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Huang H, Shi Y, Hong Y, Zhu L, Li M, Zhang Y. A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database. Front Pediatr 2024; 12:1357972. [PMID: 39301040 PMCID: PMC11410630 DOI: 10.3389/fped.2024.1357972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 08/20/2024] [Indexed: 09/22/2024] Open
Abstract
Introduction The objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea. Methods We retrospectively analysed 8024 newborns from the MIMIC IV database, building logistic regression models and decision tree models. The performance of the model is examined by decision curves, calibration curves and ROC curves. Variables were screened by stepwise logistic regression analysis and LASSO regression. Results A total of 7 indicators were ultimately included in the model: gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes and acetaminophen. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the logistic regression model in the training set and the AUC in the validation set are 0.879 and 0.865, respectively. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the decision tree model in the training set and the AUC in the validation set are 0.861 and 0.850, respectively. The calibration and decision curves in the two cohorts also demonstrated satisfactory predictive performance of the model. However, the logistic regression model performs relatively well. Discussion Our results proved that blood indicators were valuable and effective predictors of neonatal apnea, which could provide effective predictive information for medical staff.
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Affiliation(s)
- Huisi Huang
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yanhong Shi
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yinghui Hong
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lizhen Zhu
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Mengyao Li
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yue Zhang
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Boddupally K, Rani Thuraka E. Artificial intelligence for prenatal chromosome analysis. Clin Chim Acta 2024; 552:117669. [PMID: 38007058 DOI: 10.1016/j.cca.2023.117669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023]
Abstract
This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.
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Affiliation(s)
- Kavitha Boddupally
- JNTUH University, India; CVR College of Engineering, ECE, Hyderabad, India.
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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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Ji C, Liu K, Yang X, Cao Y, Cao X, Pan Q, Yang Z, Sun L, Yin L, Deng X, Ni D. A novel artificial intelligence model for fetal facial profile marker measurement during the first trimester. BMC Pregnancy Childbirth 2023; 23:718. [PMID: 37817098 PMCID: PMC10563312 DOI: 10.1186/s12884-023-06046-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 10/03/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND To study the validity of an artificial intelligence (AI) model for measuring fetal facial profile markers, and to evaluate the clinical value of the AI model for identifying fetal abnormalities during the first trimester. METHODS This retrospective study used two-dimensional mid-sagittal fetal profile images taken during singleton pregnancies at 11-13+ 6 weeks of gestation. We measured the facial profile markers, including inferior facial angle (IFA), maxilla-nasion-mandible (MNM) angle, facial-maxillary angle (FMA), frontal space (FS) distance, and profile line (PL) distance using AI and manual measurements. Semantic segmentation and landmark localization were used to develop an AI model to measure the selected markers and evaluate the diagnostic value for fetal abnormalities. The consistency between AI and manual measurements was compared using intraclass correlation coefficients (ICC). The diagnostic value of facial markers measured using the AI model during fetal abnormality screening was evaluated using receiver operating characteristic (ROC) curves. RESULTS A total of 2372 normal fetuses and 37 with abnormalities were observed, including 18 with trisomy 21, 7 with trisomy 18, and 12 with CLP. Among them, 1872 normal fetuses were used for AI model training and validation, and the remaining 500 normal fetuses and all fetuses with abnormalities were used for clinical testing. The ICCs (95%CI) of the IFA, MNM angle, FMA, FS distance, and PL distance between the AI and manual measurement for the 500 normal fetuses were 0.812 (0.780-0.840), 0.760 (0.720-0.795), 0.766 (0.727-0.800), 0.807 (0.775-0.836), and 0.798 (0.764-0.828), respectively. IFA clinically significantly identified trisomy 21 and trisomy 18, with areas under the ROC curve (AUC) of 0.686 (95%CI, 0.585-0.788) and 0.729 (95%CI, 0.621-0.837), respectively. FMA effectively predicted trisomy 18, with an AUC of 0.904 (95%CI, 0.842-0.966). MNM angle and FS distance exhibited good predictive value in CLP, with AUCs of 0.738 (95%CI, 0.573-0.902) and 0.677 (95%CI, 0.494-0.859), respectively. CONCLUSIONS The consistency of fetal facial profile marker measurements between the AI and manual measurement was good during the first trimester. The AI model is a convenient and effective tool for the early screen for fetal trisomy 21, trisomy 18, and CLP, which can be generalized to first-trimester scanning (FTS).
