Artificial intelligence driven next-generation renal histomorphometry

Santo, Briana; Rosenberg, Avi Z.; Sarder, Pinaki

doi:10.1097/mnh.0000000000000598

Cited by 22 publications

(27 citation statements)

References 42 publications

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“…Despite having numerous imperfections, big data, as well as artificial intelligence have been applied in the field of medication from numerous parts [115,116]. There are numerous possible guidelines of using big data and artificial intelligence in nephrology that requires greater attention, as well as further consideration [74,78,[117][118][119][120][121][122][123][124][125].…”

Section: Potential Directions and Future Scopementioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

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Section: Potential Directions and Future Scopementioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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“…Further, these novel approaches could pave the way for the development of machine learning tools that provide disease prognosis or predicting treatment response 24 and even facilitate discovery of clinically actionable, nondestructive computational pathology-based imaging diagnostic biomarkers for kidney diseases. 25,27,48…”

Section: Interpreting Segmentation Resultsmentioning

confidence: 99%

“…Recent studies have suggested that computer vision tools can serve as triage and decision support tools for disease diagnosis with digital pathology. [24][25][26][27] Thus, automated image analysis tools need to be implemented and integrated into the pathology workflow for efficient and reliable segmentation of histologic primitives across multiple types of stains. DL segmentation tools could greatly facilitate derivation of not only the visual but also subvisual histomorphometric features (e.g., shape, textural, and graph features) for correlation with diagnosis and outcome.…”

Section: Discussionmentioning

confidence: 99%

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Jayapandian¹,

Chen²,

Janowczyk³

et al. 2021

Kidney International

131

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“…It can be postulated that the kidney is uniquely positioned as a fertile area for the application of IA and AI, because quantitative data are often included in kidney biopsy reports (the total number of glomeruli, the number/proportion of sclerotic glomeruli, the extent of tubulointerstitial and vascular scarring, etc.). 9 This is particularly true in the area of renal transplant pathology, in which the Banff classification of allograft pathology is often applied. The Banff classification includes semiquantitative scores for various histological features [tubulitis (t), endarteritis (v), interstitial inflammation (i, ti, i-IFTA), etc.]…”

Section: Kidney Transplant Pathology Examplesmentioning

confidence: 99%

“…This and other key definitions are shown in Table 2. The definitions in Table 2 and throughout this article are based on our own experience, expert group publications, 1,8 and useful reviews [9][10][11][12][13][14][15][16] ; however, we recognise that variable definitions are provided and used in other publications, and are probably in flux.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Farris¹,

Vizcarra

Amgad

et al. 2021

Histopathology

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Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examplesWhole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

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Artificial intelligence driven next-generation renal histomorphometry

Cited by 22 publications

References 42 publications

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

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