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Liu M, Fan Z, Gao Y, Mubonanyikuzo V, Wu R, Li W, Xu N, Liu K, Zhou L. A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury. Sci Rep 2024; 14:16794. [PMID: 39039115 DOI: 10.1038/s41598-024-63793-3] [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: 12/11/2023] [Accepted: 06/03/2024] [Indexed: 07/24/2024] Open
Abstract
Acute kidney injury (AKI) is one of the most important lethal factors for patients admitted to intensive care units (ICUs), and timely high-risk prognostic assessment and intervention are essential to improving patient prognosis. In this study, a stacking model using the MIMIC-III dataset with a two-tier feature selection approach was developed to predict the risk of in-hospital mortality in ICU patients admitted for AKI. External validation was performed using separate MIMIC-IV and eICU-CRD. The area under the curve (AUC) was calculated using the stacking model, and features were selected using the Boruta and XGBoost feature selection methods. This study compares the performance of a stacking model using two-tier feature selection with a model using single-tier feature selection (XGBoost: 85; Boruta: 83; two-tier: 0.91). The predictive effectiveness of the stacking model was further validated by using different datasets (Validation 1: 0.83; Validation 2: 0.85) and comparing it with a simpler model and traditional clinical scores (SOFA: 0.65; APACH IV: 0.61). In addition, this study combined interpretable techniques and causal inference to analyze the causal relationship between features and predicted outcomes.
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Affiliation(s)
- Mengqing Liu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zhiping Fan
- Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Yu Gao
- Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Vivens Mubonanyikuzo
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Ruiqian Wu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Wenjin Li
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Naiyue Xu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Kun Liu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Liang Zhou
- Jiading District Central Hospital Affiliated to Shanghai University of Medicine & Health Sciences, Shanghai, 201899, China.
- Research Center for Medical Intelligent Development, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, 200025, China.
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2
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Ho YS, Fülöp T, Krisanapan P, Soliman KM, Cheungpasitporn W. Artificial intelligence and machine learning trends in kidney care. Am J Med Sci 2024; 367:281-295. [PMID: 38281623 DOI: 10.1016/j.amjms.2024.01.018] [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: 05/05/2023] [Revised: 12/12/2023] [Accepted: 01/23/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics. METHODS The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5425 documents were identified and analyzed. RESULTS The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology. CONCLUSIONS The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.
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Affiliation(s)
- Yuh-Shan Ho
- Trend Research Centre, Asia University, Wufeng, Taichung, Taiwan
| | - Tibor Fülöp
- Medical Services, Ralph H. Johnson VA Medical Center, Charleston, SC, USA; Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA.
| | - Pajaree Krisanapan
- Division of Nephrology, Department of Internal Medicine, Thammasat University, Pathum Thani, Thailand, 12120
| | - Karim M Soliman
- Medical Services, Ralph H. Johnson VA Medical Center, Charleston, SC, USA; Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
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3
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Xu X, Li J, Zhu Z, Zhao L, Wang H, Song C, Chen Y, Zhao Q, Yang J, Pei Y. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis. Bioengineering (Basel) 2024; 11:219. [PMID: 38534493 DOI: 10.3390/bioengineering11030219] [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: 12/29/2023] [Revised: 02/15/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.
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Affiliation(s)
- Xi Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Zhichao Zhu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Linna Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Huina Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Changwei Song
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yining Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Qing Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jijiang Yang
- Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
| | - Yan Pei
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
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4
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Shickel B, Bihorac A. The dawn of multimodal artificial intelligence in nephrology. Nat Rev Nephrol 2024; 20:79-80. [PMID: 38097775 DOI: 10.1038/s41581-023-00799-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Affiliation(s)
- Benjamin Shickel
- Division of Nephrology, Hypertension and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL, USA
- Intelligent Clinical Care Center (IC3), University of Florida, Gainesville, FL, USA
| | - Azra Bihorac
- Division of Nephrology, Hypertension and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL, USA.
- Intelligent Clinical Care Center (IC3), University of Florida, Gainesville, FL, USA.
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5
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Suppadungsuk S, Thongprayoon C, Miao J, Krisanapan P, Qureshi F, Kashani K, Cheungpasitporn W. Exploring the Potential of Chatbots in Critical Care Nephrology. MEDICINES (BASEL, SWITZERLAND) 2023; 10:58. [PMID: 37887265 PMCID: PMC10608511 DOI: 10.3390/medicines10100058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023]
Abstract
The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.
