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Nguyen TTU, Nguyen AT, Kim H, Jung YJ, Park W, Kim KM, Park I, Kim W. Deep-learning model for evaluating histopathology of acute renal tubular injury. Sci Rep 2024; 14:9010. [PMID: 38637573 PMCID: PMC11026462 DOI: 10.1038/s41598-024-58506-9] [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: 08/08/2023] [Accepted: 03/30/2024] [Indexed: 04/20/2024] Open
Abstract
Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models to assist in the histopathological diagnosis of acute kidney injury. This study aimed to perform histopathological segmentation to identify the four structures of acute renal tubular injury using deep-learning models. A segmentation model was used to classify tubule-specific injuries following cisplatin treatment. A total of 45 whole-slide images with 400 generated patches were used in the segmentation model, and 27,478 annotations were created for four classes: glomerulus, healthy tubules, necrotic tubules, and tubules with casts. A segmentation model was developed using the DeepLabV3 architecture with a MobileNetv3-Large backbone to accurately identify the four histopathological structures associated with acute renal tubular injury in PAS-stained mouse samples. In the segmentation model for four structures, the highest Intersection over Union and the Dice coefficient were obtained for the segmentation of the "glomerulus" class, followed by "necrotic tubules," "healthy tubules," and "tubules with cast" classes. The overall performance of the segmentation algorithm for all classes in the test set included an Intersection over Union of 0.7968 and a Dice coefficient of 0.8772. The Dice scores for the glomerulus, healthy tubules, necrotic tubules, and tubules with cast are 91.78 ± 11.09, 87.37 ± 4.02, 88.08 ± 6.83, and 83.64 ± 20.39%, respectively. The utilization of deep learning in a predictive model has demonstrated promising performance in accurately identifying the degree of injured renal tubules. These results may provide new opportunities for the application of the proposed methods to evaluate renal pathology more effectively.
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Affiliation(s)
- Thi Thuy Uyen Nguyen
- Department of Histology, Embryology, Pathology and Forensic Medicine, Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam
| | - Anh-Tien Nguyen
- Department of Radiology, Chonnam National University and Hospital, Gwangju, Korea
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Hyeongwan Kim
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea
| | - Yu Jin Jung
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea
| | - Woong Park
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea
| | - Kyoung Min Kim
- Department of Pathology, Jeonbuk National University Medical School, Jeonju, Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University and Hospital, Gwangju, Korea.
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea.
- Department of Data Science, Chonnam National University, Gwangju, Korea.
| | - Won Kim
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea.
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea.
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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: 1] [Impact Index Per Article: 1.0] [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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Huang YL, Liu XQ, Huang Y, Jin FY, Zhao Q, Wu QY, Ma KL. Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy. J Clin Pathol 2023:jcp-2023-209215. [PMID: 38123970 DOI: 10.1136/jcp-2023-209215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset of 'real' data, and in reality, it should be robust, user-friendly and universally applicable. METHODS WSIs of primary IgAN were collected and annotated. The H-AI-L algorithm which could facilitate direct WSI viewing and potential ROI detection for clinicians was built on the cloud server of matpool, a shared internet-based service platform. Model performance was evaluated using F1-score, precision, recall and Matthew's correlation coefficient (MCC). RESULTS The F1-score of glomerular localisation in WSIs was 0.85 and 0.89 for the initial and pretrained models, respectively, with corresponding recall values of 0.79 and 0.83, and precision scores of 0.92 and 0.97. Dichotomous differentiation between global sclerotic (GS) and other glomeruli revealed F1-scores of 0.70 and 0.91, and MCC values of 0.55 and 0.87, for the initial and pretrained models, respectively. The overall F1-score of multiclassification was 0.81 for the pretrained models. The total glomerular recall rate was 0.96, with F1-scores of 0.68, 0.56 and 0.26 for GS, segmental glomerulosclerosis and crescent (C), respectively. Interstitial fibrosis/tubular atrophy lesion similarity between the true label and model predictions was 0.75. CONCLUSIONS Our results underscore the efficacy of the ML integration algorithm in segmenting ROIs in IgAN WSIs, and the internet-based model deployment is in favour of widespread adoption and utilisation across multiple centres and increased volumes of WSIs.
