491 ScanNav® audit: an AI-powered screening assistant for fetal anatomical ultrasound

Yaqub, Mohammad; Sleep, Nicholas; Syme, Steven; Ze-zhi, Chen; Ryou, Hosuk; Walton, Sacha; Noble, J. Alison; Papageorghiou, A.T.

doi:10.1016/j.ajog.2020.12.512

Cited by 7 publications

(5 citation statements)

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“…In other recent research papers in our field, algorithms have been shown to be useful in identifying the fetal occiput position during labor 8 and in the classification of ovarian tumors as benign or malignant 9 . Additionally, major ultrasound machine manufacturers have unveiled applications that are based on algorithms, although their development and validity often remain unreported for commercial reasons [10][11][12] . Therefore, all of these AI systems should remain at Level 2 of automation (Figure 2) until robust clinical evidence is collected via prospective trials.…”

Section: Drukkermentioning

confidence: 99%

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Drukker

2022

Ultrasound in Obstet & Gyne

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Section: Drukkermentioning

confidence: 99%

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Drukker

2022

Ultrasound in Obstet & Gyne

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“…Although it is not based on clinical criteria, detailed anatomical annotations are still required in training. A recent evaluation of a real-time Artificial Intelligence (AI) based system that automatically keeps track of acquired images and checks images conform to imaging protocol standards is reported in [17] where five experienced sonographers are used as the reference. A specified pre-defined protocol and annotated locations of anatomical structures are required in the aforementioned methods, which limits transferability to new applications.…”

Section: Related Workmentioning

confidence: 99%

Towards Unsupervised Ultrasound Video Clinical Quality Assessment with Multi-modality Data

Zhao

Zheng

Teng

et al. 2022

Lecture Notes in Computer Science

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Video quality assurance is an important topic in obstetric ultrasound imaging to ensure that captured videos are suitable for biometry and fetal health assessment. Previously, one successful objective approach to automated ultrasound image quality assurance has considered it as a supervised learning task of detecting anatomical structures defined by a clinical protocol. In this paper, we propose an alternative and purely data-driven approach that makes effective use of both spatial and temporal information and the model learns from high-quality videos without any anatomy-specific annotations. This makes it attractive for potentially scalable generalisation. In the proposed model, a 3D encoder and decoder pair bi-directionally learns a spatio-temporal representation between the video space and the feature space. A zoom-in module is introduced to encourage the model to focus on the main object in a frame. A further design novelty is the introduction of two additional modalities in model training (sonographer gaze and optical flow derived from the video). Finally, our approach is applied to identify high-quality videos for fetal head circumference measurement in freehand second-trimester ultrasound scans. Extensive experiments are conducted, and the results demonstrate the effectiveness of our approach with an AUC of 0.911.

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“…Yacub et al took a similar approach, using a neural network to, on the one hand, ascertain the completeness of a sonographic abnormality diagnosis and, on the other, to perform quality control of the image data obtained (in accordance with international guidelines). No differences were demonstrated in this case compared to manual expert assessment [77,78]. The same approaches to modelling now also form the (intelligent) basis for the worldʼs first fully integrated AI tool for automated biometric detection of foetal target structures and AI-supported quality control (SonoLyst) [5].…”

Section: Ai In Standardised Diagnostics Of the Foetal Cnsmentioning

confidence: 99%

“…Einen ähnlichen Ansatz wählten Yacub et al, die ein neuronales Netzwerk einsetzten, was zum einen der Feststellung der Vollständigkeit einer sonografischen Fehlbildungsdiagnostik und zum anderen der Qualitätskontrolle der gewonnenen Bilddaten (entsprechend den Vorgaben internationaler Richtlinien) diente. Hier zeigten sich im Vergleich zur manuellen Expertenbegutachtung keine Unterschiede 77 , 78 . Die gleichen Modellansätze bilden mittlerweile auch die (intelligente) Basis für das weltweit erste vollständig integrierte KI-Tool (SonoLyst) zur automatisierten biometrischen Erfassung fetaler Zielstrukturen und ebenfalls KI-gestützter Qualitätskontrolle 5 .…”

Section: Ki In Der Standardisierten Diagnostik Des Fetalen Znsunclassified

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

Weichert

Welp

Scharf

et al. 2021

Geburtshilfe Frauenheilkd

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The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter – at least in part – into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

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491 ScanNav® audit: an AI-powered screening assistant for fetal anatomical ultrasound

Cited by 7 publications

References 0 publications

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Real‐time identification of fetal anomalies on ultrasound using artificial intelligence: what's next?

Towards Unsupervised Ultrasound Video Clinical Quality Assessment with Multi-modality Data

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

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