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Chen J, Chen R, Chen L, Zhang L, Wang W, Zeng X. Kidney medicine meets computer vision: a bibliometric analysis. Int Urol Nephrol 2024:10.1007/s11255-024-04082-w. [PMID: 38814370 DOI: 10.1007/s11255-024-04082-w] [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: 02/27/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. METHODS The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. RESULTS There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. CONCLUSION The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.
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Affiliation(s)
- Junren Chen
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Rui Chen
- The Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Liangyin Chen
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Lei Zhang
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Wei Wang
- School of Automation, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Xiaoxi Zeng
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China.
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He S, Zou Y, Li B, Peng F, Lu X, Guo H, Tan X, Chen Y. An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet. Sci Rep 2024; 14:1033. [PMID: 38200109 PMCID: PMC10781987 DOI: 10.1038/s41598-024-51651-1] [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: 07/23/2023] [Accepted: 01/08/2024] [Indexed: 01/12/2024] Open
Abstract
The percent global glomerulosclerosis is a key factor in determining the outcome of renal transfer surgery. At present, the rate is typically computed by pathologists, which is labour intensive and nonstandardized. With the development of Deep Learning (DL), DL-based segmentation models can be used to better identify and segment normal and sclerosed glomeruli. Based on this, we can better quantify percent global glomerulosclerosis to reduce the discard rate of donor kidneys. We used 51 whole slide images (WSIs) from different institutions that are publicly available on the internet. However, the number of sclerosed glomeruli is much smaller than that of normal glomeruli in different WSIs, which can reduce the effectiveness of Deep Learning. For better sclerosed glomerular identification and segmentation performance, we modified and trained a GAN (generative adversarial network)-based image inpainting model to obtain more synthetic sclerosed glomeruli. Our proposed inpainting method achieved an average SSIM (Structural Similarity) of 0.8086 and an average PSNR (Peak Signal-to-Noise Ratio) of 22.8943 dB in the area of generated sclerosed glomeruli. We obtained sclerosed glomerular segmentation performance improvement by adding synthetic sclerosed glomerular images and achieved the best Dice of glomerular segmentation in different test sets based on the modified Unet model.
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Affiliation(s)
- Songping He
- Digital Manufacturing Equipment National Engineering Research Center, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Zou
- National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan, China
| | - Bin Li
- Digital Manufacturing Equipment National Engineering Research Center, Huazhong University of Science and Technology, Wuhan, China
| | - Fangyu Peng
- National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan, China
| | - Xia Lu
- Key Laboratory of Organ Transplantation of Ministry of Education, Institute of Organ Transplantation, Tongji Hospital, Tongji Medical College, National Health Commission and Chinese Academy of Medical Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Guo
- Key Laboratory of Organ Transplantation of Ministry of Education, Institute of Organ Transplantation, Tongji Hospital, Tongji Medical College, National Health Commission and Chinese Academy of Medical Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Tan
- Wuhan Intelligent Equipment Industrial Institute Co Ltd, Wuhan, China
| | - Yanyan Chen
- Department of Information Management, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Cazzaniga G, Rossi M, Eccher A, Girolami I, L'Imperio V, Van Nguyen H, Becker JU, Bueno García MG, Sbaraglia M, Dei Tos AP, Gambaro G, Pagni F. Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions. J Nephrol 2024; 37:65-76. [PMID: 37768550 PMCID: PMC10920416 DOI: 10.1007/s40620-023-01775-w] [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: 05/10/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. METHODS Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included. RESULTS Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification. CONCLUSION Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools.
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Affiliation(s)
- Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy.
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, Piazzale Aristide Stefani, 1, 37126, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Ilaria Girolami
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy
| | - Hien Van Nguyen
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77004, USA
| | - Jan Ulrich Becker
- Institute of Pathology, University Hospital of Cologne, Cologne, Germany
| | - María Gloria Bueno García
- VISILAB Research Group, E.T.S. Ingenieros Industriales, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Marta Sbaraglia
- Department of Pathology, Azienda Ospedale-Università Padova, Padua, Italy
- Department of Medicine, University of Padua School of Medicine, Padua, Italy
| | - Angelo Paolo Dei Tos
- Department of Pathology, Azienda Ospedale-Università Padova, Padua, Italy
- Department of Medicine, University of Padua School of Medicine, Padua, Italy
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, Piazzale Aristide Stefani, 1, 37126, Verona, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy
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Išgum I, Landman BA, Vrtovec T. Special Section Guest Editorial: Advances in High-Dimensional Medical Image Processing. J Med Imaging (Bellingham) 2022; 9:052401. [PMID: 36330041 PMCID: PMC9621447 DOI: 10.1117/1.jmi.9.5.052401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Abstract
Guest Editors Ivana Išgum, Bennett A. Landman, and Tomaž Vrtovec introduce the JMI Special Section on Advances in High-Dimensional Medical Image Processing.
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Affiliation(s)
- Ivana Išgum
- Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
| | | | - Tomaž Vrtovec
- University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia
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