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Affiliation(s)
- Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Nephrology and Hypertension, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Fawad Qureshi
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Kianoush Kashani
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
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Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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7
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Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel) 2023; 13:2760. [PMID: 37685300 PMCID: PMC10487271 DOI: 10.3390/diagnostics13172760] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 09/10/2023] Open
Abstract
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
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Affiliation(s)
- Reabal Najjar
- Canberra Health Services, Australian Capital Territory 2605, Australia
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8
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Wu C, Zhang Y, Nie S, Hong D, Zhu J, Chen Z, Liu B, Liu H, Yang Q, Li H, Xu G, Weng J, Kong Y, Wan Q, Zha Y, Chen C, Xu H, Hu Y, Shi Y, Zhou Y, Su G, Tang Y, Gong M, Wang L, Hou F, Liu Y, Li G. Predicting in-hospital outcomes of patients with acute kidney injury. Nat Commun 2023; 14:3739. [PMID: 37349292 PMCID: PMC10287760 DOI: 10.1038/s41467-023-39474-6] [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: 12/25/2022] [Accepted: 06/15/2023] [Indexed: 06/24/2023] Open
Abstract
Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI.
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Affiliation(s)
- Changwei Wu
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Sheng Nie
- National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Daqing Hong
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Jiajing Zhu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Bicheng Liu
- Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, 210000, Nanjing, China
| | - Huafeng Liu
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University, 524000, Zhanjiang, China
| | - Qiongqiong Yang
- Department of Nephrology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510515, Guangzhou, China
| | - Hua Li
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Gang Xu
- Division of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430000, Wuhan, China
| | - Jianping Weng
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230000, Hefei, China
| | - Yaozhong Kong
- Department of Nephrology, the First People's Hospital of Foshan, 528000, Foshan, China
| | - Qijun Wan
- The Second People's Hospital of Shenzhen, Shenzhen University, 518000, Shenzhen, China
| | - Yan Zha
- Guizhou Provincial People's Hospital, Guizhou University, 550000, Guiyang, China
| | - Chunbo Chen
- Department of Critical Care Medicine, Maoming People's Hospital, 525000, Maoming, China
| | - Hong Xu
- Children's Hospital of Fudan University, 200000, Shanghai, China
| | - Ying Hu
- The Second Affiliated Hospital of Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Yongjun Shi
- Huizhou Municipal Central Hospital, Sun Yat-Sen University, 516000, Huizhou, China
| | - Yilun Zhou
- Department of Nephrology, Beijing Tiantan Hospital, Capital Medical University, 100000, Beijing, China
| | - Guobin Su
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital, The Second Clinical College, Guangzhou University of Chinese Medicine, 510000, Guangzhou, China
| | - Ying Tang
- The Third Affiliated Hospital of Southern Medical University, 510000, Guangzhou, China
| | - Mengchun Gong
- Institute of Health Management, Southern Medical University, 510000, Guangzhou, China
- DHC Technologies, 100000, Beijing, China
| | - Li Wang
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Fanfan Hou
- National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China.
| | - Guisen Li
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China.
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9
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Stafford H, Buell J, Chiang E, Ramesh U, Migden M, Nagarajan P, Amit M, Yaniv D. Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review. Cancers (Basel) 2023; 15:3094. [PMID: 37370703 PMCID: PMC10295857 DOI: 10.3390/cancers15123094] [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: 05/05/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy.
