Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Abstract

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat 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 list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological 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 for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, 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.

How often do we identify fetal abnormalities during routine third‐trimester ultrasound? A systematic review and meta‐analysis

“…Our findings are also important when designing systems for artificial intelligence that assess third‐trimester biometry 55 , 56 . The very low incidence of identifiable abnormalities on the three basic biometric planes means that being unable to detect a standard plane is most likely due to an acquisition difficulty, rather than a fetal abnormality.…”

“…http://www.R-project. org/) using the 'Meta' 57 and 'Metafor' 58 , packages to create the analysis and plots used in this paper.…”

Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease

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Künstliche Intelligenz in der Medizin und Gynäkologie – Holzweg oder Heilversprechen?

“…B. zur Verbesserung der Sensitivität der Kardiotokographie [CTG]), in der onkologischen Gynäkologie (wie oben genannt, aber auch zur automatischen Krebsprognose anhand molekularer Biomarker und FIGO[Fédération Internationale de Gynécologie et d’Obstétrique]-Klassifizierung), generell das Feld der personalisierten Medizin (wo die ontologische Modellierung gut zu gebrauchen ist) und die Embryoauswahl bei IVF (In-vitro-Fertilisation; [ 12 ]). Weiter zeigt die Forschungsarbeit [ 11 ] bei der Interpretation von Ultraschallbildern und Videos nachvollziehbare KI-Anwendungsfelder auf. Diese Anwendungsfelder sind Sondenführung und Navigation, fetale biometrische Ebenenfinder, Anomalie-Scans, Highlighting und Zystenklassifikation.…”