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Affiliation(s)
- Yu-Lin Huang
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiao Qi Liu
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Huang
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Feng Yong Jin
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qing Zhao
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qin Yi Wu
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Kun Ling Ma
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Altini N, Rossini M, Turkevi-Nagy S, Pesce F, Pontrelli P, Prencipe B, Berloco F, Seshan S, Gibier JB, Pedraza Dorado A, Bueno G, Peruzzi L, Rossi M, Eccher A, Li F, Koumpis A, Beyan O, Barratt J, Vo HQ, Mohan C, Nguyen HV, Cicalese PA, Ernst A, Gesualdo L, Bevilacqua V, Becker JU. Performance and limitations of a supervised deep learning approach for the histopathological Oxford Classification of glomeruli with IgA nephropathy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107814. [PMID: 37722311 DOI: 10.1016/j.cmpb.2023.107814] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/20/2023]
Abstract
BACKGROUND AND OBJECTIVE The Oxford Classification for IgA nephropathy is the most successful example of an evidence-based nephropathology classification system. The aim of our study was to replicate the glomerular components of Oxford scoring with an end-to-end deep learning pipeline that involves automatic glomerular segmentation followed by classification for mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S) and active crescents (C). METHODS A total number of 1056 periodic acid-Schiff (PAS) whole slide images (WSIs), coming from 386 kidney biopsies, were annotated. Several detection models for glomeruli, based on the Mask R-CNN architecture, were trained on 587 WSIs, validated on 161 WSIs, and tested on 127 WSIs. For the development of segmentation models, 20,529 glomeruli were annotated, of which 16,571 as training and 3958 as validation set. The test set of the segmentation module comprised of 2948 glomeruli. For the Oxford classification, 6206 expert-annotated glomeruli from 308 PAS WSIs were labelled for M, E, S, C and split into a training set of 4298 glomeruli from 207 WSIs, and a test set of 1908 glomeruli. We chose the best-performing models to construct an end-to-end pipeline, which we named MESCnn (MESC classification by neural network), for the glomerular Oxford classification of WSIs. RESULTS Instance segmentation yielded excellent results with an AP50 ranging between 78.2-80.1 % (79.4 ± 0.7 %) on the validation and 75.1-77.7 % (76.5 ± 0.9 %) on the test set. The aggregated Jaccard Index was between 73.4-75.9 % (75.0 ± 0.8 %) on the validation and 69.1-73.4 % (72.2 ± 1.4 %) on the test set. At granular glomerular level, Oxford Classification was best replicated for M with EfficientNetV2-L with a mean ROC-AUC of 90.2 % and a mean precision/recall area under the curve (PR-AUC) of 81.8 %, best for E with MobileNetV2 (ROC-AUC 94.7 %) and ResNet50 (PR-AUC 75.8 %), best for S with EfficientNetV2-M (mean ROC-AUC 92.7 %, mean PR-AUC 87.7 %), best for C with EfficientNetV2-L (ROC-AUC 92.3 %) and EfficientNetV2-S (PR-AUC 54.7 %). At biopsy-level, correlation between expert and deep learning labels fulfilled the demands of the Oxford Classification. CONCLUSION We designed an end-to-end pipeline for glomerular Oxford Classification on both a granular glomerular and an entire biopsy level. Both the glomerular segmentation and the classification modules are freely available for further development to the renal medicine community.
