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Tandon A, Avari Silva JN, Bhatt AB, Drummond CK, Hill AC, Paluch AE, Waits S, Zablah JE, Harris KC. Advancing Wearable Biosensors for Congenital Heart Disease: Patient and Clinician Perspectives: A Science Advisory From the American Heart Association. Circulation 2024; 149:e1134-e1142. [PMID: 38545775 DOI: 10.1161/cir.0000000000001225] [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] [Indexed: 05/08/2024]
Abstract
Wearable biosensors (wearables) enable continual, noninvasive physiologic and behavioral monitoring at home for those with pediatric or congenital heart disease. Wearables allow patients to access their personal data and monitor their health. Despite substantial technologic advances in recent years, issues with hardware design, data analysis, and integration into the clinical workflow prevent wearables from reaching their potential in high-risk congenital heart disease populations. This science advisory reviews the use of wearables in patients with congenital heart disease, how to improve these technologies for clinicians and patients, and ethical and regulatory considerations. Challenges related to the use of wearables are common to every clinical setting, but specific topics for consideration in congenital heart disease are highlighted.
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Liu Z, Meng Z, Wei D, Qin Y, Lv Y, Xie L, Qiu H, Xie B, Li L, Wei X, Zhang D, Liang B, Li W, Qin S, Yan T, Meng Q, Wei H, Jiang G, Su L, Jiang N, Zhang K, Lv J, Hu Y. Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning. BMC Med Inform Decis Mak 2024; 24:110. [PMID: 38664736 PMCID: PMC11046885 DOI: 10.1186/s12911-024-02511-5] [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: 07/27/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
OBJECTIVE This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). METHODS Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model. RESULTS The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814-0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD. CONCLUSION This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs.
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Affiliation(s)
- Zengjing Liu
- Information and Management College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Zhihao Meng
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Di Wei
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Yuan Qin
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Yu Lv
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Luman Xie
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Hong Qiu
- Life Sciences College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Bo Xie
- Information and Management College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Lanxiang Li
- Basic Medical College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Xihua Wei
- Life Sciences College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Die Zhang
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co- constructed by the Province, Ministry of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Boying Liang
- Basic Medical College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Wen Li
- Life Sciences College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Shanfang Qin
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Tengyue Yan
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co- constructed by the Province, Ministry of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Qiuxia Meng
- Information and Management College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Huilin Wei
- Life Sciences College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Guiyang Jiang
- Department of rehabilitation medicine, Department of the First affliated hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Lingsong Su
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China
| | - Nili Jiang
- Life Sciences College of Guangxi Medical University, Nanning, Guangxi, 530021, China
| | - Kai Zhang
- Guangxi Clinical Center for AIDS Prevention and Treatment, Chest Hospital of Guangxi Zhuang Autonomous Region, No. 8 Yangjiaoshan Road, Liuzhou, Guangxi, 545005, China.
| | - Jiannan Lv
- Affiliate Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, 533000, China.
| | - Yanling Hu
- Information and Management College of Guangxi Medical University, Nanning, Guangxi, 530021, China.
- Life Sciences College of Guangxi Medical University, Nanning, Guangxi, 530021, China.
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co- constructed by the Province, Ministry of Guangxi Medical University, Nanning, Guangxi, 530021, China.
- Faculty of Data science, City University of Macau, 999078, Macau, China.
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Gabbert DD, Petersen L, Burleigh A, Grazioli SB, Krupickova S, Koch R, Uebing AS, Santarossa M, Voges I. Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning. MAGMA (NEW YORK, N.Y.) 2024; 37:115-125. [PMID: 38214799 PMCID: PMC10876735 DOI: 10.1007/s10334-023-01124-9] [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: 06/23/2023] [Revised: 09/29/2023] [Accepted: 10/09/2023] [Indexed: 01/13/2024]
Abstract
OBJECTIVE The prospect of being able to gain relevant information from cardiovascular magnetic resonance (CMR) image analysis automatically opens up new potential to assist the evaluating physician. For machine-learning-based classification of complex congenital heart disease, only few studies have used CMR. MATERIALS AND METHODS This study presents a tailor-made neural network architecture for detection of 7 distinctive anatomic landmarks in CMR images of patients with hypoplastic left heart syndrome (HLHS) in Fontan circulation or healthy controls and demonstrates the potential of the spatial arrangement of the landmarks to identify HLHS. The method was applied to the axial SSFP CMR scans of 46 patients with HLHS and 33 healthy controls. RESULTS The displacement between predicted and annotated landmark had a standard deviation of 8-17 mm and was larger than the interobserver variability by a factor of 1.1-2.0. A high overall classification accuracy of 98.7% was achieved. DISCUSSION Decoupling the identification of clinically meaningful anatomic landmarks from the actual classification improved transparency of classification results. Information from such automated analysis could be used to quickly jump to anatomic positions and guide the physician more efficiently through the analysis depending on the detected condition, which may ultimately improve work flow and save analysis time.
