首页|University of Adelaide Reports Findings in Artificial Intelligence (Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence)
University of Adelaide Reports Findings in Artificial Intelligence (Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence)
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New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Adelaide, Australia, by NewsRx journalists, research stated, “Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy.” The news reporters obtained a quote from the research from the University of Adelaide, “Noninvasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the risk and expense of surgery. This review updates the exponentially increasing literature exploring the diagnostic value of endometriosis specialist transvaginal ultrasound (eTVUS), combinations of eTVUS and specialist magnetic resonance imaging, and artificial intelligence. Concentrating on literature that emerged after the publication of the IDEA consensus in 2016, we identified 6192 publications and reviewed 49 studies focused on diagnosing endometriosis using emerging imaging techniques. The diagnostic performance of eTVUS continues to improve but there are still limitations. eTVUS reliably detects ovarian endometriomas, shows high specificity for deep endometriosis and should be considered diagnostic. However, a negative scan cannot preclude endometriosis as eTVUS shows moderate sensitivity scores for deep endometriosis, with the sonographic evaluation of superficial endometriosis still in its infancy. The fast-growing area of artificial intelligence in endometriosis detection is still evolving, but shows great promise, particularly in the area of combined multimodal techniques. We finalize our commentary by exploring the implications of practice change for surgeons, sonographers, radiologists, and fertility specialists.”
AdelaideAustraliaAustralia and New ZealandArtificial IntelligenceDiagnostic ImagingDiagnostics and ScreeningEmerging TechnologiesEndometriosisFemale Genital Diseases and ConditionsFemale Urogenital Diseases and ConditionsHealth and MedicineMachine LearningUterine Diseases and ConditionsWomen’s Health
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