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Affiliation(s)
- Haleigh Stafford
- School of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jane Buell
- School of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Elizabeth Chiang
- School of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Uma Ramesh
- School of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael Migden
- Division of Internal Medicine, Department of Dermatology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Priyadharsini Nagarajan
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Moran Amit
- Graduate School of Biomedical Sciences, The University of Texas, Houston, TX 77030, USA;
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Dan Yaniv
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
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10
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Forrest IS, Petrazzini BO, Duffy Á, Park JK, O'Neal AJ, Jordan DM, Rocheleau G, Nadkarni GN, Cho JH, Blazer AD, Do R. A machine learning model identifies patients in need of autoimmune disease testing using electronic health records. Nat Commun 2023; 14:2385. [PMID: 37169741 PMCID: PMC10130143 DOI: 10.1038/s41467-023-37996-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 04/05/2023] [Indexed: 05/13/2023] Open
Abstract
Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anya J O'Neal
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashira D Blazer
- Division of Rheumatology, Hospital for Special Surgery, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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11
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Shickel B, Loftus TJ, Ren Y, Rashidi P, Bihorac A, Ozrazgat-Baslanti T. Digital Health Transformers and Opportunities for Artificial Intelligence-Enabled Nephrology. Clin J Am Soc Nephrol 2023; 18:527-529. [PMID: 36750442 PMCID: PMC10103323 DOI: 10.2215/cjn.0000000000000085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Benjamin Shickel
- Intelligent Critical Care Center, University of Florida, Gainesville, Florida
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida
| | - Tyler J. Loftus
- Intelligent Critical Care Center, University of Florida, Gainesville, Florida
- Department of Surgery, University of Florida, Gainesville, Florida
| | - Yuanfang Ren
- Intelligent Critical Care Center, University of Florida, Gainesville, Florida
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, Florida
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, Florida
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center, University of Florida, Gainesville, Florida
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida
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12
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Park Y, Hu J. Bias in Artificial Intelligence: Basic Primer. Clin J Am Soc Nephrol 2023; 18:394-396. [PMID: 36723176 PMCID: PMC10103344 DOI: 10.2215/cjn.0000000000000078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Yoonyoung Park
- IBM Research Cambridge, Cambridge, Massachusetts
- Moderna Inc., Cambridge, Massachusetts
| | - Jianying Hu
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York
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13
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Jha V. Renal Revolution: Anticipating the Next 25 Years of India's Kidney Care. Indian J Nephrol 2023; 33:79-82. [PMID: 37234432 PMCID: PMC10208534 DOI: 10.4103/ijn.ijn_125_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/05/2023] [Indexed: 05/28/2023] Open
Affiliation(s)
- Vivekanand Jha
- Executive Director, The George Institute for Global Health, New Delhi, India, School of Public Health, Imperial College, London, UK, Manipal Academy of Higher Education, Manipal, India
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14
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Díez-Sanmartín C, Cabezuelo AS, Belmonte AA. A new approach to predicting mortality in dialysis patients using sociodemographic features based on artificial intelligence. Artif Intell Med 2023; 136:102478. [PMID: 36710068 DOI: 10.1016/j.artmed.2022.102478] [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: 04/24/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/30/2022]
Abstract
One of the main problems that affect patients in dialysis therapy who are on the waiting list to receive a kidney transplant is predicting their survival time if they do not receive a transplant. This paper proposes a new approach to survival prediction based on artificial intelligence techniques combined with statistical methods to study the association between sociodemographic factors and patient survival on the waiting list if they do not receive a kidney transplant. This new approach consists of a first stage that uses the clustering techniques that are best suited to the data structure (K-Means, Mini Batch K-Means, Agglomerative Clustering and K-Modes) used to identify the risk profile of dialysis patients. Later, a new method called False Clustering Discovery Reduction is performed to determine the minimum number of populations to be studied, and whose mortality risk is statistically differentiable. This approach was applied to the OPTN medical dataset (n = 44,663). The procedure started from 11 initial clusters obtained with the Agglomerative technique, and was reduced to eight final risk populations, for which their Kaplan-Meier survival curves were provided. With this result, it is possible to make predictions regarding the survival time of a new patient who enters the waiting list if the sociodemographic profile of the patient is known. To do so, the predictive algorithm XGBoost is used, which allows the cluster to which it belongs to be predicted and the corresponding Kaplan-Meier curve to be associated with it. This prediction process is achieved with an overall Multi-class AUC of 99.08 %.
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Affiliation(s)
- Covadonga Díez-Sanmartín
- Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain.
| | - Antonio Sarasa Cabezuelo
- Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, 28040 Madrid, Spain
| | - Amado Andrés Belmonte
- Nephrology Department, 12 de Octubre Hospital, Complutense University of Madrid, 28041 Madrid, Spain.
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15
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Postoperative Intensive Care Unit Overtriage: An Application of Machine Learning. Ann Surg 2023; 277:186-187. [PMID: 35730429 DOI: 10.1097/sla.0000000000005541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Filler G, Gipson DS, Iyamuremye D, Díaz González de Ferris ME. Artificial Intelligence in Pediatric Nephrology-A Call for Action. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:17-24. [PMID: 36723276 DOI: 10.1053/j.akdh.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/24/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. This article provides an overview of medical artificial intelligence applications relevant to pediatric nephrology. We describe the core concepts of artificial intelligence and machine learning and cover the basics of neural networks and deep learning. We also discuss some examples for clinical applications of artificial intelligence in pediatric nephrology, including neonatal kidney function, early recognition of acute kidney injury, renally cleared drug dosing, intrapatient variability, urinary tract infection workup in infancy, and longitudinal disease progression. Furthermore, we consider the future of artificial intelligence in clinical pediatric nephrology and its potential impact on medical practice and address the ethical issues artificial intelligence raises in terms of clinical decision-making, health care provider-patient relationship, patient privacy, and data collection. This article also represents a call for action involving those of us striving to provide optimal services for children, adolescents, and young adults with chronic conditions.
