Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence

J Am Soc Nephrol. 2025 Nov 12. doi: 10.1681/ASN.0000000923. Online ahead of print.

Christos Matsoukas^{1

2

3}, Tajana Tesan Tomic⁴, Pernilla Tonelius⁵, Esther Nuñez-Duran⁵, Lihuan Liang⁶, Annika Wernerson⁷, Johan Mölne^{8

9}, Robert I Menzies⁵, Anna B Granqvist^{5

10}, Pernille B L Hansen⁵, Kevin Smith^{2

3}, Magnus Söderberg¹

¹ Pathology, Clinical Pharmacology and Safety Sciences, R&D AstraZeneca, Gothenburg, Sweden.
² KTH Royal Institute of Technology, Stockholm, Sweden.
³ Science for Life Laboratory, Stockholm, Sweden.
⁴ Bioscience Technology, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
⁵ Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
⁶ Bioscience Renal, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom.
⁷ Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Renal Medicine, Karolinska Institutet, Stockholm, Sweden.
⁸ Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden.
⁹ Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
¹⁰ Ribocure Pharmaceuticals AB/Ribo Life Science Co Ltd., Gothenburg, Sweden.

No abstract available

Keywords: CKD; diabetic kidney disease; glomerular diseases; glomerulus.

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