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Jost E, Kosian P, Jimenez Cruz J, Albarqouni S, Gembruch U, Strizek B, Recker F. Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J Clin Med 2023; 12:6833. [PMID: 37959298 PMCID: PMC10649694 DOI: 10.3390/jcm12216833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
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.
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Affiliation(s)
- Elena Jost
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Philipp Kosian
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Jorge Jimenez Cruz
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Shadi Albarqouni
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
- Helmholtz AI, Helmholtz Munich, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Ulrich Gembruch
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
| | - Florian Recker
- Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany
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Herrera CL, Kim MJ, Do QN, Owen DM, Fei B, Twickler DM, Spong CY. The human placenta project: Funded studies, imaging technologies, and future directions. Placenta 2023; 142:27-35. [PMID: 37634371 PMCID: PMC11257151 DOI: 10.1016/j.placenta.2023.08.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 08/16/2023] [Accepted: 08/19/2023] [Indexed: 08/29/2023]
Abstract
The placenta plays a critical role in fetal development. It serves as a multi-functional organ that protects and nurtures the fetus during pregnancy. However, despite its importance, the intricacies of placental structure and function in normal and diseased states have remained largely unexplored. Thus, in 2014, the National Institute of Child Health and Human Development launched the Human Placenta Project (HPP). As of May 2023, the HPP has awarded over $101 million in research funds, resulting in 41 funded studies and 459 publications. We conducted a comprehensive review of these studies and publications to identify areas of funded research, advances in those areas, limitations of current research, and continued areas of need. This paper will specifically review the funded studies by the HPP, followed by an in-depth discussion on advances and gaps within placental-focused imaging. We highlight the progress within magnetic reasonance imaging and ultrasound, including development of tools for the assessment of placental function and structure.
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Affiliation(s)
- Christina L Herrera
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, and Parkland Health Dallas, Texas, USA; Green Center for Reproductive Biology Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Meredith J Kim
- University of Texas Southwestern Medical School, Dallas, TX, USA
| | - Quyen N Do
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - David M Owen
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, and Parkland Health Dallas, Texas, USA; Green Center for Reproductive Biology Sciences, UT Southwestern Medical Center, Dallas, TX, USA
| | - Baowei Fei
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA; Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA; Department of Bioengineering, University of Texas at Dallas, Dallas, TX, USA
| | - Diane M Twickler
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, and Parkland Health Dallas, Texas, USA; Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Catherine Y Spong
- Department of Obstetrics and Gynecology, UT Southwestern Medical Center, and Parkland Health Dallas, Texas, USA
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Zimmer VA, Gomez A, Skelton E, Wright R, Wheeler G, Deng S, Ghavami N, Lloyd K, Matthew J, Kainz B, Rueckert D, Hajnal JV, Schnabel JA. Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view. Med Image Anal 2022; 83:102639. [PMID: 36257132 PMCID: PMC7614009 DOI: 10.1016/j.media.2022.102639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 03/09/2022] [Accepted: 09/15/2022] [Indexed: 02/04/2023]
Abstract
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.
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Affiliation(s)
- Veronika A. Zimmer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,Faculty of Informatics, Technical University of Munich, Germany,Corresponding author at: School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom. , (V.A. Zimmer)
| | - Alberto Gomez
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Emily Skelton
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Health Sciences, City, University of London, London, United Kingdom
| | - Robert Wright
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gavin Wheeler
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Shujie Deng
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Nooshin Ghavami
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Karen Lloyd
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jacqueline Matthew
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Bernhard Kainz
- BioMedIA group, Imperial College London, London, United Kingdom,FAU Erlangen-Nürnberg Germany
| | - Daniel Rueckert
- Faculty of Informatics, Technical University of Munich, Germany,BioMedIA group, Imperial College London, London, United Kingdom
| | - Joseph V. Hajnal
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Julia A. Schnabel
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,Faculty of Informatics, Technical University of Munich, Germany,Helmholtz Center Munich, Germany
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