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Affiliation(s)
- Nicola Altini
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n.4, Bari 70126, Italy
| | - Michele Rossini
- Nephrology Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Sándor Turkevi-Nagy
- Department of Pathology, Albert Szent-Györgyi Health Center, University of Szeged, Szeged, Hungary
| | - Francesco Pesce
- Nephrology Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy; Division of Renal Medicine, "Fatebenefratelli Isola Tiberina - Gemelli Isola", Rome, Italy
| | - Paola Pontrelli
- Nephrology Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Berardino Prencipe
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n.4, Bari 70126, Italy
| | - Francesco Berloco
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n.4, Bari 70126, Italy
| | - Surya Seshan
- Department of Pathology, Weill-Cornell Medical Center/New York Presbyterian Hospital, New York, NY, USA
| | - Jean-Baptiste Gibier
- Department of Pathology, Pathology Institute, Lille University Hospital (CHU), Lille, France
| | | | - Gloria Bueno
- VISILAB Research Group, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Licia Peruzzi
- AOU Città della Salute e della Scienza di Torino, Regina Margherita Children's Hospital, Turin, Italy
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, Verona, Italy
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Feifei Li
- Faculty of Medicine, University Hospital Cologne, University of Cologne, Institute for Medical Informatics, Cologne, Germany
| | - Adamantios Koumpis
- Faculty of Medicine, University Hospital Cologne, University of Cologne, Institute for Medical Informatics, Cologne, Germany
| | - Oya Beyan
- Faculty of Medicine, University Hospital Cologne, University of Cologne, Institute for Medical Informatics, Cologne, Germany
| | - Jonathan Barratt
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Huy Quoc Vo
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Chandra Mohan
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Hien Van Nguyen
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | | | - Angela Ernst
- Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany
| | - Loreto Gesualdo
- Nephrology Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n.4, Bari 70126, Italy; Apulian Bioengineering s.r.l., Via delle Violette n.14, Modugno 70026, Italy.
| | - Jan Ulrich Becker
- Institute of Pathology, University Hospital of Cologne, Cologne, Germany
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Gaupp C, Schmid B, Tripal P, Edwards A, Daniel C, Zimmermann S, Goppelt-Struebe M, Willam C, Rosen S, Schley G. Reconfiguration and loss of peritubular capillaries in chronic kidney disease. Sci Rep 2023; 13:19660. [PMID: 37952029 PMCID: PMC10640592 DOI: 10.1038/s41598-023-46146-4] [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: 02/03/2023] [Accepted: 10/27/2023] [Indexed: 11/14/2023] Open
Abstract
Functional and structural alterations of peritubular capillaries (PTCs) are a major determinant of chronic kidney disease (CKD). Using a software-based algorithm for semiautomatic segmentation and morphometric quantification, this study analyzes alterations of PTC shape associated with chronic tubulointerstitial injury in three mouse models and in human biopsies. In normal kidney tissue PTC shape was predominantly elongated, whereas the majority of PTCs associated with chronic tubulointerstitial injury had a rounder shape. This was reflected by significantly reduced PTC luminal area, perimeter and diameters as well as by significantly increased circularity and roundness. These morphological alterations were consistent in all mouse models and human kidney biopsies. The mean circularity of PTCs correlated significantly with categorized glomerular filtration rates and the degree of interstitial fibrosis and tubular atrophy (IFTA) and classified the presence of CKD or IFTA. 3D reconstruction of renal capillaries revealed not only a significant reduction, but more importantly a substantial simplification and reconfiguration of the renal microvasculature in mice with chronic tubulointerstitial injury. Computational modelling predicted that round PTCs can deliver oxygen more homogeneously to the surrounding tissue. Our findings indicate that alterations of PTC shape represent a common and uniform reaction to chronic tubulointerstitial injury independent of the underlying kidney disease.
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Affiliation(s)
- Charlotte Gaupp
- Department of Nephrology and Hypertension, Friedrich-Alexander University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Benjamin Schmid
- Optical Imaging Center Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Philipp Tripal
- Optical Imaging Center Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Aurélie Edwards
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Christoph Daniel
- Department of Nephropathology, Friedrich-Alexander University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Erlangen, Germany
| | - Stefan Zimmermann
- Department of Computer Science, University of Applied Sciences Worms, Worms, Germany
| | - Margarete Goppelt-Struebe
- Department of Nephrology and Hypertension, Friedrich-Alexander University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Carsten Willam
- Department of Nephrology and Hypertension, Friedrich-Alexander University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Seymour Rosen
- Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Gunnar Schley
- Department of Nephrology and Hypertension, Friedrich-Alexander University Erlangen-Nürnberg (FAU) and University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany.
