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Sivera R, Clark AE, Dall'Asta A, Ghi T, Schievano S, Lees CC. Fetal face shape analysis from prenatal 3D ultrasound images. Sci Rep 2024; 14:4411. [PMID: 38388522 PMCID: PMC10884000 DOI: 10.1038/s41598-023-50386-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/19/2023] [Indexed: 02/24/2024] Open
Abstract
3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.
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Affiliation(s)
- Raphael Sivera
- Institute of Cardiovascular Science, University College London, London, UK
| | - Anna E Clark
- Institute of Reproductive and Development Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Andrea Dall'Asta
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Tullio Ghi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Silvia Schievano
- Institute of Cardiovascular Science, University College London, London, UK
| | - Christoph C Lees
- Institute of Reproductive and Development Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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Miyagi Y, Hata T, Miyake T. Fetal brain activity and the free energy principle. J Perinat Med 2023; 51:925-931. [PMID: 37096665 DOI: 10.1515/jpm-2023-0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/12/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVES To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence. METHODS In this observational study, we used a four-dimensional ultrasound technique obtained to collect images of fetal faces from pregnancies at 27-37 weeks of gestation, between February and December 2021. We developed an artificial intelligence classifier that recognizes fetal facial expressions, which are thought to relate to fetal brain activity. We then applied the classifier to video files of facial images to generate each expression category's probabilities. We calculated the chaotic dimensions from the probability lists, and we created and investigated the free energy principle's mathematical model that was assumed to be linked to the chaotic dimension. We used a Mann-Whitney test, linear regression test, and one-way analysis of variance for statistical analysis. RESULTS The chaotic dimension revealed that the fetus had dense and sparse states of brain activity, which fluctuated at a statistically significant level. The chaotic dimension and free energy were larger in the sparse state than in the dense state. CONCLUSIONS The fluctuating free energy suggests consciousness seemed to exist in the fetus after 27 weeks.
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Affiliation(s)
- Yasunari Miyagi
- Department of Gynecology, Miyake Ofuku Clinic, Okayama, Japan
- Medical Data Labo, Okayama, Japan
| | - Toshiyuki Hata
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan
- Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Kagawa, Japan
| | - Takahito Miyake
- Department of Gynecology, Miyake Ofuku Clinic, Okayama, Japan
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan
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Tang J, Han J, Jiang Y, Xue J, Zhou H, Hu L, Chen C, Lu L. An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal Ultrasound. Bioengineering (Basel) 2023; 10:873. [PMID: 37508900 PMCID: PMC10376765 DOI: 10.3390/bioengineering10070873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/25/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model's ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals.
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Affiliation(s)
- Jiajie Tang
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Jin Han
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Graduate School, Guangzhou Medical University, Guangzhou 511495, China
| | - Yuxuan Jiang
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- School of Information Management, Wuhan University, Wuhan 430072, China
- Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan 430072, China
| | - Jiaxin Xue
- Graduate School, Guangzhou Medical University, Guangzhou 511495, China
| | - Hang Zhou
- Graduate School, Guangzhou Medical University, Guangzhou 511495, China
| | - Lianting Hu
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangzhou 510317, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou 510317, China
| | - Caiyuan Chen
- Graduate School, Guangzhou Medical University, Guangzhou 511495, China
| | - Long Lu
- Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- School of Information Management, Wuhan University, Wuhan 430072, China
- School of Public Health, Wuhan University, Wuhan 430072, China
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Tang J, Han J, Xue J, Zhen L, Yang X, Pan M, Hu L, Li R, Jiang Y, Zhang Y, Jing X, Li F, Chen G, Zhang K, Zhu F, Liao C, Lu L. A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound. Biomedicines 2023; 11:1756. [PMID: 37371851 DOI: 10.3390/biomedicines11061756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 05/25/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
A global survey indicates that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses can only be performed after babies are born. Abnormal facial characteristics have been identified in various genetic diseases; however, current facial identification technologies cannot be applied to prenatal diagnosis. We developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect high-risk fetuses affected by a variety of genetic diseases. In screening for Trisomy 21, Trisomy 18, Trisomy 13, and rare genetic diseases, Pgds-ResNet achieved sensitivities of 0.83, 0.92, 0.75, and 0.96, and specificities of 0.94, 0.93, 0.95, and 0.92, respectively. As shown in heatmaps, the abnormalities detected by Pgds-ResNet are consistent with clinical reports. In a comparative experiment, the performance of Pgds-ResNet is comparable to that of experienced sonographers. This fetal genetic screening technology offers an opportunity for early risk assessment and presents a non-invasive, affordable, and complementary method to identify high-risk fetuses affected by genetic diseases. Additionally, it has the capability to screen for certain rare genetic conditions, thereby enhancing the clinic's detection rate.
