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Paradigmatic patterns of glomerular diseases. In terms of conventional morphology, the fundamental glomerular changes were attributed to 9 patterns (Nrs. 01-09). Extraglomerular structures were labeled as 'default' pattern 09. MPGN, membranoproliferative glomerulonephritis
Source publication
Background
Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed t...
Citations
... As a result, specific pathological patterns and classifications of severity have been defined for each possible cause of the disease (e.g., diabetic nephropathy [4] and IgA nephropathy [5]). In the field of glomerular pathology analysis, various AI systems AUTOMATED SCORING OF GLOMERULAR INJURY 4 have been reported, including AI for diagnosing membranous nephropathy [6], AI for classifying the severity of diabetic nephropathy [7], AI for classifying the severity of IgA nephropathy (MEST-C) [8], AI for classifying the severity of lupus nephritis [9], AI for detecting specific glomerular lesions or classifying lesion patterns [10][11][12][13], and AI for diagnosing nephropathies [14]. These projects were primarily developed for use in clinical settings. ...
Several artificial intelligence (AI) systems have been developed for glomerular pathology analysis in clinical settings. However, the application of AI systems in nonclinical fields remains limited. In this study, we trained a convolutional neural network model, which is an AI algorithm, to classify the severity of Tensin 2 (TNS2)-deficient nephropathy into seven categories. A dataset consisting of 803 glomerular images was generated from kidney sections of TNS2-deficient and wild-type mice. Manual evaluations of the images were conducted to assess their glomerular injury scores. The trained AI achieved approximately 70% accuracy in predicting the glomerular injury score for TNS2-deficient nephropathy. However, the AI achieved approximately 100% accuracy when considering predictions within one score of the true label as correct. The AI’s predicted mean score closely matched the true mean score. In conclusion, while the AI model may not replace human judgment entirely, it can serve as a reliable second assessor in scoring glomerular injury, offering potential benefits in enhancing the accuracy and objectivity of such assessments.
... Glomerular diseases are key information reflecting the etiology of CKD, so recognizing pathological manifestations is crucial. 3,4 Generally, the diameter of the adult glomerulus is about 100 microns, and the distance between two sections of biopsy tissue is about 3-5 microns. Since lesions can appear at any location, it is necessary to use several profiles to reflect richer information about the lesions. ...
... This paper has also constructed a practical human-computer interface, laying the foundation for efficient renal pathology diagnosis and AI-assisted diagnosis. To comprehensively evaluate glomerular lesions for glomerular disease diagnosis, each case was consecutively cut into 12 levels (named levels [1][2][3][4][5][6][7][8][9][10][11][12], with the staining of the first and last six levels being H&E, PAS, MT, PASM, H&E, PAS. Note that levels 1 and 7 are placed on the same slide, level 2 and 8 are placed on the same slide, and so on. ...
The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.
... Diese benötigen zum Training typischerweise große Fallzahlen und Gewebemengen. Obwohl in der digitalen Nephropathologie auch End-to-end-Ansätze existieren [17,21,24], liegt ein wesentlicher Forschungsfokus auf Systemen, die in einem explorativen Ansatz interpretierbare quantitative Daten liefern [3,8,9,12,13,15,20,26]. Diese lassen sich post hoc mit klinischen oder anderen Daten verbinden. ...
Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes.
To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions.
Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design.
Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.
... Thus, the features it relies on are trained for real-world objects like plants or trees and not histological objects. It was trained using cross-entropy loss, learning rate scheduler and stochastic gradient descend optimizer as described previously in Weis et al. [31]. The target output (or clinical output) has been the nodal status (N− or N+) of the case from which the image tiles come. ...