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Affiliation(s)
- Chunya Ji
- Center for Medical Ultrasound, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Kai Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Xueyuan Blvd, Nanshan, Shenzhen, Guangdong, China
| | - Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Xueyuan Blvd, Nanshan, Shenzhen, Guangdong, China
| | - Yan Cao
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Xueyuan Blvd, Nanshan, Shenzhen, Guangdong, China
- Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xiaoju Cao
- Center for Reproduction and Genetics, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, No. 26 Daoqian Street, Suzhou, 215002, Jiangsu, China
| | - Qi Pan
- Center for Medical Ultrasound, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Zhong Yang
- Center for Medical Ultrasound, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Lingling Sun
- Center for Medical Ultrasound, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Linliang Yin
- Center for Medical Ultrasound, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, Jiangsu, China.
| | - Xuedong Deng
- Center for Medical Ultrasound, Suzhou Municipal Hospital, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University, Suzhou, Jiangsu, China.
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Xueyuan Blvd, Nanshan, Shenzhen, Guangdong, China.
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Hajiahmadi S, Adariani AR, Amini E, Rasti S. Reference values for ductus venosus Doppler velocity indices between 11 and 13+6 weeks of gestation: A single-center prospective study in Iran. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2023; 28:55. [PMID: 37496642 PMCID: PMC10366976 DOI: 10.4103/jrms.jrms_808_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/12/2023] [Accepted: 03/22/2023] [Indexed: 07/28/2023]
Abstract
Background This study aimed to investigate reference Doppler velocimetry indices (DVIs) of the fetal ductus venosus (DV) during 11-13 + 6 gestational weeks. Materials and Methods In a prospective observation over referrals to a single tertiary care center in a 2-year interval, normal singleton pregnancies with fetal crown-rump lengths (CRLs) of 43-80 mm were examined by a single experienced sonographer for their DV pulsatility index (DVPI), DV resistance index (DVRI), and S-wave maximum velocity/A-wave minimum velocity (S/A ratio). Multinomial and quantile regression functions were used to analyze the effect of gestational age (estimated by CRL) on reference values (5th and 95th percentiles of the distribution in each gestational day/week). P < 0.05 was considered significant. Results Over a sample of 415 participants with a mean/median gestational age of 12 + 1 weeks, no significant correlations were found between the CRL and DVIs using multinomial regression functions (linear model best fitted for all [DVPI: B coefficient = 0.001, P = 0.235] [DVRI: B coefficient = 0.001, P = 0.287] [DV S/A: B coefficient = 0.010, P = 283]). Quantile regression analyses of DVIs' reference values were nonsignificant across the CRL range except for the DVRI ([5th regression line: coefficient = -0.004, P = 0.018] [95th regression line: coefficient = -0.001, P = 0.030]). Conclusion Reference values for DVPI, DVRI, and DV S/A ratios were established as 0.80-1.39, 0.62-0.88, and 2.57-6.70, respectively. Future meta-analyses and multicenter studies are required to incorporate DV DVIs into an updated universal version of the practice.
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Affiliation(s)
- Somayeh Hajiahmadi
- Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Ehsan Amini
- Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sina Rasti
- Department of Radiology, Isfahan University of Medical Sciences, Isfahan, Iran
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Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S. A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. Diagnostics (Basel) 2022; 12:2643. [PMID: 36359487 PMCID: PMC9689292 DOI: 10.3390/diagnostics12112643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 11/25/2023] Open
Abstract
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.
