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

Abstract

Whole 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.

Keywords: artificial intelligence; digital pathology; image analysis; machine learning; renal transplant pathology.

Figure 1:

Publications per year based on the PubMed search terms specified below are shown. The “&” in the figure key designates that the “AND” Boolean operator used to combine the specified terms in the PubMed Advanced Search Builder.

Figure 2:

Publications per year pertaining to the kidney and kidney transplantation based on the PubMed search terms specified below are shown. The “&” in the figure key designates that the “AND” Boolean operator used to combine the specified terms in the PubMed Advanced Search Builder.

Figure 3:

A summary of the application digital pathology is shown in different forms of whole slide image (WSI) analysis (based in part on a prior publication from our group and others ^, . Slides are scanned into WSIs (1). In a targeted or hypothesis-driven algorithmic approach (2a), specific algorithms are run (3a). When artificial intelligence (AI) algorithms are used (3b), the previously trained AI algorithms (e.g., neural networks with differentially weighted nodes and connections) are executed on the image (3b). For both targeted and AI, pathologists (or other trained individuals) review the results in some manner and eventually report the results for patient care or research.

Figure 4:

Examples of image analysis of the kidney are shown. In the upper panels (a), examples of a positive pixel count (PPC) algorithm to detected fibrous areas on trichrome and collagen III immunohistochemistry (IHC) are depicted. In the markup images showing the algorithm analysis depicted on the right, tissue considered “positive” is marked up as yellow, orange, or red, in that order with increasing positivity of match to the algorithm parameters. In the lower panel (b), an example of glomerular detection conducted on Human Leukocyte Antigen (HLA)-DR IHC using the Leica/Aperio GENIE algorithm is shown. In the markup images showing the algorithm analysis depicted on the right, areas classified as glomeruli by the algorithm are depicted in yellow; and selected glomeruli in the field are pointed out with red arrows. It can be appreciated that some smaller yellow areas amidst the remaining renal parenchyma (in green) do not represent glomeruli, showing that additional algorithm training is needed.