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Affiliation(s)
- Guido Filler
- Division of Pediatric Nephrology, Departments of Paediatrics, Western University, London, Ontario, Canada; Departments of Medicine, Western University, London, Ontario, Canada; Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
| | - Debbie S Gipson
- Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
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17
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Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2023; 17:224-238. [PMID: 36121302 PMCID: PMC9846408 DOI: 10.1177/19322968221124583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.
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Affiliation(s)
| | | | - David G. Armstrong
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - Ashley N. Battarbee
- Center for Women’s Reproductive Health,
The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jorge Cuadros
- Meredith Morgan Optometric Eye Center,
University of California, Berkeley, Berkeley, CA, USA
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles,
University of Southern California, Los Angeles, CA, USA
| | | | | | - Mark A. Swerdlow
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - David C. Klonoff
- Diabetes Technology Society,
Burlingame, CA, USA
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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18
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Shi Y, Wang H, Bai L, Wu Y, Zhang L, Zheng X, Lv JH, Pei HH, Bai ZH. The rate of acute kidney injury (AKI) alert detection by the attending physicians was associated with the prognosis of patients with AKI. Front Public Health 2022; 10:1031529. [PMID: 36466503 PMCID: PMC9712962 DOI: 10.3389/fpubh.2022.1031529] [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: 08/30/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Early identification of AKI was always considered to improve patients' prognosis. Some studies found that AKI early warning tools didn't affect patients' prognosis. Therefore, additional studies were necessary to explore the reasons. Methods This study was a secondary analysis of a multicenter randomized controlled trial that found electronic health record warnings for AKI did not influence patients' prognoses. Univariate, multivariate, subgroup, curve fitting, and threshold effect analysis were used to explore the association between AKI warnings detected by attending physicians and the patient's prognosis. Results A total of 6,030 AKI patients were included in the study. The patients were classified into two groups based on the rate of AKI alerts detected by attending physicians: the partial group (n = 5,377), and the complete group (n = 653). In comparison to the partial group, the complete group significantly decreased 14-day AKI progression, 14-day dialysis, and 14-day mortality, with adjusted ORs of 0.48 (0.33, 0.70), 0.26 (0.09, 0.77), and 0.53 (0.33, 0.84) respectively, and the complete group significantly improve the discharge to home, with an OR value of 1.50 (1.21, 1.87). When the rate of AKI alerts detected by the attending physicians as a continuity variable, we found that the rate of alerts seen by attending physicians was associated with 14-day mortality and the discharge to home, with adjusted ORs of 1.76 (1.11, 2.81) and 1.42 (1.13, 1.80). The sensitivity analysis, curve-fitting analysis, and threshold effect analysis also showed that the rate of alert seen by the attending physician was correlated with the patient's prognosis. Conclusion The rate of AKI alert detection by attending physician were related to the patient's prognosis. The higher the rate of AKI alert detection by attending physicians, the better the prognosis of patients with AKI.
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Affiliation(s)
- Yu Shi
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hai Wang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ling Bai
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yuan Wu
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Li Zhang
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xin Zheng
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jun-hua Lv
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hong-hong Pei
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,*Correspondence: Hong-hong Pei
| | - Zheng-hai Bai
- Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Zheng-hai Bai
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19
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Sandys V, Sexton D, O'Seaghdha C. Artificial intelligence and digital health for volume maintenance in hemodialysis patients. Hemodial Int 2022; 26:480-495. [PMID: 35739632 PMCID: PMC9796027 DOI: 10.1111/hdi.13033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 05/18/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022]
Abstract
Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 aimed at fostering innovation in fluid management therapies. This review article focuses on the current limitations in our assessment of extracellular volume, and the novel technology and methods that can create a new paradigm for fluid management. The cardiology community has published research on multiparametric wearable devices that can create individualized predictions for heart failure events. In the future, similar wearable technology may be capable of tracking fluid changes during the interdialytic period and enabling behavioral change. Machine learning methods have shown promise in the prediction of volume-related adverse events. Similar methods can be leveraged to create accurate, automated predictions of dry weight that can potentially be used to guide ultrafiltration targets and interdialytic weight gain goals.
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Affiliation(s)
- Vicki Sandys
- Royal College of Surgeons in IrelandDublinIreland
| | - Donal Sexton
- St James's HospitalDublin 8Ireland,Trinity Health Kidney CentreSchool of Medicine, Trinity College DublinDublinIreland,ADAPT: Research Centre for AI‐Driven Digital Content TechnologyIreland
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