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Liu X, Wu Y, Chen Y, Hui D, Zhang J, Hao F, Lu Y, Cheng H, Zeng Y, Han W, Wang C, Li M, Zhou X, Zheng W. Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators. Comput Biol Med 2023; 166:107470. [PMID: 37722173 DOI: 10.1016/j.compbiomed.2023.107470] [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: 06/15/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 09/20/2023]
Abstract
Diagnosis of diabetic kidney disease (DKD) mainly relies on screening the morphological variations and internal lesions of glomeruli from pathological kidney biopsy. The prominent pathological alterations of glomeruli for DKD include glomerular hypertrophy and nodular mesangial sclerosis. However, the qualitative judgment of these alterations is inaccurate and inconstant due to the intra- and inter-subject variability of pathologists. It is necessary to design artificial intelligence (AI) methods for accurate quantification of these pathological alterations and outcome prediction of DKD. In this work, we present an AI-driven framework to quantify the volume of glomeruli and degree of nodular mesangial sclerosis, respectively, based on an instance segmentation module and a novel weakly supervised Macro-Micro Aggregation (MMA) module. Subsequently, we construct classic machine learning models to predict the degree of DKD based on three selected pathological indicators via factor analysis. These corresponding modules are trained and tested on a total of 281 whole slide images (WSIs) digitized from two hospitals with different scanners. Our designed AI framework achieved inspiring results with 0.926 mIoU for glomerulus segmentation, and 0.899 F1 score for glomerulus classification in the external testing dataset. Meantime, the visualized results of the MMA module could reflect the location of the lesions. The performance of predicting disease achieved the F1 score of 0.917, which further proved the effectiveness of our AI-driven quantification of pathological indicators. Additionally, the interpretation of the machine learning model with the SHAP method showed similar accordance with the development of DKD in pathology. In conclusion, the proposed auxiliary diagnostic technologies have the feasibility for quantitative analysis of glomerular pathological tissues and alterations in DKD. Pathological quantitative indicators will also make it more convenient to provide doctors with assistance in clinical practice.
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Affiliation(s)
- Xueyu Liu
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yongfei Wu
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
| | - Yilin Chen
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Dongna Hui
- Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Jianan Zhang
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Fang Hao
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yuanyue Lu
- Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Hangbei Cheng
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yue Zeng
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Weixia Han
- Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Chen Wang
- Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ming Li
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiaoshuang Zhou
- Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
| | - Wen Zheng
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
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Besusparis J, Morkunas M, Laurinavicius A. A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images. J Imaging 2023; 9:220. [PMID: 37888327 PMCID: PMC10607091 DOI: 10.3390/jimaging9100220] [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: 09/07/2023] [Revised: 09/26/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. MATERIALS AND METHODS We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. RESULTS A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers. CONCLUSIONS We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.
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Affiliation(s)
- Justinas Besusparis
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| | - Mindaugas Morkunas
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| | - Arvydas Laurinavicius
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
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9
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Kaur G, Garg M, Gupta S, Juneja S, Rashid J, Gupta D, Shah A, Shaikh A. Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model. Diagnostics (Basel) 2023; 13:3152. [PMID: 37835895 PMCID: PMC10572820 DOI: 10.3390/diagnostics13193152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/23/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.
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Affiliation(s)
- Gurjinder Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Meenu Garg
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sapna Juneja
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Junaid Rashid
- Department of Data Science, Sejong University, Seoul 05006, Republic of Korea;
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Asadullah Shah
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia;
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10