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Affiliation(s)
- Jiajie Tang
- School of Information Management, Wuhan University, Wuhan 430072, China
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Obstetrics and Gynecology Medical Center, Dongguan Kanghua Hospital, Dongguan 523080, China
- Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan 430072, China
| | - Jin Han
- School of Information Management, Wuhan University, Wuhan 430072, China
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Obstetrics and Gynecology Medical Center, Dongguan Kanghua Hospital, Dongguan 523080, China
| | - Jiaxin Xue
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Li Zhen
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Xin Yang
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Min Pan
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Lianting Hu
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangzhou 510317, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou 510317, China
| | - Ru Li
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Yuxuan Jiang
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Yongling Zhang
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Xiangyi Jing
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Fucheng Li
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Guilian Chen
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Kanghui Zhang
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Fanfan Zhu
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Can Liao
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan 430072, China
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan 430072, China
- School of Public Health, Wuhan University, Wuhan 430072, China
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Fiorentino MC, Villani FP, Di Cosmo M, Frontoni E, Moccia S. A review on deep-learning algorithms for fetal ultrasound-image analysis. Med Image Anal 2023; 83:102629. [PMID: 36308861 DOI: 10.1016/j.media.2022.102629] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 07/12/2022] [Accepted: 09/10/2022] [Indexed: 11/07/2022]
Abstract
Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. A number of survey papers in the field is today available, but most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 153 research papers published after 2017. Papers are analyzed and commented from both the methodology and the application perspective. We categorized the papers into (i) fetal standard-plane detection, (ii) anatomical structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. In addition, emerging applications are also outlined. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into actual clinical practice.
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Affiliation(s)
| | | | - Mariachiara Di Cosmo
- Department of Information Engineering, Università Politecnica delle Marche, Italy
| | - Emanuele Frontoni
- Department of Information Engineering, Università Politecnica delle Marche, Italy; Department of Political Sciences, Communication and International Relations, Università degli Studi di Macerata, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Italy
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Hata T, Ahmed Mostafa AboEllail M, Miyake T, Kanenishi K. Does fetus feel stress or pain on uterine contraction? J Perinat Med 2022:jpm-2022-0514. [PMID: 36480468 DOI: 10.1515/jpm-2022-0514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Toshiyuki Hata
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan.,Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Miki, Kagawa, Japan
| | | | - Takahito Miyake
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama, Japan.,Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Miki, Kagawa, Japan
| | - Kenji Kanenishi
- Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Miki, Kagawa, Japan
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Miyagi Y, Hata T, Bouno S, Koyanagi A, Miyake T. Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions. Int J Gynaecol Obstet 2022; 161:877-885. [PMID: 36352833 DOI: 10.1002/ijgo.14569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/22/2022] [Accepted: 11/01/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To examine whether artificial intelligence can achieve discoveries regarding fetal brain activity. METHODS In this observational study, the authors collected images of fetal faces using a four-dimensional ultrasound technique obtained from singleton pregnancies of outpatients in routine practice at 27 to 37 weeks of gestation between February 1 and December 31, 2021. The authors developed an artificial intelligence classifier to recognize seven facial expressions of fetuses, then applied it to video files of fetal facial images to generate the probabilities, as confidence scores, of each expression category. Discrete Fourier transform and chaotic analysis were used to investigate the scores. Mann-Whitney test, t test, variance test, and one-way analysis of variance were used for statistical analysis. RESULTS Facial expression changes were observed in cycles averaging 66 to 73 s. The power spectrum showed that mouthing and neutral expressions were the most prevalent. There was a difference between categories for the spectrum (p = 0.004). Two different states--dense and sparse--of confidence scores were discovered. The correlation dimension was 1.19 ± 0.22 and 1.33 ± 0.27 for dense and sparse, respectively (p = 0.047). CONCLUSION This method objectively and quantitatively demonstrated fetal brain activity and may provide insight into how the fetus spends its time in utero.
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Affiliation(s)
- Yasunari Miyagi
- Department of Gynecology, Miyake Ofuku Clinic, Okayama City, Okayama Prefecture, Japan
- Medical Data Labo, Okayama City, Okayama Prefecture, Japan
- Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka City, Saitama Prefecture, Japan
| | - Toshiyuki Hata
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama City, Okayama Prefecture, Japan
- Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Miki Town, Kagawa Prefecture, Japan
| | - Saori Bouno
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama City, Okayama Prefecture, Japan
| | - Aya Koyanagi
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama City, Okayama Prefecture, Japan
| | - Takahito Miyake
- Department of Gynecology, Miyake Ofuku Clinic, Okayama City, Okayama Prefecture, Japan
- Department of Obstetrics and Gynecology, Miyake Clinic, Okayama City, Okayama Prefecture, Japan
- Department of Perinatology and Gynecology, Kagawa University Graduate School of Medicine, Miki Town, Kagawa Prefecture, Japan
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Alzubaidi M, Agus M, Alyafei K, Althelaya KA, Shah U, Abd-Alrazaq AA, Anbar M, Makhlouf M, Househ M. Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images. iScience 2022; 25:104713. [PMID: 35856024 PMCID: PMC9287600 DOI: 10.1016/j.isci.2022.104713] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/09/2022] [Accepted: 06/28/2022] [Indexed: 11/26/2022] Open
Abstract
Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the guidelines for PRISMA. We conducted a comprehensive search of eight bibliographic databases. Out of 1269 studies 107 are included. We found that 2D ultrasound images were more popular (88) than 3D and 4D ultrasound images (19). Classification is the most used method (42), followed by segmentation (31), classification integrated with segmentation (16) and other miscellaneous methods such as object-detection, regression, and reinforcement learning (18). The most common areas that gained traction within the pregnancy domain were the fetus head (43), fetus body (31), fetus heart (13), fetus abdomen (10), and the fetus face (10). This survey will promote the development of improved AI models for fetal clinical applications. Artificial intelligence studies to monitor fetal development via ultrasound images Fetal issues categorized based on four categories — general, head, heart, face, abdomen The most used AI techniques are classification, segmentation, object detection, and RL The research and practical implications are included.
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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Abstract
This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.
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