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Mennickent D, Rodríguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14:1130139. [PMID: 37274341 PMCID: PMC10235786 DOI: 10.3389/fendo.2023.1130139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
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Affiliation(s)
- Daniela Mennickent
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Andrés Rodríguez
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Chillán, Chile
| | - Ma. Cecilia Opazo
- Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas, Santiago, Chile
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
| | - Claudia A. Riedel
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
- Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Erica Castro
- Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama, Copiapó, Chile
| | - Alma Eriz-Salinas
- Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Javiera Appel-Rubio
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Claudio Aguayo
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Alicia E. Damiano
- Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Enrique Guzmán-Gutiérrez
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Juan Araya
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
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Zhao F, Dong D, Du H, Guo Y, Su X, Wang Z, Xie X, Wang M, Zhang H, Cao X, He X. Diagnosis of endometrium hyperplasia and screening of endometrial intraepithelial neoplasia in histopathological images using a global-to-local multi-scale convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106906. [PMID: 35671602 DOI: 10.1016/j.cmpb.2022.106906] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/10/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Endometrial hyperplasia (EH), a uterine pathology characterized by an increased gland-to-stroma ratio compared to normal endometrium (NE), may precede the development of endometrial cancer (EC). Particularly, atypical EH also known as endometrial intraepithelial neoplasia (EIN), has been proven to be a precursor of EC. Thus, diagnosing different EH (EIN, hyperplasia without atypia (HwA) and NE) and screening EIN from non-EIN are crucial for the health of female reproductive system. Computer-aided-diagnosis (CAD) was used to diagnose endometrial histological images based on machine learning and deep learning. However, these studies perform single-scale image analysis and thus can only characterize partial endometrial features. Empirically, both global (cytological changes relative to background) and local features (gland-to-stromal ratio and lesion dimension) are helpful in identifying endometrial lesions. METHODS We proposed a global-to-local multi-scale convolutional neural network (G2LNet) to diagnose different EH and to screen EIN in endometrial histological images stained by hematoxylin and eosin (H&E). The G2LNet first used a supervised model in the global part to extract contextual features of endometrial lesions, and simultaneously deployed multi-instance learning in the local part to obtain textural features from multiple image patches. The contextual and textural features were used together to diagnose different endometrial lesions after fusion by a convolutional block attention module. In addition, we visualized the salient regions on both the global image and local images to investigate the interpretability of the model in endometrial diagnosis. RESULTS In the five-fold cross validation on 7812 H&E images from 467 endometrial specimens, G2LNet achieved an accuracy of 97.01% for EH diagnosis and an area-under-the-curve (AUC) of 0.9902 for EIN screening, significantly higher than state-of-the-arts. In external validation on 1631 H&E images from 135 specimens, G2LNet achieved an accuracy of 95.34% for EH diagnosis, which was comparable to that of a mid-level pathologist (95.71%). Specifically, G2LNet had advantages in diagnosing EIN, while humans performed better in identifying NE and HwA. CONCLUSIONS The developed G2LNet that integrated both the global (contextual) and local (textural) features may help pathologists diagnose endometrial lesions in clinical practices, especially to improve the accuracy and efficiency of screening for precancerous lesions.
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Affiliation(s)
- Fengjun Zhao
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Didi Dong
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Hongyan Du
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China.
| | - Yinan Guo
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Xue Su
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Zhiwei Wang
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Xiaoyang Xie
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Mingjuan Wang
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Haiyan Zhang
- Department of Pathology, Northwest Women and Children's Hospital, Xi'an, Shaanxi 710061, China
| | - Xin Cao
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China
| | - Xiaowei He
- Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China.
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Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N. COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:149808-149824. [PMID: 34931154 PMCID: PMC8668160 DOI: 10.1109/access.2020.3016780] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 08/11/2020] [Indexed: 05/02/2023]
Abstract
Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.
