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Related Concept Videos

Kidney Transplant I: Introduction01:28

Kidney Transplant I: Introduction

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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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Kidney Transplant II: Surgical Procedure01:26

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Preoperative ManagementThe primary goals of preoperative management in kidney transplantation are to optimize the patient’s metabolic state and prepare them for surgery through diet adjustments, necessary dialysis, and tailored medical treatment. This phase also involves comprehensive infection screening and patient education about the surgical procedure and postoperative care to improve outcomes and adherence.Medical ManagementA comprehensive evaluation is required for both the living...
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Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

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Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
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Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Alton B Farris1, Juan Vizcarra2, Mohamed Amgad3

  • 1Department of Pathology and Laboratory Medicine, Atlanta, GA, USA.

Histopathology
|November 19, 2020
PubMed
Summary

Whole slide imaging (WSI) and image analysis (IA), including artificial intelligence (AI), are increasingly used in pathology. This review focuses on AI applications in renal pathology, particularly for renal transplant pathology using the Banff classification.

Keywords:
artificial intelligencedigital pathologyimage analysismachine learningrenal transplant pathology

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Renal Pathology

Background:

  • Whole slide imaging (WSI) is a key digital pathology technique with growing applications.
  • Image analysis (IA), encompassing artificial intelligence (AI) and targeted algorithms, is increasingly integrated with WSI.
  • Renal pathology, especially renal transplant pathology, is well-suited for WSI and IA due to its reliance on semiquantitative classifications like the Banff criteria.

Purpose of the Study:

  • To review the application of AI and other IA algorithms to whole slide images (WSIs) in pathology.
  • To highlight specific examples within renal pathology, with a focus on renal transplant pathology.
  • To discuss the role of machine learning, particularly deep learning methods like ANNs/CNNs, in analyzing complex pathology data.

Main Methods:

  • Review of existing literature on AI and IA in digital pathology.
  • Discussion of targeted/hypothesis-driven algorithms for renal feature assessment (e.g., fibrosis, atrophy, inflammation).
  • Exploration of AI/machine learning advancements for glomeruli identification, histological segmentation, and other applications in renal pathology.

Main Results:

  • Significant increase in research citations for WSI and IA in pathology.
  • Growing use of AI, especially deep learning (ANNs/CNNs), for analyzing large WSI datasets.
  • AI algorithms (supervised and unsupervised) are employed for image and semantic classification tasks.

Conclusions:

  • AI and IA are transforming renal pathology, offering advanced analytical capabilities for WSI.
  • Renal transplant pathology benefits significantly from these technologies, aiding in the interpretation of Banff classification criteria.
  • The integration of AI into digital pathology workflows promises enhanced diagnostic accuracy and efficiency.