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Sun B, Fang Y, Yang H, Meng F, He C, Zhao Y, Zhao K, Zhang H. The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening. Talanta 2024; 275:126109. [PMID: 38648686 DOI: 10.1016/j.talanta.2024.126109] [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: 02/05/2024] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC = 0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC = 0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.
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Affiliation(s)
- Borui Sun
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yiwei Fang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China; Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, No. 49, North Garden Road, Haidian district, Beijing, 100191, China; National Clinical Research Center for Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China; State Key Laboratory of Female Fertility Promotion, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China; Key Laboratory of Assisted Reproduction, Ministry of Education, Peking University, Beijing, 100191, China; Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, 100191, China.
| | - Hui Yang
- Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China
| | - Fan Meng
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Chao He
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yun Zhao
- Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China.
| | - Kai Zhao
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Huiping Zhang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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Bamba H, Singh G, John J, Inban P, Prajjwal P, Alhussain H, Marsool MDM. Precision Medicine Approaches in Cardiology and Personalized Therapies for Improved Patient Outcomes: A systematic review. Curr Probl Cardiol 2024; 49:102470. [PMID: 38369209 DOI: 10.1016/j.cpcardiol.2024.102470] [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: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
Personalized medicine is a novel and rapidly evolving approach to clinical practice that involves making decisions about disease prediction, prevention, diagnosis, and treatment by utilizing modern technologies. The concepts of precision medicine have grown as a result of ongoing developments in genomic analysis, molecular diagnostics, and technology. These advancements have enabled a deeper understanding and interpretation of the human genome, allowing for a personalized approach to clinical care. The primary objective of this research is to assess personalized medicine in terms of its indications, advantages, practical clinical uses, potential future directions, problems, and effects on healthcare. An extensive analysis of the scientific literature regarding this topic demonstrated the new medical approach's relevance and usefulness, as well as the fact that personalized medicine is becoming increasingly prevalent in various sectors. The online, internationally indexed databases PubMed and Cochrane Reviews were used to conduct searches for and critically evaluate the most relevant published research including original papers and reviews in the scientific literature. The findings suggest that precision medicine has a lot of potential and its implementation lowers the incidence of stroke as well as coronary heart disease and improves patient health in cardiology.
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Affiliation(s)
- Hyma Bamba
- Cardiology, Government Medical College and Hospital, Chandigarh, India
| | - Gurmehar Singh
- Cardiology, Government Medical College and Hospital, Chandigarh, India
| | - Jobby John
- Cardiology, Dr. Somervell Memorial CSI Medical College and Hospital Karakonam, Trivandrum, India
| | | | | | - Haitham Alhussain
- Public Health and Infection Control dept, King Fahad Hospital, Alhofuf, Saudi Arabia
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Lettieri G, Marinaro C, Brogna C, Montano L, Lombardi M, Trotta A, Troisi J, Piscopo M. A Metabolomic Analysis to Assess the Responses of the Male Gonads of Mytilus galloprovincialis after Heavy Metal Exposure. Metabolites 2023; 13:1168. [PMID: 38132850 PMCID: PMC10744773 DOI: 10.3390/metabo13121168] [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] [Received: 10/13/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
In recent years, metabolomics has become a valuable new resource in environmental monitoring programs based on the use of bio-indicators such as Mytilus galloprovincialis. The reproductive system is extremely susceptible to the effects of environmental pollutants, and in a previous paper, we showed metabolomic alterations in mussel spermatozoa exposed to metal chlorides of copper, nickel, and cadmium, and the mixture with these metals. In order to obtain a better overview, in the present work, we evaluated the metabolic changes in the male gonad under the same experimental conditions used in the previous work, using a metabolomic approach based on GC-MS analysis. A total of 248 endogenous metabolites were identified in the male gonads of mussels. Statistical analyses of the data, including partial least squares discriminant analysis, enabled the identification of key metabolites through the use of variable importance in projection scores. Furthermore, a metabolite enrichment analysis revealed complex and significant interactions within different metabolic pathways and between different metabolites. Particularly significant were the results on pyruvate metabolism, glycolysis, and gluconeogenesis, and glyoxylate and dicarboxylate metabolism, which highlighted the complex and interconnected nature of these biochemical processes in mussel gonads. Overall, these results add new information to the understanding of how certain pollutants may affect specific physiological functions of mussel gonads.
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Affiliation(s)
- Gennaro Lettieri
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy
| | - Carmela Marinaro
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy
| | - Carlo Brogna
- Department of Research, Craniomed Group Facility S.r.l., 20091 Bresso, Italy
| | - Luigi Montano
- Andrology Unit and Service of LifeStyle Medicine in Uro-Andrology, Local Health Authority (ASL) Salerno, 84084 Salerno, Italy
| | - Martina Lombardi
- Theoreo S.r.l.—Spin-off Company, University of Salerno, 84084 Salerno, Italy
| | - Alessio Trotta
- Theoreo S.r.l.—Spin-off Company, University of Salerno, 84084 Salerno, Italy
| | - Jacopo Troisi
- Theoreo S.r.l.—Spin-off Company, University of Salerno, 84084 Salerno, Italy
| | - Marina Piscopo
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy
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Lettieri G, Marinaro C, Notariale R, Perrone P, Lombardi M, Trotta A, Troisi J, Piscopo M. Impact of Heavy Metal Exposure on Mytilus galloprovincialis Spermatozoa: A Metabolomic Investigation. Metabolites 2023; 13:943. [PMID: 37623886 PMCID: PMC10456258 DOI: 10.3390/metabo13080943] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 08/26/2023] Open
Abstract
Metabolomics is a method that provides an overview of the physiological and cellular state of a specific organism or tissue. This method is particularly useful for studying the influence the environment can have on organisms, especially those used as bio-indicators, e.g., Mytilus galloprovincialis. Nevertheless, a scarcity of data on the complete metabolic baseline of mussel tissues still exists, but more importantly, the effect of mussel exposure to certain heavy metals on spermatozoa is unknown, also considering that, in recent years, the reproductive system has proved to be very sensitive to the effects of environmental pollutants. In order to fill this knowledge gap, the similarities and differences in the metabolic profile of spermatozoa of mussels exposed to metallic chlorides of copper, nickel, and cadmium, and to the mixture to these metals, were studied using a metabolomics approach based on GC-MS analysis, and their physiological role was discussed. A total of 237 endogenous metabolites were identified in the spermatozoa of these mussel. The data underwent preprocessing steps and were analyzed using statistical methods such as PLS-DA. The results showed effective class separation and identified key metabolites through the VIP scores. Heatmaps and cluster analysis further evaluated the metabolites. The metabolite-set enrichment analysis revealed complex interactions within metabolic pathways and metabolites, especially involving glucose and central carbon metabolism and oxidative stress metabolism. Overall, the results of this study are useful to better understand how some pollutants can affect the specific physiological functions of the spermatozoa of this mussel, as well as for further GC-MS-based metabolomic health and safety studies of marine bivalves.
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Affiliation(s)
- Gennaro Lettieri
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy
| | - Carmela Marinaro
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy
| | - Rosaria Notariale
- Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale 1, 80121 Naples, Italy
| | - Pasquale Perrone
- Department of Precision Medicine, School of Medicine, University of Campania “Luigi Vanvitelli”, Via Luigi de Crecchio, 80138 Naples, Italy
| | - Martina Lombardi
- Theoreo S.R.L.—Spin-off Company of the University of Salerno, 84098 Montecorvino Pugliano (SA), Italy
| | - Alessio Trotta
- Theoreo S.R.L.—Spin-off Company of the University of Salerno, 84098 Montecorvino Pugliano (SA), Italy
| | - Jacopo Troisi
- Theoreo S.R.L.—Spin-off Company of the University of Salerno, 84098 Montecorvino Pugliano (SA), Italy
| | - Marina Piscopo
- Department of Biology, University of Naples Federico II, Via Cinthia, 21, 80126 Naples, Italy
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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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Mires S, Reddy S, Skerritt C, Caputo M, Eastwood K. Maternal metabolomic profiling and congenital heart disease risk in offspring: A systematic review of observational studies. Prenat Diagn 2023; 43:647-660. [PMID: 36617630 PMCID: PMC10946495 DOI: 10.1002/pd.6301] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/19/2022] [Accepted: 01/02/2023] [Indexed: 01/10/2023]
Abstract
Aetiological understanding and screening methods for congenital heart disease (CHD) are limited. Maternal metabolomic assessment offers the potential to identify risk factors and biomarkers. We performed a systematic review (PROSPERO CRD42022308452) investigating the association between fetal/childhood CHD and endogenous maternal metabolites. Ovid-MEDLINE, Ovid-EMBASE and Cochrane Library were searched between inception and 06/09/2022. Case control studies included analysing maternal blood or urine metabolites in pregnancy or postpartum where there was foetal/childhood CHD. Risk of bias assessment utilised the Scottish Intercollegiate Guidelines Network methodology checklist and narrative synthesis was performed. A total of 134 records were screened with eight eligible studies (n = 3242 pregnancies, n = 842 CHD-affected offspring). Five studies performed metabolomic analysis in pregnancy. Metabolites distinguishing case and control groups spanned lipid, glucose and amino-acid pathways, with the development of sensitive risk prediction models. No single metabolite consistently distinguished cases and controls across studies. Three studies performed targeted analysis postnatally with altered lipid and amino acid metabolites and raised homocysteine and markers of oxidative stress identified in cases. Included studies reported small sample sizes, analysing different biosamples at variable time points using differing techniques. At present, there is not enough evidence to confidently associate maternal metabolomic profiles with offspring CHD risk. However, several identified pathways warrant further investigation.
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Affiliation(s)
- Stuart Mires
- University of BristolBristolUK
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
| | - Snigdha Reddy
- Birmingham Women's and Children's NHS Foundation TrustBirminghamUK
| | - Clare Skerritt
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
| | - Massimo Caputo
- University of BristolBristolUK
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
| | - Kelly‐Ann Eastwood
- University of BristolBristolUK
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
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Fang Y, Zhang Z, Zhao Y, Sun G, Peng M, Liu C, Yi G, Zhao K, Yang H. The value of lipid metabolites 9,10-DOA and 11,12-EET in prenatal diagnosis of fetal heart defects. Clin Chim Acta 2023; 544:117330. [PMID: 37037297 DOI: 10.1016/j.cca.2023.117330] [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: 05/18/2022] [Revised: 03/15/2023] [Accepted: 04/02/2023] [Indexed: 04/12/2023]
Abstract
AIMS To explore the maternal metabolic changes of fetal congenital heart disease (FCHD), and screen metabolic markers to establish a practical diagnostic model. METHODS Maternal peripheral serum from 17 FCHD and 63 non-FCHD pregnant were analyzed by Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS). RESULTS In the FCHD and the non-FCHD, 132 metabolites were identified, including 35 differential metabolites enriched in the purine, caffeine, primary bile acid, and arachidonic acid metabolism pathway. Finally, the screened (+/-)9,10-dihydroxy-12Z-octadecenoic acid (AUC = 0.888) and 11,12-epoxy-(5Z,8Z,11Z)-icosatrienoic acid (AUC = 0.995) were incorporated into the logistic regression model. The AUC value of the two-metabolite model was 1.0, superior to proline (AUC = 0.867), uric acid (AUC = 0.789), glutamine (AUC = 0.705), and taurine (AUC = 0.923) previously reported. The clinical decision curve analysis (DCA) showed the highest clinical net benefit of the model, and internal validation by bootstrap shows the robustness of the model (Brier Score = 0.005). CONCLUSION For the prenatal diagnosis of CHD, our findings are of great clinical significance. As an additional screening procedure, the identification model might be used to detect.
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Affiliation(s)
- Yiwei Fang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zheng Zhang
- College of Life Sciences, Central China Normal University, Wuhan, 430079, China; Wuhan Prevention and Treatment Center for Occupational Diseases, Wuhan, 430015, China
| | - Yun Zhao
- Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China
| | - Guoqiang Sun
- Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China
| | - Meilin Peng
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Chunyan Liu
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Guilin Yi
- Wuhan Prevention and Treatment Center for Occupational Diseases, Wuhan, 430015, China.
| | - Kai Zhao
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hui Yang
- Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China.
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Momeni M, Rashidifar M, Balam FH, Roointan A, Gholaminejad A. A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures. Sci Rep 2023; 13:5599. [PMID: 37019895 PMCID: PMC10075178 DOI: 10.1038/s41598-023-32268-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/24/2023] [Indexed: 04/07/2023] Open
Abstract
COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.
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Affiliation(s)
- Maryam Momeni
- Department of Biotechnology, Faculty of Biological Science and Technology, The University of Isfahan, Isfahan, Iran
| | - Maryam Rashidifar
- Department of Plant Sciences and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Farinaz Hosseini Balam
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Roointan
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan Univerity of Medical Sciences, Hezar Jarib St, Isfahan, 81746-73461, Iran
| | - Alieh Gholaminejad
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan Univerity of Medical Sciences, Hezar Jarib St, Isfahan, 81746-73461, Iran.
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Troisi J, Lombardi M, Scala G, Cavallo P, Tayler RS, Symes SJK, Richards SM, Adair DC, Fasano A, McCowan LM, Guida M. A screening test proposal for congenital defects based on maternal serum metabolomics profile. Am J Obstet Gynecol 2023; 228:342.e1-342.e12. [PMID: 36075482 DOI: 10.1016/j.ajog.2022.08.050] [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] [Received: 12/23/2021] [Revised: 08/12/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology. OBJECTIVE This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects. STUDY DESIGN This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort. RESULTS Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]). CONCLUSION This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.
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Affiliation(s)
- Jacopo Troisi
- Department of Medicine, Surgery, and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi, Salerno, Italy; Theoreo srl, Montecorvino Pugliano, Salerno, Italy; Department of Chemistry and Biology, "A. Zambelli," University of Salerno, Fisciano, Salerno, Italy.
| | - Martina Lombardi
- Theoreo srl, Montecorvino Pugliano, Salerno, Italy; Department of Chemistry and Biology, "A. Zambelli," University of Salerno, Fisciano, Salerno, Italy
| | - Giovanni Scala
- Theoreo srl, Montecorvino Pugliano, Salerno, Italy; Hosmotic srl, Vico Equense, Italy
| | - Pierpaolo Cavallo
- Department of Physics, University of Salerno, Fisciano, Salerno, Italy; Istituto Sistemi Complessi - Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Rennae S Tayler
- Faculty of Medical and Health Sciences, Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - Steven J K Symes
- Department of Chemistry and Physics, University of Tennessee at Chattanooga, Chattanooga, TN; Department of Obstetrics and Gynecology, University of Tennessee College of Medicine, Chattanooga, TN
| | - Sean M Richards
- Department of Obstetrics and Gynecology, University of Tennessee College of Medicine, Chattanooga, TN; Department of Biology, Geology, and Environmental Sciences, University of Tennessee at Chattanooga, Chattanooga, TN
| | - David C Adair
- Department of Obstetrics and Gynecology, University of Tennessee College of Medicine, Chattanooga, TN
| | - Alessio Fasano
- Department of Chemistry and Biology, "A. Zambelli," University of Salerno, Fisciano, Salerno, Italy; Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Lesley M McCowan
- Faculty of Medical and Health Sciences, Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - Maurizio Guida
- Theoreo srl, Montecorvino Pugliano, Salerno, Italy; Department of Neurosciences and Reproductive and Dentistry Sciences, University of Naples Federico II, Naples, Italy
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10
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Placental Metabolomics of Fetal Growth Restriction. Metabolites 2023; 13:metabo13020235. [PMID: 36837853 PMCID: PMC9959525 DOI: 10.3390/metabo13020235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
Fetal growth restriction is an obstetrical pathological condition that causes high neonatal mortality and morbidity. The mechanisms of its onset are not completely understood. Metabolites were extracted from 493 placentas from non-complicated pregnancies in Hamilton Country, TN (USA), and analyzed by gas chromatography-mass spectrometry (GC-MS). Newborns were classified according to raw fetal weight (low birth weight (LBW; <2500 g) and non-low birth weight (Non-LBW; >2500 g)), and according to the calculated birth weight centile as it relates to gestational age (small for gestational age (SGA), large for gestational age (LGA), and adequate for gestational age (AGA)). Mothers of LBW infants had a lower pre-pregnancy weight (66.2 ± 17.9 kg vs. 73.4 ± 21.3 kg, p < 0.0001), a lower body mass index (BMI) (25.27 ± 6.58 vs. 27.73 ± 7.83, p < 0.001), and a shorter gestation age (246.4 ± 24.0 days vs. 267.2 ± 19.4 days p < 0.001) compared with non-LBW. Marital status, tobacco use, and fetus sex affected birth weight centile classification according to gestational age. Multivariate statistical comparisons of the extracted metabolomes revealed that asparagine, aspartic acid, deoxyribose, erythritol, glycerophosphocholine, tyrosine, isoleucine, serine, and lactic acid were higher in both SGA and LBW placentas, while taurine, ethanolamine, β-hydroxybutyrate, and glycine were lower in both SGA and LBW. Several metabolic pathways are implicated in fetal growth restriction, including those related to the hypoxia response and amino-acid uptake and metabolism. Inflammatory pathways are also involved, suggesting that fetal growth restriction might share some mechanisms with preeclampsia.
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Metabolomics: A New Tool in Our Understanding of Congenital Heart Disease. CHILDREN (BASEL, SWITZERLAND) 2022; 9:children9121803. [PMID: 36553246 PMCID: PMC9776621 DOI: 10.3390/children9121803] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/12/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022]
Abstract
Although the genetic origins underpinning congenital heart disease (CHD) have been extensively studied, genes, by themselves, do not entirely predict phenotypes, which result from the complex interplay between genes and the environment. Consequently, genes merely suggest the potential occurrence of a specific phenotype, but they cannot predict what will happen in reality. This task can be revealed by metabolomics, the most promising of the "omics sciences". Though metabolomics applied to CHD is still in its infant phase, it has already been applied to CHD prenatal diagnosis, as well as to predict outcomes after cardiac surgery. Particular metabolomic fingerprints have been identified for some of the specific CHD subtypes. The hallmarks of CHD-related pulmonary arterial hypertension have also been discovered. This review, which is presented in a narrative format, due to the heterogeneity of the selected papers, aims to provide the readers with a synopsis of the literature on metabolomics in the CHD setting.
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The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery. Biomolecules 2022; 12:biom12091229. [PMID: 36139068 PMCID: PMC9496630 DOI: 10.3390/biom12091229] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 12/11/2022] Open
Abstract
Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.
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Troisi J, Tafuro M, Lombardi M, Scala G, Richards SM, Symes SJK, Ascierto PA, Delrio P, Tatangelo F, Buonerba C, Pierri B, Cerino P. A Metabolomics-Based Screening Proposal for Colorectal Cancer. Metabolites 2022; 12:metabo12020110. [PMID: 35208185 PMCID: PMC8878838 DOI: 10.3390/metabo12020110] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/17/2022] [Accepted: 01/22/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.
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Affiliation(s)
- Jacopo Troisi
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, 84081 Baronissi, Italy
- Theoreo srl, Via degli Ulivi 3, 84090 Montecorvino Pugliano, Italy; (M.L.); (G.S.)
- Correspondence: or (J.T.); (B.P.)
| | - Maria Tafuro
- Centro di Referenza Nazionale per l’Analisi e Studio di Correlazione tra Ambiente, Animale e Uomo, Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (M.T.); (C.B.); (P.C.)
| | - Martina Lombardi
- Theoreo srl, Via degli Ulivi 3, 84090 Montecorvino Pugliano, Italy; (M.L.); (G.S.)
| | - Giovanni Scala
- Theoreo srl, Via degli Ulivi 3, 84090 Montecorvino Pugliano, Italy; (M.L.); (G.S.)
- Hosmotic srl, Via R. Bosco 178, 80069 Vico Equense, Italy
| | - Sean M. Richards
- Department of Obstetrics and Gynecology, Section on Maternal-Fetal Medicine, University of Tennessee College of Medicine, 960 East Third Street, Suite 100, 902 McCallie Avenue, Chattanooga, TN 37403, USA; (S.M.R.); (S.J.K.S.)
- Department of Biology, Geology and Environmental Sciences, University of Tennessee at Chattanooga, 615 McCallie Ave., Chattanooga, TN 37403, USA
| | - Steven J. K. Symes
- Department of Obstetrics and Gynecology, Section on Maternal-Fetal Medicine, University of Tennessee College of Medicine, 960 East Third Street, Suite 100, 902 McCallie Avenue, Chattanooga, TN 37403, USA; (S.M.R.); (S.J.K.S.)
- Department of Chemistry and Physics, University of Tennessee at Chattanooga, 615 McCallie Ave., Chattanooga, TN 37403, USA
| | - Paolo Antonio Ascierto
- Istituto Nazionale Tumori Fondazione Pascale IRCCS, 80131 Napoli, Italy; (P.A.A.); (P.D.); (F.T.)
| | - Paolo Delrio
- Istituto Nazionale Tumori Fondazione Pascale IRCCS, 80131 Napoli, Italy; (P.A.A.); (P.D.); (F.T.)
| | - Fabiana Tatangelo
- Istituto Nazionale Tumori Fondazione Pascale IRCCS, 80131 Napoli, Italy; (P.A.A.); (P.D.); (F.T.)
| | - Carlo Buonerba
- Centro di Referenza Nazionale per l’Analisi e Studio di Correlazione tra Ambiente, Animale e Uomo, Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (M.T.); (C.B.); (P.C.)
| | - Biancamaria Pierri
- Centro di Referenza Nazionale per l’Analisi e Studio di Correlazione tra Ambiente, Animale e Uomo, Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (M.T.); (C.B.); (P.C.)
- Correspondence: or (J.T.); (B.P.)
| | - Pellegrino Cerino
- Centro di Referenza Nazionale per l’Analisi e Studio di Correlazione tra Ambiente, Animale e Uomo, Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (M.T.); (C.B.); (P.C.)
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Metabolomics Signatures and Subsequent Maternal Health among Mothers with a Congenital Heart Defect-Affected Pregnancy. Metabolites 2022; 12:metabo12020100. [PMID: 35208175 PMCID: PMC8877777 DOI: 10.3390/metabo12020100] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 12/24/2022] Open
Abstract
Congenital heart defects (CHDs) are the most prevalent and serious of all birth defects in the United States. However, little is known about the impact of CHD-affected pregnancies on subsequent maternal health. Thus, there is a need to characterize the metabolic alterations associated with CHD-affected pregnancies. Fifty-six plasma samples were identified from post-partum women who participated in the National Birth Defects Prevention Study between 1997 and 2011 and had (1) unaffected control offspring (n = 18), (2) offspring with tetralogy of Fallot (ToF, n = 22), or (3) hypoplastic left heart syndrome (HLHS, n = 16) in this pilot study. Absolute concentrations of 408 metabolites using the AbsoluteIDQ® p400 HR Kit (Biocrates) were evaluated among case and control mothers. Twenty-six samples were randomly selected from above as technical repeats. Analysis of covariance (ANCOVA) and logistic regression models were used to identify significant metabolites after controlling for the maternal age at delivery and body mass index. The receiver operating characteristic (ROC) curve and area-under-the-curve (AUC) are reported to evaluate the performance of significant metabolites. Overall, there were nine significant metabolites (p < 0.05) identified in HLHS case mothers and 30 significant metabolites in ToF case mothers. Statistically significant metabolites were further evaluated using ROC curve analyses with PC (34:1), two sphingolipids SM (31:1), SM (42:2), and PC-O (40:4) elevated in HLHS cases; while LPC (18:2), two triglycerides: TG (44:1), TG (46:2), and LPC (20:3) decreased in ToF; and cholesterol esters CE (22:6) were elevated among ToF case mothers. The metabolites identified in the study may have profound structural and functional implications involved in cellular signaling and suggest the need for postpartum dietary supplementation among women who gave birth to CHD offspring.
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Van den Eynde J, Kutty S, Danford DA, Manlhiot C. Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data. Curr Opin Cardiol 2022; 37:130-136. [PMID: 34857721 DOI: 10.1097/hco.0000000000000927] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) has changed virtually every aspect of modern life, and medicine is no exception. Pediatric cardiology is both a perceptual and a cognitive subspecialty that involves complex decision-making, so AI is a particularly attractive tool for this medical discipline. This review summarizes the foundational work and incremental progress made as AI applications have emerged in pediatric cardiology since 2020. RECENT FINDINGS AI-based algorithms can be useful for pediatric cardiology in many areas, including: (1) clinical examination and diagnosis, (2) image processing, (3) planning and management of cardiac interventions, (4) prognosis and risk stratification, (5) omics and precision medicine, and (6) fetal cardiology. Most AI initiatives showcased in medical journals seem to work well in silico, but progress toward implementation in actual clinical practice has been more limited. Several barriers to implementation are identified, some encountered throughout medicine generally, and others specific to pediatric cardiology. SUMMARY Despite barriers to acceptance in clinical practice, AI is already establishing a durable role in pediatric cardiology. Its potential remains great, but to fully realize its benefits, substantial investment to develop and refine AI for pediatric cardiology applications will be necessary to overcome the challenges of implementation.
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Affiliation(s)
- Jef Van den Eynde
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Cardiovascular Sciences, KU Leuven & Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium
| | - Shelby Kutty
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David A Danford
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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Helman SM, Herrup EA, Christopher AB, Al-Zaiti SS. The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. Cardiol Young 2021; 31:1770-1780. [PMID: 34725005 PMCID: PMC8805679 DOI: 10.1017/s1047951121004212] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.
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Affiliation(s)
- Stephanie M Helman
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth A Herrup
- Division of Pediatric Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Adam B Christopher
- Division of Pediatric Cardiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Salah S Al-Zaiti
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
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