Artificial Intelligence and Algorithmic Computational Pathology: Introduction with Renal Allograft Examples.

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Abstract

Whole slide imaging (WSI), an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilization; and with more widespread WSI utilization, there will also be increased interest in and implementation of image analysis techniques. Image analysis includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, citations related to these topics have increased in recent years. Renal pathology is one anatomic pathology subspecialty that has utilized WSIs and image analysis algorithms; and it can be argued that renal transplant pathology could be particularly suited for WSI and image analysis, since renal transplant pathology is frequently classified using 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 and tubular atrophy and inflammation); and in recent years, research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histologic segmentation, and other applications. Deep learning is the form of machine learning 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 utilized. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other image analysis algorithms applied to WSIs are discussed; and examples from renal pathology are covered, with an emphasis on renal transplant pathology.
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