Background: In this study focusing on colorectal carcinoma (CRC), we address the imperative task of predicting post-surgery treatment needs by identifying crucial tumor features within whole slide images of solid tumors, analogous to locating a needle in a histological haystack. We evaluate two approaches to address this challenge using a small CRC dataset. Methods: First, we explore a conventional tile-level training approach, testing various data augmentation methods to mitigate the memorization effect in a noisy label setting. Second, we examine a multi-instance learning (MIL) approach at the case level, adapting data augmentation techniques to prevent over-fitting in the limited data set context. Results: The tile-level approach proves ineffective due to the limited number of informative image tiles per case. Conversely, the MIL approach demonstrates success for the small dataset when coupled with post-feature vector creation data augmentation techniques. In this setting, the MIL model accurately predicts nodal status corresponding to expert-based budding scores for these cases. Conclusions: This study incorporates data augmentation techniques into a MIL approach, highlighting the effectiveness of the MIL method in detecting predictive factors such as tumor budding, despite the constraints of a limited dataset size.
... Recent progress in digital image analysis and machine-learning applications has opened new prospects for automated renal pathology assays for both segmentation and quantification tasks [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Several studies have shown deep-learning algorithms for automated recognition and segmentation of kidney histology compartments. ...
... Weis et al., tested various CNN methods and proposed a CNNbased approach to simultaneously assess various glomerular lesions with convincingly good classification results (Cohen's kappa values 0.838-0.938) [33]. Yang et al., explored the possibilities of an integrated classification model to determine various patterns of glomerular disease in whole slide images with AUC values ranging from 0.687 to 0.947 for scorable glomerular lesions [31]. ...
... To the best of our knowledge, until now, no CNN-based approach has been reported that is focused on intraglomerular classification and quantification of essential injury patterns for grading systems to be tested against manually predefined regions. Previous experiments achieved acceptable accuracies for glomerular injury classification with a gradient-weighted class activation mapping technique (Grad-CAM) to visualize the performance of the classifier [31,33,40]. This technique is a post hoc neural network attention method that is not utilized for classifier training. ...
Unlabelled:
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.
... Weis et al. trained the use of 12 existing convolutional neural network models to classify nine glomerular morphological patterns and achieved good classification results with kappa values ranging from 0.838 to 0.938. Among the models, ResNet152 showed the best classification results with an accuracy of 0.944 and a kappa value of 0.938 [9]. ...
Classification of glomerular pathology based on histology sections is the key to diagnose the type and degree of kidney diseases. To address problems in the classification of glomerular lesions in children, a deep learning-based complete glomerular classification framework was designed to detect and classify glomerular pathology. A neural network integrating Resnet and Senet (RS-INet) was proposed and a glomerular classification algorithm implemented to achieve high-precision classification of glomerular pathology. SE-Resnet was applied with improvement by transforming the convolutional layer of the original Resnet residual block into a convolutional block with smaller parameters as well as reduced network parameters on the premise of ensuring network performance. Experimental results showed that our algorithm had the best performance in differentiating mesangial proliferative glomerulonephritis (MsPGN), crescent glomerulonephritis (CGN), and glomerulosclerosis (GS) from normal glomerulus (Normal) compared with other classification algorithms. The accuracy rates were 0.960, 0.940, 0.937, and 0.968, respectively. This suggests that the classification algorithm proposed in the present study is able to identify glomerular lesions with a higher precision, and distinguish similar glomerular pathologies from each other.
... "Detection and quanti cation" was the sixth theme that was extracted after analysis of the studies. We found that AI is applied in automated detection and quanti cation, automatic detection of thyroid diseases, Covid-19 detection and prevention, detection and classi cation of different classes of colorectal cancer (CC), detection to minimize incomplete colon capsule endoscopy (CCE), detection of disease and differentiation of pathology, detection of malignant arrhythmias, early detection of cardiac events, early fertility detection, non-invasive detection of atherosclerotic coronary artery aneurysms (CAA), proper detection and treatment of oral cancer (OC), and discrete recognition (29,56,(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70). Therefore, AI's ability in detection and quanti cation of disease should not be underestimated; rather, this feature should be optimized. ...
Background: Artificial intelligence (AI) has several potential applications in medicine, creating opportunities for reliable and evidence based decision making in disease management. Thus, the practical aspects of AI in decision-making should be identified. This study was conducted to identify AI applications in decision making for disease management.
Method: This study was a systematic review using the PRISMA-ScR checklist. Data collection was carried out by searching the related keywords in WOS and Scopus in May 2023.
