Literature DB >> 33581198

AI applications in renal pathology.

Yuankai Huo1, Ruining Deng1, Quan Liu1, Agnes B Fogo2, Haichun Yang3.   

Abstract

The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.
Copyright © 2021 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; deep learning; machine learning; renal pathology

Mesh:

Year:  2021        PMID: 33581198      PMCID: PMC8154730          DOI: 10.1016/j.kint.2021.01.015

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  66 in total

1.  Three-dimensional reconstruction of glomeruli by electron microscopy reveals a distinct restrictive urinary subpodocyte space.

Authors:  Christopher R Neal; Hayley Crook; Edward Bell; Steven J Harper; David O Bates
Journal:  J Am Soc Nephrol       Date:  2005-04-13       Impact factor: 10.121

2.  Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology.

Authors:  Michael Gadermayr; Laxmi Gupta; Vitus Appel; Peter Boor; Barbara M Klinkhammer; Dorit Merhof
Journal:  IEEE Trans Med Imaging       Date:  2019-02-14       Impact factor: 10.048

3.  Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

Authors:  Meyke Hermsen; Thomas de Bel; Marjolijn den Boer; Eric J Steenbergen; Jesper Kers; Sandrine Florquin; Joris J T H Roelofs; Mark D Stegall; Mariam P Alexander; Byron H Smith; Bart Smeets; Luuk B Hilbrands; Jeroen A W M van der Laak
Journal:  J Am Soc Nephrol       Date:  2019-09-05       Impact factor: 10.121

4.  An integrated iterative annotation technique for easing neural network training in medical image analysis.

Authors:  Brendon Lutnick; Brandon Ginley; Darshana Govind; Sean D McGarry; Peter S LaViolette; Rabi Yacoub; Sanjay Jain; John E Tomaszewski; Kuang-Yu Jen; Pinaki Sarder
Journal:  Nat Mach Intell       Date:  2019-02-11

Review 5.  Artificial Intelligence in Surgery: Promises and Perils.

Authors:  Daniel A Hashimoto; Guy Rosman; Daniela Rus; Ozanan R Meireles
Journal:  Ann Surg       Date:  2018-07       Impact factor: 12.969

Review 6.  Artificial intelligence and machine learning in nephropathology.

Authors:  Jan U Becker; David Mayerich; Meghana Padmanabhan; Jonathan Barratt; Angela Ernst; Peter Boor; Pietro A Cicalese; Chandra Mohan; Hien V Nguyen; Badrinath Roysam
Journal:  Kidney Int       Date:  2020-04-01       Impact factor: 10.612

7.  Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

Authors:  Brandon Ginley; Brendon Lutnick; Kuang-Yu Jen; Agnes B Fogo; Sanjay Jain; Avi Rosenberg; Vighnesh Walavalkar; Gregory Wilding; John E Tomaszewski; Rabi Yacoub; Giovanni Maria Rossi; Pinaki Sarder
Journal:  J Am Soc Nephrol       Date:  2019-09-05       Impact factor: 14.978

8.  Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

Authors:  Nassim Bouteldja; Barbara M Klinkhammer; Roman D Bülow; Patrick Droste; Simon W Otten; Saskia Freifrau von Stillfried; Julia Moellmann; Susan M Sheehan; Ron Korstanje; Sylvia Menzel; Peter Bankhead; Matthias Mietsch; Charis Drummer; Michael Lehrke; Rafael Kramann; Jürgen Floege; Peter Boor; Dorit Merhof
Journal:  J Am Soc Nephrol       Date:  2020-11-05       Impact factor: 10.121

9.  Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.

Authors:  Charat Thongprayoon; Wisit Kaewput; Karthik Kovvuru; Panupong Hansrivijit; Swetha R Kanduri; Tarun Bathini; Api Chewcharat; Napat Leeaphorn; Maria L Gonzalez-Suarez; Wisit Cheungpasitporn
Journal:  J Clin Med       Date:  2020-04-13       Impact factor: 4.241

Review 10.  Artificial Intelligence and Digital Pathology: Challenges and Opportunities.

Authors:  Hamid Reza Tizhoosh; Liron Pantanowitz
Journal:  J Pathol Inform       Date:  2018-11-14
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  5 in total

1.  Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining.

Authors:  Tianyuan Yao; Yuzhe Lu; Jun Long; Aadarsh Jha; Zheyu Zhu; Zuhayr Asad; Haichun Yang; Agnes B Fogo; Yuankai Huo
Journal:  J Med Imaging (Bellingham)       Date:  2022-06-20

2.  Circle Representation for Medical Object Detection.

Authors:  Ethan H Nguyen; Haichun Yang; Ruining Deng; Yuzhe Lu; Zheyu Zhu; Joseph T Roland; Le Lu; Bennett A Landman; Agnes B Fogo; Yuankai Huo
Journal:  IEEE Trans Med Imaging       Date:  2022-03-02       Impact factor: 10.048

3.  Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis.

Authors:  Luping Shen; Wenyi Sun; Qixiang Zhang; Mengru Wei; Huanke Xu; Xuan Luo; Guangji Wang; Fang Zhou
Journal:  Kidney Dis (Basel)       Date:  2022-06-07

Review 4.  Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.

Authors:  Yiqin Wang; Qiong Wen; Luhua Jin; Wei Chen
Journal:  J Clin Med       Date:  2022-08-22       Impact factor: 4.964

5.  Identification of glomerulosclerosis using IBM Watson and shallow neural networks.

Authors:  Francesco Pesce; Federica Albanese; Davide Mallardi; Michele Rossini; Giuseppe Pasculli; Paola Suavo-Bulzis; Antonio Granata; Antonio Brunetti; Giacomo Donato Cascarano; Vitoantonio Bevilacqua; Loreto Gesualdo
Journal:  J Nephrol       Date:  2022-01-18       Impact factor: 4.393

  5 in total

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