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Affiliation(s)
- Dominik Daniel Gabbert
- Department of Congenital Heart Disease and Pediatric Cardiology, DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Hospital Schleswig-Holstein, Kiel, Germany.
| | - Lennart Petersen
- Department of Congenital Heart Disease and Pediatric Cardiology, DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Hospital Schleswig-Holstein, Kiel, Germany
- Department of Computer Science, Kiel University, Kiel, Germany
| | - Abigail Burleigh
- Department of Congenital Heart Disease and Pediatric Cardiology, DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Simona Boroni Grazioli
- Department of Congenital Heart Disease and Pediatric Cardiology, DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Sylvia Krupickova
- Department of Pediatric Cardiology, Royal Brompton Hospital, London, UK
| | - Reinhard Koch
- Department of Computer Science, Kiel University, Kiel, Germany
| | - Anselm Sebastian Uebing
- Department of Congenital Heart Disease and Pediatric Cardiology, DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Inga Voges
- Department of Congenital Heart Disease and Pediatric Cardiology, DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, University Hospital Schleswig-Holstein, Kiel, Germany
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Chhatwal K, Smith JJ, Bola H, Zahid A, Venkatakrishnan A, Brand T. Uncovering the Genetic Basis of Congenital Heart Disease: Recent Advancements and Implications for Clinical Management. CJC PEDIATRIC AND CONGENITAL HEART DISEASE 2023; 2:464-480. [PMID: 38205435 PMCID: PMC10777202 DOI: 10.1016/j.cjcpc.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/13/2023] [Indexed: 01/12/2024]
Abstract
Congenital heart disease (CHD) is the most prevalent hereditary disorder, affecting approximately 1% of all live births. A reduction in morbidity and mortality has been achieved with advancements in surgical intervention, yet challenges in managing complications, extracardiac abnormalities, and comorbidities still exist. To address these, a more comprehensive understanding of the genetic basis underlying CHD is required to establish how certain variants are associated with the clinical outcomes. This will enable clinicians to provide personalized treatments by predicting the risk and prognosis, which might improve the therapeutic results and the patient's quality of life. We review how advancements in genome sequencing are changing our understanding of the genetic basis of CHD, discuss experimental approaches to determine the significance of novel variants, and identify barriers to use this knowledge in the clinics. Next-generation sequencing technologies are unravelling the role of oligogenic inheritance, epigenetic modification, genetic mosaicism, and noncoding variants in controlling the expression of candidate CHD-associated genes. However, clinical risk prediction based on these factors remains challenging. Therefore, studies involving human-induced pluripotent stem cells and single-cell sequencing help create preclinical frameworks for determining the significance of novel genetic variants. Clinicians should be aware of the benefits and implications of the responsible use of genomics. To facilitate and accelerate the clinical integration of these novel technologies, clinicians should actively engage in the latest scientific and technical developments to provide better, more personalized management plans for patients.
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Affiliation(s)
- Karanjot Chhatwal
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, Imperial Center of Clinical and Translational Medicine, London, United Kingdom
| | - Jacob J. Smith
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, Imperial Center of Clinical and Translational Medicine, London, United Kingdom
| | - Harroop Bola
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, Imperial Center of Clinical and Translational Medicine, London, United Kingdom
| | - Abeer Zahid
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, Imperial Center of Clinical and Translational Medicine, London, United Kingdom
| | - Ashwin Venkatakrishnan
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, Imperial Center of Clinical and Translational Medicine, London, United Kingdom
| | - Thomas Brand
- National Heart and Lung Institute, Imperial College London, Imperial Center of Clinical and Translational Medicine, London, United Kingdom
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Sunthankar SD, Zhao J, Wei WQ, Hill GD, Parra DA, Kohl K, McCoy A, Jayaram NM, Godown J. Machine Learning to Predict Interstage Mortality Following Single Ventricle Palliation: A NPC-QIC Database Analysis. Pediatr Cardiol 2023; 44:1242-1250. [PMID: 36820914 PMCID: PMC10627450 DOI: 10.1007/s00246-023-03130-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/10/2023] [Indexed: 02/24/2023]
Abstract
There is high risk of mortality between stage I and stage II palliation of single ventricle heart disease. This study aimed to leverage advanced machine learning algorithms to optimize risk-prediction models and identify features most predictive of interstage mortality. This study utilized retrospective data from the National Pediatric Cardiology Quality Improvement Collaborative and included all patients who underwent stage I palliation and survived to hospital discharge (2008-2019). Multiple machine learning models were evaluated, including logistic regression, random forest, gradient boosting trees, extreme gradient boost trees, and light gradient boosting machines. A total of 3267 patients were included with 208 (6.4%) interstage deaths. Machine learning models were trained on 180 clinical features. Digoxin use at discharge was the most influential factor resulting in a lower risk of interstage mortality (p < 0.0001). Stage I surgery with Blalock-Taussig-Thomas shunt portended higher risk than Sano conduit (7.8% vs 4.4%, p = 0.0002). Non-modifiable risk factors identified with increased risk of interstage mortality included female sex, lower gestational age, and lower birth weight. Post-operative risk factors included the requirement of unplanned catheterization and more severe atrioventricular valve insufficiency at discharge. Light gradient boosting machines demonstrated the best performance with an area under the receiver operative characteristic curve of 0.642. Advanced machine learning algorithms highlight a number of modifiable and non-modifiable risk factors for interstage mortality following stage I palliation. However, model performance remains modest, suggesting the presence of unmeasured confounders that contribute to interstage risk.
