学）；Yunnan Maternal and Child Health Hospital（云南省妇幼保健院）；Zhejiang University（浙江大学）
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程技术研究中心，武汉大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（computing and Data Science学院，南洋理工大学）
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）；Guy’s and St Thomas’ NHS Foundation（盖伊和圣托马斯国民保健服务基金会）
从胎儿超声图像中测量头部生物特征对于监测胎儿的健康发育具有重要意义。然而，相关解剖结构的准确测量在临床中受到较大的观察者间差异的影响。为了解决这个问题，提出了一种利用全卷积网络（FCN）来确定胎儿头围（HC）和双顶径（BPD）测量值的自动化方法。在常规筛查检查期间，在头部的大约2000个2D超声图像上训练FCN，所述图像具有由45个不同的超声医师提供的注释，以执行头部的语义分割。椭圆被拟合到所得到的分割轮廓，以模仿通常由声谱仪操作员产生的注释。该模型的性能与观察者间的变异性进行了比较，其中两位专家手动注释了100张测试图像。HC的平均绝对模型专家误差略优于观察者间误差（1.99 mm vs 2.16 mm），BPD（0.61 mm vs 0.59 mm）以及Dice系数（0.980 vs 0.980）相当。我们的研究结果表明，该模型的性能与人类专家相似，并学会从由许多超声医师注释的大型数据集中产生准确的预测。此外，在GPU上以15 fps的速度近实时生成测量结果，这可以加快熟练和实习超声医师的临床工作流程。
