تقرير
Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans
| العنوان: | Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans |
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| المؤلفون: | Venturini, Lorenzo, Budd, Samuel, Farruggia, Alfonso, Wright, Robert, Matthew, Jacqueline, Day, Thomas G., Kainz, Bernhard, Razavi, Reza, Hajnal, Jo V. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.4.7, J.3 |
| الوصف: | 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. Comment: 14 pages, 16 figures. Submitted to NPJ digital medicine. For associated video file, see http://wp.doc.ic.ac.uk/ifind/wp-content/uploads/sites/79/2023/12/realtime.gif |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2401.01201 |
| رقم الأكسشن: | edsarx.2401.01201 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |