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Park S, Moon J, Eun H, Hong JH, Lee K. Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants. J Clin Med 2024; 13:2089. [PMID: 38610854 PMCID: PMC11012712 DOI: 10.3390/jcm13072089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/02/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children's Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system's performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.
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Affiliation(s)
- Seoyeon Park
- Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea; (S.P.); (K.L.)
| | - Junhyung Moon
- Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea; (S.P.); (K.L.)
| | - Hoseon Eun
- Department of Pediatrics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seoul 03722, Republic of Korea;
| | - Jin-Hyuk Hong
- School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Gwangju 61005, Republic of Korea;
| | - Kyoungwoo Lee
- Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seoul 03722, Republic of Korea; (S.P.); (K.L.)
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Bravo MC, Jiménez R, Parrado-Hernández E, Fernández JJ, Pellicer A. Predicting the effectiveness of drugs used for treating cardiovascular conditions in newborn infants. Pediatr Res 2024; 95:1124-1131. [PMID: 38092963 DOI: 10.1038/s41390-023-02964-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/08/2023] [Accepted: 11/27/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND Cardiovascular support (CVS) treatment failure (TF) is associated with a poor prognosis in preterm infants. METHODS Medical charts of infants with a birth weight <1500 g who received either dopamine (Dp) or dobutamine (Db), were reviewed. Treatment response (TR) occurred if blood pressure increased >3rd centile for gestational age or superior vena cava flow was maintained >55 ml/kg/min, with decreased lactate or less negative base excess, without additional CVS. A predictive model of Dp and Db on TR was designed and the impact of TR on survival was analyzed. RESULTS Sixty-six infants (median gestational age 27.3 weeks, median birth weight 864 g) received Dp (n = 44) or Db (n = 22). TR occurred in 59% of the cases treated with Dp and 31% with Db, p = 0.04. Machine learning identified a model that correctly labeled Db response in 90% of the cases and Dp response in 61.4%. Sixteen infants died (9% of the TR group, 39% of the TF group; p = 0.004). Brain or gut morbidity-free survival was observed in 52% vs 30% in the TR and TF groups, respectively (p = 0.08). CONCLUSIONS New predictive models can anticipate Db but not Dp effectiveness in preterm infants. These algorithms may help the clinicians in the decision-making process. IMPACT Failure of cardiovascular support treatment increases the risk of mortality in very low birth weight infants. A predictive model built with machine learning techniques can help anticipate treatment response to dobutamine with high accuracy. Predictive models based on artificial intelligence may guide the clinicians in the decision-making process.
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Affiliation(s)
- María Carmen Bravo
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain.
| | - Raquel Jiménez
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain
- Department of Signal Theory and Communications, Carlos III University, Madrid, Spain
| | | | | | - Adelina Pellicer
- Department of Neonatology, La Paz University Hospital and IdiPaz (La Paz Hospital Institute for Health Research), Madrid, Spain
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Erno J, Gomes T, Baltimore C, Lineberger JP, Smith DH, Baker GH. Automated Identification of Patent Ductus Arteriosus Using a Computer Vision Model. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:2707-2713. [PMID: 37449663 DOI: 10.1002/jum.16305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/12/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVES Patent ductus arteriosus (PDA) is a vascular defect common in preterm infants and often requires treatment to avoid associated long-term morbidities. Echocardiography is the primary tool used to diagnose and monitor PDA. We trained a deep learning model to identify PDA presence in relevant echocardiographic images. METHODS Echocardiography video clips (n = 2527) in preterm infants were reviewed by a pediatric cardiologist and those relevant to PDA diagnosis were selected and labeled (PDA present/absent/indeterminate). We trained a convolutional neural network to classify each echocardiography frame of a clip as belonging to clips with or without PDA. A novel attention mechanism that aggregated predictions for all frames in each clip to obtain a clip-level prediction by weighting relevant frames. RESULTS In early model iterations, we discovered training with color Doppler echocardiography clips produced the best performing classifier. For model training and validation, 1145 such clips from 66 patients (661 PDA+ clips, 484 PDA- clips) were used. Our best classifier for clip level performance obtained sensitivity of 0.80 (0.83-0.90), specificity of 0.77 (0.62-0.92) and AUC of 0.86 (0.83-0.90). Study level performance obtained sensitivity of 0.83 (0.72-0.94), specificity of 0.89 (0.79-1.0) and AUC of 0.93 (0.89-0.98). CONCLUSIONS Our novel deep learning model demonstrated strong performance in classifying echocardiography clips with and without PDA. Further model development and external validation are warranted. Ultimately, integration of such a classifier into auto detection software could streamline PDA imaging workflow. This work is the first step toward semi-automated, bedside detection of PDA in preterm infants.
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Affiliation(s)
- Jason Erno
- College of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Thomas Gomes
- College of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Christopher Baltimore
- College of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - John P Lineberger
- Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina, USA
| | - D Hudson Smith
- Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina, USA
| | - G Hamilton Baker
- Department of Pediatrics, Medical University of South Carolina, Charleston, South Carolina, USA
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Chen YX, Xiao TT, Chen HY, Chen X, Wang YQ, Ni Q, Wu BB, Wang HJ, Lu YL, Hu LY, Cao Y, Cheng GQ, Wang LS, Xiao FF, Yang L, Dong XR, Zhou WH. Risk stratification of hemodynamically significant patent ductus arteriosus by clinical and genetic factors. World J Pediatr 2023; 19:1192-1202. [PMID: 37318723 DOI: 10.1007/s12519-023-00733-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 05/10/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND Hemodynamically significant patent ductus arteriosus (hsPDA) is associated with increased comorbidities in neonates. Early evaluation of hsPDA risk is critical to implement individualized intervention. The aim of the study was to provide a powerful reference for the early identification of high-risk hsPDA population and early treatment decisions. METHODS We enrolled infants who were diagnosed with PDA and performed exome sequencing. The collapsing analyses were used to find the risk gene set (RGS) of hsPDA for model construction. The credibility of RGS was proven by RNA sequencing. Multivariate logistic regression was performed to establish models combining clinical and genetic features. The models were evaluated by area under the receiver operating curve (AUC) and decision curve analysis (DCA). RESULTS In this retrospective cohort study of 2199 PDA patients, 549 (25.0%) infants were diagnosed with hsPDA. The model [all clinical characteristics selected by least absolute shrinkage and selection operator regression (all CCs)] based on six clinical variables was acquired within three days of life, including gestational age (GA), respiratory distress syndrome (RDS), the lowest platelet count, invasive mechanical ventilation, and positive inotropic and vasoactive drugs. It has an AUC of 0.790 [95% confidence interval (CI) = 0.749-0.832], while the simplified model (basic clinical characteristic model) including GA and RDS has an AUC of 0.753 (95% CI = 0.706-0.799). There was a certain consistency between RGS and differentially expressed genes of the ductus arteriosus in mice. The AUC of the models was improved by RGS, and the improvement was significant (all CCs vs. all CCs + RGS: 0.790 vs. 0.817, P < 0.001). DCA demonstrated that all models were clinically useful. CONCLUSIONS Models based on clinical factors were developed to accurately stratify the risk of hsPDA in the first three days of life. Genetic features might further improve the model performance. Video Abstract (MP4 86834 kb).
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Affiliation(s)
- Yu-Xi Chen
- Center for Molecular Medicine of Children's Hospital of Fudan University, Institutes of Biomedical Sciences, Fudan University, 138 Yi Xue Yuan Road, Shanghai, China
| | - Tian-Tian Xiao
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Hui-Yao Chen
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Xiang Chen
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Ya-Qiong Wang
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Qi Ni
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Bing-Bing Wu
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Hui-Jun Wang
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Yu-Lan Lu
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Li-Yuan Hu
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Yun Cao
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Guo-Qiang Cheng
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Lai-Shuan Wang
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Fei-Fan Xiao
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Lin Yang
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China
| | - Xin-Ran Dong
- Center for Molecular Medicine, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, China.
| | - Wen-Hao Zhou
- Center for Molecular Medicine of Children's Hospital of Fudan University, Institutes of Biomedical Sciences, Fudan University, 138 Yi Xue Yuan Road, Shanghai, China.
- Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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Na JY, Jung D, Cha JH, Kim D, Son J, Hwang JK, Kim TH, Park HK. Learning-Based Longitudinal Prediction Models for Mortality Risk in Very-Low-Birth-Weight Infants: A Nationwide Cohort Study. Neonatology 2023; 120:652-660. [PMID: 37459839 DOI: 10.1159/000530738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/12/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Prediction models assessing the mortality of very-low-birth-weight (VLBW) infants were confined to models using only pre- and perinatal variables. We aimed to construct a prediction model comprising multifactorial clinical events with data obtainable at various time points. METHODS We included 15,790 (including 2,045 in-hospital deaths) VLBW infants born between 2013 and 2020 who were enrolled in the Korean Neonatal Network, a nationwide registry. In total, 53 prenatal and postnatal variables were sequentially added into the three discrete models stratified by hospital days: (1) within 24 h (TL-1d), (2) from day 2 to day 7 after birth (TL-7d), (3) from day 8 after birth to discharge from the neonatal intensive care unit (TL-dc). Each model predicted the mortality of VLBW infants within the affected period. Multilayer perception (MLP)-based network analysis was used for modeling, and ensemble analysis with traditional machine learning (ML) analysis was additionally applied. The performance of models was compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was applied to reveal the contribution of each variable. RESULTS Overall, the in-hospital mortality was 13.0% (1.2% in TL-1d, 4.1% in TL-7d, and 7.7% in TL-dc). Our MLP-based mortality prediction model combined with ML ensemble analysis had AUROC values of 0.932 (TL-1d), 0.973 (TL-7d), and 0.950 (TL-dc), respectively, outperforming traditional ML analysis in each timeline. Birth weight and gestational age were constant and significant risk factors, whereas the impact of the other variables varied. CONCLUSION The findings of the study suggest that our MLP-based models could be applied in predicting in-hospital mortality for high-risk VLBW infants. We highlight that mortality prediction should be customized according to the timing of occurrence.
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Affiliation(s)
- Jae Yoon Na
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Donggoo Jung
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Jong Ho Cha
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Daehyun Kim
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Joonhyuk Son
- Department of Pediatric Surgery, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jae Kyoon Hwang
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Tae Hyun Kim
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Hyun-Kyung Park
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
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Hwang JK, Kim DH, Na JY, Son J, Oh YJ, Jung D, Kim CR, Kim TH, Park HK. Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study. Front Pediatr 2023; 11:1155921. [PMID: 37384307 PMCID: PMC10294267 DOI: 10.3389/fped.2023.1155921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/16/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction The aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort. Methods We included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013-2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable. Results We included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants. Conclusions We developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field.
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Affiliation(s)
- Jae Kyoon Hwang
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Dae Hyun Kim
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Jae Yoon Na
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Joonhyuk Son
- Department of Pediatric Surgery, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Yoon Ju Oh
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Donggoo Jung
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Chang-Ryul Kim
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Tae Hyun Kim
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Hyun-Kyung Park
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, Republic of Korea
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Sharma P, Beam K, Levy P, Beam AL. PD(AI): the role of artificial intelligence in the management of patent ductus arteriosus. J Perinatol 2023; 43:257-258. [PMID: 36646822 DOI: 10.1038/s41372-023-01606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 01/18/2023]
Affiliation(s)
- Puneet Sharma
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA.
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Philip Levy
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Improving child health through Big Data and data science. Pediatr Res 2023; 93:342-349. [PMID: 35974162 PMCID: PMC9380977 DOI: 10.1038/s41390-022-02264-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 12/04/2022]
Abstract
Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.
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Sethi Y, Patel N, Kaka N, Desai A, Kaiwan O, Sheth M, Sharma R, Huang H, Chopra H, Khandaker MU, Lashin MMA, Hamd ZY, Emran TB. Artificial Intelligence in Pediatric Cardiology: A Scoping Review. J Clin Med 2022; 11:jcm11237072. [PMID: 36498651 PMCID: PMC9738645 DOI: 10.3390/jcm11237072] [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: 11/05/2022] [Revised: 11/22/2022] [Accepted: 11/26/2022] [Indexed: 12/05/2022] Open
Abstract
The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.
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Affiliation(s)
- Yashendra Sethi
- PearResearch, Dehradun 248001, India
- Department of Medicine, Government Doon Medical College, Dehradun 248001, India
| | - Neil Patel
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Nirja Kaka
- PearResearch, Dehradun 248001, India
- Department of Medicine, GMERS Medical College, Himmatnagar 383001, India
| | - Ami Desai
- Department of Medicine, SMIMER Medical College, Surat 395010, India
| | - Oroshay Kaiwan
- PearResearch, Dehradun 248001, India
- Department of Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Mili Sheth
- Department of Medicine, GMERS Gandhinagar, Gandhinagar 382012, India
| | - Rupal Sharma
- Department of Medicine, Government Medical College, Nagpur 440003, India
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, D02 YN77 Dublin, Ireland
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, India
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway 47500, Malaysia
| | - Maha M. A. Lashin
- Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
| | - Zuhal Y. Hamd
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
- Correspondence: (O.K.); (Z.Y.H.); (T.B.E.)
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