NDLI: Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy

Content Provider	Springer Nature : BioMed Central
Author	Lei, Qunjuan Hou, Xiaoshuai Liu, Xumeng Liang, Dongmei Fan, Yun Xu, Feng Liang, Shaoshan Liang, Dandan Yang, Jing Xie, Guotong Liu, Zhihong Zeng, Caihong
Abstract	Background Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed. Methods A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed. Results Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function. Conclusion Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-024-05221-8.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-024-05221-8
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2024-04-29
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Artificial intelligence Diabetic nephropathy Glomerulus Pathology Identification Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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