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Denic A, Gaddam M, Moustafa A, Mullan AF, Luehrs AC, Sharma V, Thompson RH, Smith ML, Alexander MP, Lerman LO, Barisoni L, Rule AD. Tubular and Glomerular Size by Cortex Depth as Predictor of Progressive CKD after Radical Nephrectomy for Tumor. J Am Soc Nephrol 2023; 34:1535-1545. [PMID: 37430426 PMCID: PMC10482069 DOI: 10.1681/asn.0000000000000180] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/08/2023] [Indexed: 07/12/2023] Open
Abstract
SIGNIFICANCE STATEMENT Glomerular size differs by cortex depth. Larger nephrons are prognostic of progressive kidney disease, but it is unknown whether this risk differs by cortex depth or by glomeruli versus proximal or distal tubule size. We studied the average minor axis diameter in oval proximal and distal tubules separately and by cortex depth in patients who had radical nephrectomy to remove a tumor from 2019 to 2020. In adjusted analyses, larger glomerular volume in the middle and deep cortex predicted progressive kidney disease. Wider proximal tubular diameter did not predict progressive kidney disease independent of glomerular volume. Wider distal tubular diameter showed a gradient of strength of prediction of progressive kidney disease in the more superficial cortex than in the deep cortex. BACKGROUND Larger nephrons are prognostic of progressive kidney disease, but whether this risk differs by nephron segments or by depth in the cortex is unclear. METHODS We studied patients who underwent radical nephrectomy for a tumor between 2000 and 2019. Large wedge kidney sections were scanned into digital images. We estimated the diameters of proximal and distal tubules by the minor axis of oval tubular profiles and estimated glomerular volume with the Weibel-Gomez stereological model. Analyses were performed separately in the superficial, middle, and deep cortex. Cox proportional hazard models assessed the risk of progressive CKD (dialysis, kidney transplantation, sustained eGFR <10 ml/min per 1.73 m 2 , or a sustained 40% decline from the postnephrectomy baseline eGFR) with glomerular volume or tubule diameters. At each cortical depth, models were unadjusted, adjusted for glomerular volume or tubular diameter, and further adjusted for clinical characteristics (age, sex, body mass index, hypertension, diabetes, postnephrectomy baseline eGFR, and proteinuria). RESULTS Among 1367 patients were 62 progressive CKD events during a median follow-up of 4.5 years. Glomerular volume predicted CKD outcomes at all depths, but only in the middle and deep cortex after adjusted analyses. Proximal tubular diameter also predicted progressive CKD at any depth but not after adjusted analyses. Distal tubular diameter showed a gradient of more strongly predicting progressive CKD in the superficial than deep cortex, even in adjusted analysis. CONCLUSIONS Larger glomeruli are independent predictors of progressive CKD in the deeper cortex, whereas in the superficial cortex, wider distal tubular diameters are an independent predictor of progressive CKD.
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Affiliation(s)
- Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Mrunanjali Gaddam
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Amr Moustafa
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Aidan F. Mullan
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Anthony C. Luehrs
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Vidit Sharma
- Department of Urology, Mayo Clinic, Rochester, Minnesota
| | | | - Maxwell L. Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Lilach O. Lerman
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Laura Barisoni
- Department of Pathology and Medicine, Duke University, Durham, North Carolina
| | - Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
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11
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Ayorinde JOO, Citterio F, Landrò M, Peruzzo E, Islam T, Tilley S, Taylor G, Bardsley V, Liò P, Samoshkin A, Pettigrew GJ. Artificial Intelligence You Can Trust: What Matters Beyond Performance When Applying Artificial Intelligence to Renal Histopathology? J Am Soc Nephrol 2022; 33:2133-2140. [PMID: 36351761 PMCID: PMC9731632 DOI: 10.1681/asn.2022010069] [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] [Indexed: 11/11/2022] Open
Abstract
Although still in its infancy, artificial intelligence (AI) analysis of kidney biopsy images is anticipated to become an integral aspect of renal histopathology. As these systems are developed, the focus will understandably be on developing ever more accurate models, but successful translation to the clinic will also depend upon other characteristics of the system.In the extreme, deployment of highly performant but "black box" AI is fraught with risk, and high-profile errors could damage future trust in the technology. Furthermore, a major factor determining whether new systems are adopted in clinical settings is whether they are "trusted" by clinicians. Key to unlocking trust will be designing platforms optimized for intuitive human-AI interactions and ensuring that, where judgment is required to resolve ambiguous areas of assessment, the workings of the AI image classifier are understandable to the human observer. Therefore, determining the optimal design for AI systems depends on factors beyond performance, with considerations of goals, interpretability, and safety constraining many design and engineering choices.In this article, we explore challenges that arise in the application of AI to renal histopathology, and consider areas where choices around model architecture, training strategy, and workflow design may be influenced by factors beyond the final performance metrics of the system.
