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Zhang J, Xiao S, Zhu Y, Zhang Z, Cao H, Xie M, Zhang L. Advances in the Application of Artificial Intelligence in Fetal Echocardiography. J Am Soc Echocardiogr 2024; 37:550-561. [PMID: 38199332 DOI: 10.1016/j.echo.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024]
Abstract
Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research.
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Affiliation(s)
- Junmin Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Sushan Xiao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Haiyan Cao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinical Research Center for Medical Imaging, Hubei Province, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
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Enache IA, Iovoaica-Rămescu C, Ciobanu ȘG, Berbecaru EIA, Vochin A, Băluță ID, Istrate-Ofițeru AM, Comănescu CM, Nagy RD, Iliescu DG. Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain. Life (Basel) 2024; 14:166. [PMID: 38398675 PMCID: PMC10890185 DOI: 10.3390/life14020166] [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: 10/24/2023] [Revised: 12/28/2023] [Accepted: 01/20/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks. OBJECTIVES We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.
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Affiliation(s)
- Iuliana-Alina Enache
- Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (I.-A.E.); (C.I.-R.); (E.I.A.B.)
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
| | - Cătălina Iovoaica-Rămescu
- Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (I.-A.E.); (C.I.-R.); (E.I.A.B.)
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
| | - Ștefan Gabriel Ciobanu
- Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (I.-A.E.); (C.I.-R.); (E.I.A.B.)
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
| | - Elena Iuliana Anamaria Berbecaru
- Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (I.-A.E.); (C.I.-R.); (E.I.A.B.)
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
| | - Andreea Vochin
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
| | - Ionuț Daniel Băluță
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
| | - Anca Maria Istrate-Ofițeru
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
- Ginecho Clinic, Medgin SRL, 200333 Craiova, Romania
- Research Centre for Microscopic Morphology and Immunology, University of Medicine and Pharmacy of Craiova, 200642 Craiova, Romania
| | - Cristina Maria Comănescu
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
- Ginecho Clinic, Medgin SRL, 200333 Craiova, Romania
- Department of Anatomy, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Rodica Daniela Nagy
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
- Ginecho Clinic, Medgin SRL, 200333 Craiova, Romania
| | - Dominic Gabriel Iliescu
- Department of Obstetrics and Gynecology, University Emergency County Hospital, 200642 Craiova, Romania; (A.V.); (I.D.B.); (A.M.I.-O.); (C.M.C.); (R.D.N.); (D.G.I.)
- Ginecho Clinic, Medgin SRL, 200333 Craiova, Romania
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
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Yousefpour Shahrivar R, Karami F, Karami E. Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches. Biomimetics (Basel) 2023; 8:519. [PMID: 37999160 PMCID: PMC10669151 DOI: 10.3390/biomimetics8070519] [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: 08/29/2023] [Revised: 10/05/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.
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Affiliation(s)
- Ramin Yousefpour Shahrivar
- Department of Biology, College of Convergent Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, 14515-775, Iran
| | - Fatemeh Karami
- Department of Medical Genetics, Applied Biophotonics Research Center, Science and Research Branch, Islamic Azad University, Tehran, 14515-775, Iran
| | - Ebrahim Karami
- Department of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
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Mohsin SN, Gapizov A, Ekhator C, Ain NU, Ahmad S, Khan M, Barker C, Hussain M, Malineni J, Ramadhan A, Halappa Nagaraj R. The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases. Cureus 2023; 15:e44374. [PMID: 37664359 PMCID: PMC10469091 DOI: 10.7759/cureus.44374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.
