Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Abstract

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

“…In fact, despite gynecology being one of the areas with the largest imaging component, the impact of AI in practice is still in an embryonic phase. Nevertheless, there is a need to understand the limitations of the available clinical imaging methods, namely clinician workload and intra and interobserver variability, and AI has the potential to overcome these limitations while increasing diagnostic accuracy [23]. However, AI has a huge and recognized potential to assist in repetitive tasks, such as automatically identifying good-quality images and identifying imaging patterns [21].…”

Ultrasound-Based Deep Learning Models Performance versus Expert Subjective Assessment for Discriminating Adnexal Masses: A Head-to-Head Systematic Review and Meta-Analysis

“…Artificial intelligence is becoming a relevant issue in medical practice, particularly for diagnosis. In fact, the use of AI models in the differential diagnosis of adnexal masses for detecting ovarian cancer is increasing, as recently described [64,65]. AI models in medical imaging may be based on data and/or image processing [25].…”

How automated techniques ease functional assessment of the fetal heart: applicability of two-dimensional speckle-tracking echocardiography for comprehensive analysis of global and segmental cardiac deformation using fetalHQ®

“…Therefore, extensive training and education of future experts is of enormous significance [48][49][50][51]. Applications based on artificial intelligence (AI), on whose algorithms prenatal diagnostics will rely on increasingly, are transforming the way clinicians use ultrasound [50,[52][53][54][55][56][57][58][59][60][61][62][63][64][65].…”