Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis
Introduction: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)–associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that d...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Kidney International Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468024924020163 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849336192722534400 |
|---|---|
| author | Maria A.C. Wester Trejo Maryam Sadeghi Shivam Singh Naghmeh Mahmoodian Samir Sharifli Zdenka Hruskova Vladimír Tesař Xavier Puéchal Jan Anthonie Bruijn Georg Göbel Ingeborg M. Bajema Andreas Kronbichler |
| author_facet | Maria A.C. Wester Trejo Maryam Sadeghi Shivam Singh Naghmeh Mahmoodian Samir Sharifli Zdenka Hruskova Vladimír Tesař Xavier Puéchal Jan Anthonie Bruijn Georg Göbel Ingeborg M. Bajema Andreas Kronbichler |
| author_sort | Maria A.C. Wester Trejo |
| collection | DOAJ |
| description | Introduction: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)–associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their “black box” structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. Methods: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. Results: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. Conclusion: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making. |
| format | Article |
| id | doaj-art-54c6dc91f14844fabc5ef194d5c46435 |
| institution | Kabale University |
| issn | 2468-0249 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kidney International Reports |
| spelling | doaj-art-54c6dc91f14844fabc5ef194d5c464352025-08-20T03:45:03ZengElsevierKidney International Reports2468-02492025-02-0110245746510.1016/j.ekir.2024.11.005Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated GlomerulonephritisMaria A.C. Wester Trejo0Maryam Sadeghi1Shivam Singh2Naghmeh Mahmoodian3Samir Sharifli4Zdenka Hruskova5Vladimír Tesař6Xavier Puéchal7Jan Anthonie Bruijn8Georg Göbel9Ingeborg M. Bajema10Andreas Kronbichler11Department of Pathology, Leiden University Medical Center, Leiden, The NetherlandsDepartment of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck, AustriaDepartment of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck, AustriaFaculty of Computer Science, Otto von Guericke University, Magdeburg, GermanyDepartment of Internal Medicine IV, Nephrology and Hypertension, Medical University of Innsbruck, Innsbruck, AustriaDepartment of Nephrology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, CzechiaDepartment of Nephrology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, CzechiaDepartment of Internal Medicine, Hôpital Cochin, AP-HP, Paris, FranceDepartment of Pathology, Leiden University Medical Center, Leiden, The NetherlandsDepartment of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck, AustriaDepartment of Pathology and Medical Biology, University Medical Center Groningen, Groningen, The Netherlands; Correspondence: Ingeborg M. Bajema, Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, The Netherlands.Department of Internal Medicine IV, Nephrology and Hypertension, Medical University of Innsbruck, Innsbruck, Austria; Andreas Kronbichler, Department of Internal Medicine IV, Nephrology and Hypertension, Medical University of Innsbruck, Innsbruck, Austria.Introduction: The histopathological classification for antineutrophil cytoplasmic autoantibody (ANCA)–associated glomerulonephritis (ANCA-GN) is a well-established tool to reflect the variety of patterns and severity of lesions that can occur in kidney biopsies. It was demonstrated previously that deep learning (DL) approaches can aid in identifying histopathological classes of kidney diseases; for example, of diabetic kidney disease. These models can potentially be used as decision support tools for kidney pathologists. Although they reach high prediction accuracies, their “black box” structure makes them nontransparent. Explainable (X) artificial intelligence (AI) techniques can be used to make the AI model decisions accessible for human experts. We have developed a DL-based model, which detects and classifies the glomerular lesions according to the Berden classification. Methods: Kidney biopsy slides of 80 patients with ANCA-GN from 3 European centers, who underwent a diagnostic kidney biopsy between 1991 and 2011, were included. We also investigated the explainability of our model using Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps. These maps were analyzed by pathologists to compare the decision-making criteria of humans and the DL model and assess the impact of different training settings. Results: The DL model shows a prediction accuracy of 93% for classifying lesions. The heatmaps from our trained DL models showed that the most predictive areas in the image correlated well with the areas deemed to be important by the pathologist. Conclusion: We present the first DL-based computational pipeline for classifying ANCA-GN kidney biopsies as per the Berden classification. XAI techniques helped us to make the decision-making criteria of the DL accessible for renal pathologists, potentially improving clinical decision-making.http://www.sciencedirect.com/science/article/pii/S2468024924020163ANCAartificial intelligencehistopathologymachine learningvasculitis |
| spellingShingle | Maria A.C. Wester Trejo Maryam Sadeghi Shivam Singh Naghmeh Mahmoodian Samir Sharifli Zdenka Hruskova Vladimír Tesař Xavier Puéchal Jan Anthonie Bruijn Georg Göbel Ingeborg M. Bajema Andreas Kronbichler Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis Kidney International Reports ANCA artificial intelligence histopathology machine learning vasculitis |
| title | Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis |
| title_full | Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis |
| title_fullStr | Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis |
| title_full_unstemmed | Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis |
| title_short | Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis |
| title_sort | explainability of a deep learning based classification model for antineutrophil cytoplasmic autoantibody associated glomerulonephritis |
| topic | ANCA artificial intelligence histopathology machine learning vasculitis |
| url | http://www.sciencedirect.com/science/article/pii/S2468024924020163 |
| work_keys_str_mv | AT mariaacwestertrejo explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT maryamsadeghi explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT shivamsingh explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT naghmehmahmoodian explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT samirsharifli explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT zdenkahruskova explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT vladimirtesar explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT xavierpuechal explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT jananthoniebruijn explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT georggobel explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT ingeborgmbajema explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis AT andreaskronbichler explainabilityofadeeplearningbasedclassificationmodelforantineutrophilcytoplasmicautoantibodyassociatedglomerulonephritis |