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Affiliation(s)
| | | | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, USA
| | - Noor U Ain
- Medicine, Mayo Hospital, Lahore, PAK
- Medicine, King Edward Medical University, Lahore, PAK
| | | | - Mavra Khan
- Medicine and Surgery, Mayo Hospital, Lahore , PAK
| | - Chad Barker
- Public Health, University of South Florida, Tampa, USA
| | | | - Jahnavi Malineni
- Medicine and Surgery, Maharajah's Institute of Medical Sciences, Vizianagaram, IND
| | - Afif Ramadhan
- Medicine, Universal Scientific Education and Research Network (USERN), Yogyakarta, IDN
- Medicine, Faculty of Medicine, Public Health, and Nursing, Gadjah Mada University, Yogyakarta, IDN
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Ungureanu A, Marcu AS, Patru CL, Ruican D, Nagy R, Stoean R, Stoean C, Iliescu DG. Correction: Learning deep architectures for the interpretation of first‑trimester fetal echocardiography (LIFE) ‑ a study protocol for developing an automated intelligent decision support system for early fetal echocardiography. BMC Pregnancy Childbirth 2023; 23:497. [PMID: 37407933 DOI: 10.1186/s12884-023-05825-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2023] Open
Affiliation(s)
- Anda Ungureanu
- Department of Paediatric Cardiology, University Emergency County Hospital Craiova, Tabaci, no.1, Craiova, 200642, Romania
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy Craiova, Petru Rares, no. 2, Craiova, 200412, Romania
- Department of Obstetrics and Gynecology, University Emergency County Hospital Craiova, Romania Tabaci, no.1, Craiova, 200642, Romania
- MEDGIN / GINECHO Clinic, 1 Mai, no. 29, Craiova, 200333, Romania
| | - Andreea-Sorina Marcu
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy Craiova, Petru Rares, no. 2, Craiova, 200412, Romania
| | - Ciprian Laurentiu Patru
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy Craiova, Petru Rares, no. 2, Craiova, 200412, Romania.
- Department of Obstetrics and Gynecology, University Emergency County Hospital Craiova, Romania Tabaci, no.1, Craiova, 200642, Romania.
- MEDGIN / GINECHO Clinic, 1 Mai, no. 29, Craiova, 200333, Romania.
| | - Dan Ruican
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy Craiova, Petru Rares, no. 2, Craiova, 200412, Romania
- Department of Obstetrics and Gynecology, University Emergency County Hospital Craiova, Romania Tabaci, no.1, Craiova, 200642, Romania
- MEDGIN / GINECHO Clinic, 1 Mai, no. 29, Craiova, 200333, Romania
| | - Rodica Nagy
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy Craiova, Petru Rares, no. 2, Craiova, 200412, Romania
- Department of Obstetrics and Gynecology, University Emergency County Hospital Craiova, Romania Tabaci, no.1, Craiova, 200642, Romania
- MEDGIN / GINECHO Clinic, 1 Mai, no. 29, Craiova, 200333, Romania
| | - Ruxandra Stoean
- Romanian Institute of Science and Technology, Virgil Fulicea, no. 3, Cluj Napoca, 400022, Romania
- Department of Computer Science, University of Craiova, A.I. Cuza, 13, Craiova, 200585, Romania
| | - Catalin Stoean
- Romanian Institute of Science and Technology, Virgil Fulicea, no. 3, Cluj Napoca, 400022, Romania
- Department of Computer Science, University of Craiova, A.I. Cuza, 13, Craiova, 200585, Romania
| | - Dominic Gabriel Iliescu
- Department of Obstetrics and Gynecology, University of Medicine and Pharmacy Craiova, Petru Rares, no. 2, Craiova, 200412, Romania
- Department of Obstetrics and Gynecology, University Emergency County Hospital Craiova, Romania Tabaci, no.1, Craiova, 200642, Romania
- MEDGIN / GINECHO Clinic, 1 Mai, no. 29, Craiova, 200333, Romania
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Xiao S, Zhang J, Zhu Y, Zhang Z, Cao H, Xie M, Zhang L. Application and Progress of Artificial Intelligence in Fetal Ultrasound. J Clin Med 2023; 12:jcm12093298. [PMID: 37176738 PMCID: PMC10179567 DOI: 10.3390/jcm12093298] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 04/01/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.
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Affiliation(s)
- Sushan Xiao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Junmin Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Ye Zhu
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Zisang Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Haiyan Cao
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Mingxing Xie
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Li Zhang
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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