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Affiliation(s)
- Deepti Verma
- Department of Computer Application, SAGE University, Indore 452020, India
| | - Shweta Agrawal
- Institute of Advance Computing, SAGE University, Indore 452020, India
| | - Celestine Iwendi
- School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
| | - Bhisham Sharma
- Department of Computer Science & Engineering, School of Engineering and Technology, Chitkara University, Baddi 174103, India
| | - Surbhi Bhatia
- Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 36362, Saudi Arabia
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
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Ji C, Jiang X, Yin L, Deng X, Yang Z, Pan Q, Zhang J, Liang Q. Ultrasonographic study of fetal facial profile markers during the first trimester. BMC Pregnancy Childbirth 2021; 21:324. [PMID: 33894762 PMCID: PMC8070329 DOI: 10.1186/s12884-021-03813-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/20/2021] [Indexed: 01/20/2023] Open
Abstract
Background To establish reference ranges of fetal facial profile markers and study their correlations with crown-rump length (CRL) during the first trimester (11 ~ 13+ 6 weeks’ gestation) in a Chinese population. Methods Ultrasonographic images of measuring fetal nuchal translucency (NT) were retrospectively selected randomly in normal fetuses whose parents were both Chinese. The facial markers included inferior facial angle (IFA), maxilla-nasion-mandible (MNM) angle, facial maxillary angle (FMA) and profile line (PL) distance. These markers were measured through ViewPoint 6 software by two experienced sonographers. Results Three hundred and eighty fetuses were selected. The ICCs (95 % CI) of intra-operator 1 reproducibility of IFA, MNM angle, FMA, PL distance were 0.944 (0.886 ~ 0.973), 0.804 (0.629 ~ 0.902), 0.834 (0.68 ~ 0.918) and 0.935 (0.868 ~ 0.969), respectively. The ICCs (95 % CI) of intra-operator 2 reproducibility of IFA, MNM angle, FMA, PL distance were 0.931 (0.857 ~ 0.967), 0.809 (0.637 ~ 0.904), 0.786 (0.600 ~ 0.892) and 0.906 (0.813 ~ 0.954), respectively. The ICCs (95 % CI) of inter-operator reproducibility of IFA, MNM angle, FMA, PL distance were 0.885 (0.663 ~ 0.953), 0.829 (0.672 ~ 0.915), 0.77 (0.511 ~ 0.891) and 0.844 (0.68 ~ 0.925), respectively. The average ± SD of IFA, MNM angle, FMA and PL distance were 80.2°±7.25°, 4.17°±1.19°, 75.36°±5.31°, 2.78 ± 0.54 mm, respectively. IFA and PL distance significantly decreased with CRL, while MNM angle and FMA significantly increased with CRL. Conclusions It was feasible to measure fetal facial markers during the first trimester. In Chinese population, the reference ranges of IFA, MNM angle, FMA and PL distance were 80.2°±7.25°, 4.17°±1.19°, 75.36°±5.31°, 2.78 ± 0.54 mm, respectively, and the measurements were found to correlate with CRL.
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Affiliation(s)
- Chunya Ji
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China
| | - Xiaoli Jiang
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China
| | - Linliang Yin
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China.
| | - Xuedong Deng
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China.
| | - Zhong Yang
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China
| | - Qi Pan
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China
| | - Jun Zhang
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China
| | - Qing Liang
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, 215002, Suzhou, Jiangsu, China
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Chen Y, Chen Y, Wang X, Chu X, Ning W, Gu L, Li L, Xie Z, Wen C. Second trimester maternal serum D-dimer combined with alpha-fetoprotein and free β-subunit of human chorionic gonadotropin predict hypertensive disorders of pregnancy: a systematic review and retrospective case-control study. J Transl Med 2021; 19:94. [PMID: 33653375 PMCID: PMC7927388 DOI: 10.1186/s12967-021-02718-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/25/2021] [Indexed: 02/02/2023] Open
Abstract
Background This study investigated whether maternal serum D-dimer (DD) alone or DD combined with alpha-fetoprotein (AFP) and free β-subunit of human chorionic gonadotropin (free β-hCG) in the second trimester could be used to predict hypertensive disorders of pregnancy (HDP). Materials and methods In this retrospective case–control study, the data of gravidas patients who delivered at hospital were divided into the following groups: control (n = 136), gestational hypertension (GH, n = 126), preeclampsia (PE, n = 53), and severe preeclampsia (SPE, n = 41). Receiver operator characteristic (ROC) curves were used to evaluate the diagnostic value of maternal serum DD, AFP, and free β-hCG levels for HDP. Results DD levels of the GH, PE, and SPE groups were significantly higher than that of the control group (P < 0.001). The order of effectiveness for models predicting HDP was as follows: DD + AFP + free β-hCG > DD > DD + AFP > DD + free β-hCG > AFP + free β-hCG > AFP > free β-hCG. For predicting different types of HDP, DD alone had the best diagnostic value for SPE, followed by PE and GH. DD alone had a sensitivity of 100% with a 0% false negative rate and had the highest positive likelihood ratio (+ LR) for SPE. DD alone in combination with AFP alone, free β-hCG alone and AFP + free β-hCG could reduce false positive rate and improve + LR. Conclusion DD is possible the best individual predictive marker for predicting HDP. Levels of DD alone in the second trimester were positively correlated with the progression of elevated blood pressure in the third trimester, demonstrating the predicting the occurrence of HDP. The risk calculation model constructed with DD + free β-hCG + AFP had the greatest diagnostic value for SPE.