Results: Regarding the AI applications in decision making for disease management, we found 80 sub-themes which were categorized into six themes, i.e. 1) Processing and managing data, 2) Characterization and analysis, 3) Prediction and risk stratification, 4) Screening, 5) Prognosis, and 6) Diagnosis.
Conclusion: AI has considerable capability in disease treatment and would be an integral part of medicine in the future. This study clearly identified six main themes that addressed AI capability in decision making for disease management. The use of AI can help in making medical decisions with more trust and confidence and thus make medical interventions more accurate and effective.
... With advancements in digital pathology, whole-slide image technology allows high-resolution scans of complete tissue biopsy slides that can also be subjected to AI-based images analysis. Examples of applications of AI in digital pathology include the detection and segmentation of kidney structures, the auxiliary diagnosis of renal pathological changes, and CKD prognosis [71][72][73][74]. The application of AI to whole-slide image analysis increases the power of detecting subtle changes, compared with visual assessment by clinicians, and overcomes the subjectivity of manual classification induced by differences in visual perceptions and preference. ...
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
... The present learning-based work is performed by full supervision on the construction of high-accuracy datasets. Some work is carried out for the glomerulus with a single lesion [10] [11]. [12] proposes to use an uncertainty-aware module to improve the model's ability to recognize lesions. ...
Background: Glomerular lesion recognition is one of the most crucial steps in the diagnosis of kidney disease. Deep learning, which relies on large numbers of pathology images, assists pathologists to access glomerular lesions more efficiently, objectively and accurately. However, due to different pathological development of glomeruli, complicated lesion patterns, and limited resolution of pathology images, there is annotation noise in datasets, making the deep learning model under- or over-fit. Methods: In this paper, we propose a novel noisy label learning model for lesion recognition in glomerular datasets with annotation noise. The model integrates uncertainty-based noisy label discriminator, contrastive learning, and consistency regularization to achieve high signal-to-noise supervision, pathology feature extraction, and utilization of pathology images. Results: We constructed large-scale glomerular datasets from 870 kidney disease cases using different stainings including Periodic acid-Schiff (PAS), Masson Trichrome (MT) and Periodic Schiff-Methenamine (PASM). Intensive experiments demonstrated the superiority of the proposed model for glomerular lesion recognition compared to other methods, as 25% of the lesions had $f_{1}-score$ above 85%, 43.75% had $f_{1}-score$ above 80%, and 75% had $f_{1}-score$ at or above 70%. Additionally, further experiments demonstrate the effectiveness of each module. Conclusions: The noisy label learning model proposed is able to recognize the most glomerular lesions, with the annotation noise discrimination and large amounts of pathology images utilization, laying the foundation for the development of computer-aided evaluation system for the renal pathology.
... Recent progress in digital image analysis and machine learning applications for kidney tissue segmentation [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] has opened new perspectives for automated renal pathology assays. In particular, convolutional neural networks are applied for glomerular segmentation and classi cation tasks. ...
... Previous experiments achieved acceptable accuracies for glomerular injury classi cation with a gradient-weighted class activation mapping technique (Grad-CAM) to visualize the performance of the classi er. [30,32,37] This technique is a post hoc neural network attention method, that is not utilized for classi er training. Regarding to glomerular histological pattern segmentation, it can show an accurate feature recognition [30,32,37], or sometimes might present false negative intraglomerular segmentation results. ...
... [30,32,37] This technique is a post hoc neural network attention method, that is not utilized for classi er training. Regarding to glomerular histological pattern segmentation, it can show an accurate feature recognition [30,32,37], or sometimes might present false negative intraglomerular segmentation results. ...
INTRODUCTION
Pathology 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 open 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. Quality of classifier-produced heatmaps was evaluated by an intersection over union metrics (IoU) between predicted and ground truth localization heatmaps.
RESULTS
A proposed spatially guided modification of CNN classifier achieved the highest glomerular pattern classification accuracies with AUC values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, spatially guided classifier achieved significantly higher generalized mean IoU value, compared with single-multiclass and multiple-binary classifiers.
CONCLUSIONS
We propose a spatially guided CNN classifier which in our experiments reveals the potential to achieve high accuracy for intraglomerular pattern localization.
