Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

Wang, Yiqin; Wen, Qiong; Jin, Luhua; Chen, Wei

doi:10.3390/jcm11164918

Cited by 7 publications

(1 citation statement)

References 123 publications

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“…A recurrent neural network is characterized by a circular connection, allowing information to flow back and be stored in its hidden layers. Therefore, the previous outputs can influence the current inputs and outputs 6 [17].…”

Section: A Brief Overview Of Aimentioning

confidence: 99%

Artificial intelligence in renal pathology: Current status and future

Feng

Liu

2022

Bosn J of Basic Med Sci

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Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney diseases. With the increase in the incidence rate of kidney diseases, the lack of renal pathologists, and an imbalance in their distribution, there is an urgent need for a new renal pathological diagnosis model. Advances in artificial intelligence (AI) along with the growing digitization of pathology slides for diagnosis are promising approach to meet the demand for more accurate detection, classification, and prediction of the outcome of renal pathology. AI has contributed substantially to a variety of clinical applications, including renal pathology. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being used in multiple areas of pathology. In this narrative review, we first provide a general description of AI methods, and then discuss the current and prospective applications of AI in the field of renal pathology. Both diagnostic and predictive prognostic applications are covered, emphasizing AI in renal pathology images, predictive models, and 3D in renal pathology. Finally, we outline the challenges associated with the implementation of AI platforms in renal pathology and provide our perspective on how these platforms might change in this field.

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Section: A Brief Overview Of Aimentioning

confidence: 99%

Artificial intelligence in renal pathology: Current status and future

Feng

Liu

2022

Bosn J of Basic Med Sci

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A new, deep learning–based method for the analysis of autopsy kidney samples used to study sex differences in glomerular density and size in a forensic population

Vilmont,

Ngatchou,

Lioux

et al. 2024

Int J Legal Med

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Artificial intelligence (AI) is increasingly used in forensic anthropology and genetics to identify the victim and the cause of death. The large autopsy samples from persons with traumatic causes of death but without comorbidities also offer possibilities to analyze normal histology with AI. We propose a new deep learning–based method to rapidly count glomerular number and measure glomerular density (GD) and volume in post-mortem kidney samples obtained in a forensic population. We assessed whether this new method detects glomerular differences between men and women without known kidney disease. Autopsies performed between 2009 and 2015 were analyzed if subjects were aged ≥ 18 years and had no known kidney disease, diabetes mellitus, or hypertension. A large biopsy was taken from each kidney, stained with hematoxylin and eosin, and scanned. An in-house developed deep learning–based algorithm counted the glomerular density (GD), number, and size. Out of 1165 forensic autopsies, 86 met all inclusion criteria (54 men). Mean (± SD) age was 43.5 ± 14.6; 786 ± 277 glomeruli were analyzed per individual. There was no significant difference in GD between men and women (2.18 ± 0.49 vs. 2.30 ± 0.57 glomeruli/mm2, p = 0.71); glomerular diameter, area, and volume also did not differ. GD correlated inversely with age, kidney weight, and glomerular area. Glomerular area and volume increased significantly with age. In this study, there were no sex differences in glomerular density or size. Considering the size of the kidney samples, the use of the presented deep learning method can help to analyze large renal autopsy biopsies and opens perspectives for the histological study of other organs.

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