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Affiliation(s)
- John O O Ayorinde
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
| | | | | | | | | | | | | | - Victoria Bardsley
- Department of Histopathology, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Alex Samoshkin
- Office for Translational Research, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Gavin J Pettigrew
- Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
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12
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Kawazoe Y, Shimamoto K, Yamaguchi R, Nakamura I, Yoneda K, Shinohara E, Shintani-Domoto Y, Ushiku T, Tsukamoto T, Ohe K. Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy. Diagnostics (Basel) 2022; 12:diagnostics12122955. [PMID: 36552963 PMCID: PMC9776670 DOI: 10.3390/diagnostics12122955] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/20/2022] [Accepted: 11/20/2022] [Indexed: 11/29/2022] Open
Abstract
The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipelines showed a mean intersection over union (IoU) of 0.670 and 0.693 for five classes (i.e., background, Bowman's space, glomerular tuft, crescentic, and sclerotic regions) against the WSI of its facility, and 0.678 and 0.609 against the WSI of the external facility. The multivariate analysis revealed that the predicted sclerotic regions, even those that were predicted by the external model, had a significant negative impact on the slope of the estimated glomerular filtration rate after biopsy. This is the first study to demonstrate that the quantified sclerotic regions that are predicted by an automated computational pipeline for the segmentation of the histopathological glomerular components on WSIs impact the prognosis of kidney function in patients with IgAN.
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Affiliation(s)
- Yoshimasa Kawazoe
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
- Correspondence: ; Tel.: +81-3-5800-9077
| | - Kiminori Shimamoto
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Ryohei Yamaguchi
- Ohshima Memorial Kisen Hospital, 3-5-15, Misaki, Chiba 274-0812, Japan
| | - Issei Nakamura
- NTT DOCOMO, Inc., Sanno Park Tower, 2-11-1, Nagata-cho, Chiyoda-ku, Tokyo 100-6150, Japan
| | - Kota Yoneda
- Department of Reproductive, Developmental, and Aging Sciences, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Emiko Shinohara
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yukako Shintani-Domoto
- Department of Diagnostic Pathology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo 113-8602, Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tatsuo Tsukamoto
- Department of Nephrology and Dialysis, Tazuke Kofukai Medical Research Institute, Kitano Hospital, 2-4-20, Ohgimachi, Kita-ku, Osaka 530-8480, Japan
| | - Kazuhiko Ohe
- Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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13
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Tao Y, Hu H, Li J, Li M, Zheng Q, Zhang G, Ni M. A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of PJI. ARTHROPLASTY (LONDON, ENGLAND) 2022; 4:49. [PMID: 36229852 PMCID: PMC9563129 DOI: 10.1186/s42836-022-00145-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 08/10/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE This study aimed to establish a deep learning method based on convolutional networks for the preliminary study of the pathological diagnosis of prosthetic joint infections (PJI). METHODS We enrolled 20 revision patients after joint replacement from the Department of Orthopedics, the First Medical Center, General Hospital of the People's Liberation Army, from January 2021 to January 2022 (10 of whom were confirmed to be infected against 2018 ICM criteria, and the remaining 10 were verified to be non-infected), and classified high-power field images according to 2018 ICM criteria. Then, we inputted 576 positive images and 576 negative images into a neural network by employing a resNET model, used to select 461 positive images and 461 negative images as training sets, 57 positive images and 31 negative images as internal verification sets, 115 positive images and 115 negative images as external test sets. RESULTS The resNET model classification was used to analyze the pathological sections of PJI patients under high magnification fields. The results of internal validation set showed a positive accuracy of 96.49%, a negative accuracy of 87.09%, an average accuracy of 93.22%, an average recall rate 96.49%, and an F1 of 0.9482. The accuracy of external test results was 97.39% positive, 93.04% negative, the average accuracy of external test set was 93.33%, the average recall rate was 97.39%, with an F1 of 0.9482. The AUC area of the intelligent image-reading diagnosis system was 0.8136. CONCLUSIONS This study used the convolutional neural network deep learning to identify high-magnification images from pathological sections of soft tissues around joints, against the diagnostic criteria for acute infection, and a high precision and a high recall rate were accomplished. The results of this technique confirmed that better results could be achieved by comparing the new method with the standard strategies in terms of diagnostic accuracy. Continuous upgrading of extended training sets is needed to improve the diagnostic accuracy of the convolutional network deep learning before it is applied to clinical practice.
