Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Journal article

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions as good as humans (or even better). AI is already a part of our daily life – it is behind face recognition, speech recognition in virtual assistants (like Amazon Alexa, Apple's Siri, Google Assistant, and Microsoft Cortana) and self‐driving cars. AI software has been able to win world champions in Chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions, and provide quality assurance in radiology. The full list of medical image analysis AI applications with US Food and Drug Administration (FDA) or European Union regulation (soon to fall under European Union Medical Device Regulation (EU‐MDR)) is growing rapidly and covers diverse clinical needs, such as arrhythmia detection with your smartwatch or automatic triage of critical imaging studies to the top of the radiologist worklist. Deep learning, a leading tool of AI, is in particular good at image pattern recognition and therefore of high benefit to doctors who heavily depend on images, like sonologists, radiographers and pathologists. Although obstetric and gynecologic ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential to assist in repetitive ultrasound tasks, such as automatically identifying good acquisitions and immediate quality assure. For this potential to thrive interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this opinion we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare.

Authors: Drukker, L; Noble, JA; Papageorghiou, AT

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Ultrasound in Obstetrics & Gynecology
• Volume: 56
• Issue: 4
• Pages: 498-505
• Publication date: 2020-06-12
• Acceptance date: 2020-06-01
• DOI: 10.1002/uog.22122
• EISSN: 1469-0705
• ISSN: 0960-7692
• Language: English
• Keywords: ultrasound; artificial intelligence; computational algorithms; FFR; medical image analysis; big data; computer vision; medical imaging; deep learning; machine learning