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Affiliation(s)
- Yiming Chen
- Department of Prenatal Diagnosis and Screening Center, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital), No. 369, Kunpeng Road, Shangcheng District, Hangzhou, 310008, Zhejiang, China. .,Department of the Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China.
| | - Yijie Chen
- Department of the Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China
| | - Xue Wang
- Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Xuelian Chu
- Department of Laboratory, Maternal and Child Health Hospital of Yuhang District, Hangzhou, 311100, Zhejiang, China
| | - Wenwen Ning
- Department of the Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China
| | - Linyuan Gu
- Department of Laboratory, Maternal and Child Health Hospital of Yuhang District, Hangzhou, 311100, Zhejiang, China
| | - Liyao Li
- Department of Prenatal Diagnosis and Screening Center, Hangzhou Women's Hospital (Hangzhou Maternity and Child Health Care Hospital), No. 369, Kunpeng Road, Shangcheng District, Hangzhou, 310008, Zhejiang, China
| | - Zhen Xie
- Department of Obstetrics, Hangzhou Women's Hospital (Hangzhou Maternal and Child Health Care Hospital), Hangzhou, 310008, Zhejiang, China
| | - Caihe Wen
- Department of Obstetrics, Hangzhou Women's Hospital (Hangzhou Maternal and Child Health Care Hospital), Hangzhou, 310008, Zhejiang, China
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Roberto M, Pasqualone G, Di Donato NG, Malik Z, Bhaiyat S, Caponio VCA, Lillo G, Vallone G, Rinaldi CR, Martinelli V. A novel ultrasound-based approach to investigate extramedullary haematopoiesis in foetal spleen. AMERICAN JOURNAL OF BLOOD RESEARCH 2021; 11:84-92. [PMID: 33796394 PMCID: PMC8010597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 02/04/2021] [Indexed: 06/12/2023]
Abstract
Foetal spleen is described as a transient focus of haematopoiesis between the 3rd and 5th month of gestation: this function is however entirely replaced by the bone marrow before the end of pregnancy. This study identifies haematopoiesis in foetal spleen by exploring changes of echogenicity during its development throughout gestation. Two intervals of pregnancy were studied: Mid-Pregnancy (Mid-P, 19-23 weeks) and End-Pregnancy (End-P, 37-41 weeks). The foetal spleen was investigated in 80 pregnant women (41 vs 39). Due to quality criteria the comparison was made between 60 images (30 Mid-P vs 30 End-P). The acquisition of splenic parenchyma was followed by clustering segmentation. We identified two new parameters resulted from the clustering segmentation: Dark Ratio (DR) and Light Ratio (LR). These are related to splenic echogenicity expressing the percentage of dark and light signal in the clustered image, influenced by blood cellularity. The mean of DR value was different among the 2 groups (0.0631 vs 0.0483, P = 0.014), while LR did not show any significant differences. We conclude that DR may represent a reliable radiomic parameter in the determination of extramedullary haematopoiesis in the spleen.
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Affiliation(s)
- Marcello Roberto
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseCampobasso, Italy
| | - Gianmario Pasqualone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseCampobasso, Italy
| | - Nicola G Di Donato
- Obstetrics and Gynaecology Operating Unit, “Antonio Cardarelli” Hospital, Azienda Sanitaria Regionale del Molise (ASReM)Campobasso, Italy
| | - Zaheer Malik
- Haematology Department, Pilgrim Hospital, United Lincolnshire Hospitals Trust (ULHT)Boston, United Kingdom
| | - Sheereen Bhaiyat
- Haematology Department, Pilgrim Hospital, United Lincolnshire Hospitals Trust (ULHT)Boston, United Kingdom
| | - Vito CA Caponio
- Department of Clinical and Experimental Medicine, University of FoggiaFoggia, Italy
| | - Giuseppe Lillo
- Master’s Degree in Computer Engineering at Polytechnic University of TurinTorino, Italy
| | - Gianfranco Vallone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseCampobasso, Italy
| | - Ciro R Rinaldi
- Joseph Banks Laboratories, School of Life Sciences, College of Science, University of LincolnLincoln, United Kingdom
- Haematology Department, Pilgrim Hospital, United Lincolnshire Hospitals Trust (ULHT)Boston, United Kingdom
| | - Vincenzo Martinelli
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of MoliseCampobasso, Italy
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