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Affiliation(s)
- Michael J. Horry
- Centre for Advanced Modelling and
Geospatial Information Systems (CAMGIS), School of Information, Systems, and
Modeling, Faculty of Engineering and ITUniversity of Technology
SydneySydneyNSW2007Australia
- IBM Australia LimitedSydneyNSW2065Australia
| | - Subrata Chakraborty
- Centre for Advanced Modelling and
Geospatial Information Systems (CAMGIS), School of Information, Systems, and
Modeling, Faculty of Engineering and ITUniversity of Technology
SydneySydneyNSW2007Australia
| | - Manoranjan Paul
- Machine Vision and Digital Health (MaViDH),
School of Computing and MathematicsCharles Sturt UniversityBathurstNSW2795Australia
| | - Anwaar Ulhaq
- Machine Vision and Digital Health (MaViDH),
School of Computing and MathematicsCharles Sturt UniversityBathurstNSW2795Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and
Geospatial Information Systems (CAMGIS), School of Information, Systems, and
Modeling, Faculty of Engineering and ITUniversity of Technology
SydneySydneyNSW2007Australia
- Department of Energy and Mineral
Resources EngineeringSejong UniversitySeoul05006South Korea
| | - Manas Saha
- Manning Rural Referral
HospitalTareeNSW2430Australia
| | - Nagesh Shukla
- Centre for Advanced Modelling and
Geospatial Information Systems (CAMGIS), School of Information, Systems, and
Modeling, Faculty of Engineering and ITUniversity of Technology
SydneySydneyNSW2007Australia
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Chou SY, Chan C, Lee YC, Yu TN, Tzeng CR, Chen CH. Evaluation of adenomyosis after gonadotrophin-releasing hormone agonist therapy using ultrasound post-processing imaging: a pilot study. J Int Med Res 2020; 48:300060520920056. [PMID: 32536293 PMCID: PMC7297488 DOI: 10.1177/0300060520920056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objective We explored a method for the quantitative sonographic analysis of myometrial texture using computer-aided image analysis software to assess outcomes following treatment with gonadotrophin-releasing hormone (GnRH) agonist for adenomyosis in women with infertility. Method Data for patients with ultrasound images of the myometrium obtained at Taipei Medical University Hospital from 1 September 2018 to 5 April 5 2019 were analyzed. Only 10 patients with 20 ultrasound images matched the eligibility criteria. The images were divided into pre-treatment (n = 10) and post-treatment images (n = 10) and quantitative grayscale histograms were obtained from the ultrasound images using publicly available ImageJ computer-aided image analysis software. We analyzed the differences between the pre- and post-treatment images using the Mann–Whitney test and compared the results with outcomes assessed by serum CA-125 levels. Results Image analysis of the grayscale histograms revealed significant differences between before and after treatment. The classification of the myometrium pre-treatment and post-treatment was similar using CA-125 and histogram grayscale analysis. Conclusion Computer-aided image analysis of grayscale histograms of the myometrium obtained from ultrasound images is an alternative method for assessing myometrial conditions after GnRH agonist treatment in patients with adenomyosis.
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Affiliation(s)
- Szu-Yuan Chou
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital,Taipei
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei
| | - Cindy Chan
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital,Taipei
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei
| | - Yu-Chieh Lee
- Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei
| | - Tzu-Ning Yu
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital,Taipei
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei
| | - Chii-Ruey Tzeng
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital,Taipei
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei
| | - Chi-Huang Chen
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital,Taipei
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei
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Qian L, Wu JY, DiMaio SP, Navab N, Kazanzides P. A Review of Augmented Reality in Robotic-Assisted Surgery. ACTA ACUST UNITED AC 2020. [DOI: 10.1109/tmrb.2019.2957061] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Sun H, Zeng X, Xu T, Peng G, Ma Y. Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms. IEEE J Biomed Health Inform 2019; 24:1664-1676. [PMID: 31581102 DOI: 10.1109/jbhi.2019.2944977] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationships between histopathological images and their interpretations, existing computer-aided diagnosis (CAD) approaches using traditional machine learning algorithms often failed to achieve satisfying results. In this study, we develop a CAD approach based on a convolutional neural network (CNN) and attention mechanisms, called HIENet. In the ten-fold cross-validation on ∼3,300 hematoxylin and eosin (H&E) image patches from ∼500 endometrial specimens, HIENet achieved a 76.91 ± 1.17% (mean ± s. d.) accuracy for four classes of endometrial tissue, i.e., normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet obtained an area-under-the-curve (AUC) of 0.9579 ± 0.0103 with an 81.04 ± 3.87% sensitivity and 94.78 ± 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma. Besides, in the external validation on 200 H&E image patches from 50 randomly-selected female patients, HIENet achieved an 84.50% accuracy in the four-class classification task, as well as an AUC of 0.9829 with a 77.97% (95% confidence interval, CI, 65.27%∼87.71%) sensitivity and 100% (95% CI, 97.42%∼100.00%) specificity. The proposed CAD method outperformed three human experts and five CNN-based classifiers regarding overall classification performance. It was also able to provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local pixel-level image features to morphological characteristics of endometrial tissue.
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Abstract
INTRODUCTION Craniosynostosis, the premature fusion of ≥1 cranial sutures, is the leading cause of pediatric skull deformities, affecting 1 of every 2000 to 2500 live births worldwide. Technologies used for the management of craniofacial conditions, specifically in craniosynostosis, have been advancing dramatically. This article highlights the most recent technological advances in craniosynostosis surgery through a systematic review of the literature. METHODS A systematic electronic search was performed using the PubMed database. Search terms used were "craniosynostosis" AND "technology" OR "innovation" OR "novel.' Two independent reviewers subsequently reviewed the resultant articles based on strict inclusion and exclusion criteria. Selected manuscripts deemed novel by the senior authors were grouped by procedure categories. RESULTS Following review of the PubMed database, 28 of 536 articles were retained. Of the 28 articles, 20 articles consisting of 21 technologies were deemed as being novel by the senior authors. The technologies were categorized as diagnostic imaging (n = 6), surgical planning (n = 4), cranial vault evaluation (n = 4), machine learning (n = 3), ultrasound pinning (n = 3), and near-infrared spectroscopy (n = 1). CONCLUSION Multiple technological advances have impacted the treatment of craniosynostosis. These innovations include improvement in diagnosis and objective measurement of craniosynostosis, preoperative planning, intraoperative procedures, communication between both surgeons and patients, and surgical education.
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