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Affiliation(s)
- Ye Tao
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
| | - Hanwen Hu
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
| | - Jie Li
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
| | - Mengting Li
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
| | - Qingyuan Zheng
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
| | - Guoqiang Zhang
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
| | - Ming Ni
- grid.414252.40000 0004 1761 8894Department of Orthopedics, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Beijing, 100036 China
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14
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Shaikh S. Editorial for "Attention-Based Deep Learning for the Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI". J Magn Reson Imaging 2022; 57:1854-1855. [PMID: 36208098 DOI: 10.1002/jmri.28466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 11/07/2022] Open
Affiliation(s)
- Sikandar Shaikh
- Department of Radiology, Shadan Institute of Medical Sciences, Hyderabad, India
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15
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Silva J, Souza L, Chagas P, Calumby R, Souza B, Pontes I, Duarte A, Pinheiro N, Santos W, Oliveira L. Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization. Comput Med Imaging Graph 2022; 100:102104. [PMID: 36007483 DOI: 10.1016/j.compmedimag.2022.102104] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/28/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022]
Abstract
The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures in the tissue. Considering the glomerulus as one of the first structures analyzed by pathologists, we propose here a novel convolutional neural network for glomerulus segmentation. Our end-to-end network, named DS-FNet, combines the strengths of semantic segmentation and semantic boundary detection networks via an attention-aware mechanism. Although we trained the proposed network on periodic acid-Schiff (PAS)-stained WSIs, we found that our network was capable to segment glomeruli on WSIs stained with different techniques, such as periodic acid-methenamine silver (PAMS), hematoxylin-eosin (HE), and Masson trichrome (TRI). To assess the performance of the proposed method, we used three public data sets: HuBMAP (available in a Kaggle competition), a subset of the NEPTUNE data set, and a novel challenging data set, called WSI_Fiocruz. We compared the DS-FNet with six other deep learning networks: original U-Net, our attention version of U-Net called AU-Net, U-Net++, U-Net3Plus, ResU-Net, and DeepLabV3+. Results showed that DS-FNet achieved equivalent or superior results on all data sets: On the HuBMAP data set, it reached a dice score (DSC) of 95.05%, very close to the first place (95.15%); on the NEPTUNE and WSI_Fiocruz data sets, DS-FNet obtained the highest average DSC, whether on PAS-stained images or images stained with other techniques. To the best we know, this is the first work to show consistently high performance in a one-to-many-stain glomerulus segmentation following a thorough protocol on data sets from different medical labs.
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Affiliation(s)
- Jefferson Silva
- Universidade Federal do Maranhão, Brazil; Universidade Federal da Bahia, Brazil
| | | | | | | | - Bianca Souza
- Universidade Federal da Bahia, Brazil; Fundação Oswaldo Cruz, Brazil
| | | | | | | | - Washington Santos
- Universidade Federal da Bahia, Brazil; Fundação Oswaldo Cruz, Brazil
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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Jiang J, Chan L, Nadkarni GN. The promise of artificial intelligence for kidney pathophysiology. Curr Opin Nephrol Hypertens 2022; 31:380-386. [PMID: 35703218 PMCID: PMC10309072 DOI: 10.1097/mnh.0000000000000808] [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] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. RECENT FINDINGS We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. SUMMARY The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
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Affiliation(s)
- Joy Jiang
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lili Chan
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N. Nadkarni
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Klepaczko A, Majos M, Stefańczyk L, Ejkefjord E, Lundervold A. Whole kidney and renal cortex segmentation in contrast-enhanced MRI using a joint classification and segmentation convolutional neural network. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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19
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Xu F, Zhu C, Tang W, Wang Y, Zhang Y, Li J, Jiang H, Shi Z, Liu J, Jin M. Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides. Front Oncol 2021; 11:759007. [PMID: 34722313 PMCID: PMC8551965 DOI: 10.3389/fonc.2021.759007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 09/21/2021] [Indexed: 12/22/2022] Open
Abstract
Objectives To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012). Conclusion Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
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Affiliation(s)
- Feng Xu
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Chuang Zhu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Wenqi Tang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Wang
- Department of Pathology, Beijing Chao-Yang Hospital, Beijing, China
| | - Yu Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jie Li
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Hongchuan Jiang
- Department of Breast Surgery, Beijing Chao-Yang Hospital, Beijing, China
| | - Zhongyue Shi
- Department of Pathology, Beijing Chao-Yang Hospital, Beijing, China
| | - Jun Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Mulan Jin
- Department of Pathology, Beijing Chao-Yang Hospital, Beijing, China
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