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Affiliation(s)
- Sudeep D Sunthankar
- Division of Pediatric Cardiology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Thomas P. Graham Jr Division of Pediatric Cardiology, Department of Pediatrics, Monroe Carell Jr Children's Hospital at Vanderbilt, 2220 Children's Way, Suite 5230, Nashville, TN, 37232, USA.
| | - Juan Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Garick D Hill
- Division of Pediatric Cardiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - David A Parra
- Division of Pediatric Cardiology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Karen Kohl
- Division of Pediatric Cardiology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Allison McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Natalie M Jayaram
- Division of Pediatric Cardiology, Children's Mercy Hospital, Kansas City, MO, USA
| | - Justin Godown
- Division of Pediatric Cardiology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
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Helman S, Terry MA, Pellathy T, Hravnak M, George E, Al-Zaiti S, Clermont G. Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support. Appl Clin Inform 2023; 14:789-802. [PMID: 37793618 PMCID: PMC10550364 DOI: 10.1055/s-0043-1775565] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/26/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care. OBJECTIVES Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score. METHODS Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes. RESULTS Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy. CONCLUSION Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.
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Affiliation(s)
- Stephanie Helman
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Martha Ann Terry
- Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Tiffany Pellathy
- Veterans Administration Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States
| | - Marilyn Hravnak
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Elisabeth George
- Department of Nursing, University of Pittsburgh Medical Center, Presbyterian Hospital, Pittsburgh, Pennsylvania, United States
| | - Salah Al-Zaiti
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- Division of Cardiology at University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
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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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Dehghan B, Sabri MR, Ahmadi A, Ghaderian M, Mahdavi C, Ramezani Nejad D, Sattari M. Identifying the Factors Affecting the Incidence of Congenital Heart Disease Using Support Vector Machine and Particle Swarm Optimization. Adv Biomed Res 2023; 12:130. [PMID: 37434918 PMCID: PMC10331520 DOI: 10.4103/abr.abr_54_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 10/09/2022] [Accepted: 10/12/2022] [Indexed: 07/13/2023] Open
Abstract
Background Congenital malformations are defined as "any defect in the structure of a person that exists from birth". Among them, congenital heart malformations have the highest prevalence in the world. This study focuses on the development of a predictive model for congenital heart disease in Isfahan using support vector machine (SVM) and particle swarm intelligence. Materials and Methods It consists of four parts: data collection, preprocessing, identify target features, and technique. The proposed technique is a combination of the SVM method and particle swarm optimization (PSO). Results The data set includes 1389 patients and 399 features. The best performance in terms of accuracy, with 81.57%, is related to the PSO-SVM technique and the worst performance, with 78.62%, is related to the random forest technique. Congenital extra cardiac anomalies are considered as the most important factor with averages of 0.655. Conclusion Congenital extra cardiac anomalies are considered as the most important factor. Detecting more important feature affecting congenital heart disease allows physicians to treat the variable risk factors associated with congenital heart disease progression. The use of a machine learning approach provides the ability to predict the presence of congenital heart disease with high accuracy and sensitivity.
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Affiliation(s)
- Bahar Dehghan
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Reza Sabri
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Ahmadi
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehdi Ghaderian
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Chehreh Mahdavi
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Davood Ramezani Nejad
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Sattari
- Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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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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Al-Zaiti SS, Alghwiri AA, Hu X, Clermont G, Peace A, Macfarlane P, Bond R. A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML). EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:125-140. [PMID: 36713011 PMCID: PMC9708024 DOI: 10.1093/ehjdh/ztac016] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/11/2022] [Indexed: 05/06/2023]
Abstract
Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside.
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Affiliation(s)
| | - Alaa A Alghwiri
- Data Science Core, The Provost Office, University of Pittsburgh, Pittsburgh PA, USA
| | - Xiao Hu
- Center for Data Science, Emory University, Atlanta, GA, USA
| | - Gilles Clermont
- Departments of Critical Care Medicine, Mathematics, Clinical and Translational Science, and Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aaron Peace
- The Clinical Translational Research and Innovation Centre, Northern Ireland, UK
| | - Peter Macfarlane
- Institute of Health and Wellbeing, Electrocardiology Section, University of Glasgow, Glasgow, UK
| | - Raymond Bond
- School of Computing, Ulster University, Ulster, UK
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