Artificial Intelligence in Pathology

Abstract

Background: Increasing digitalization enables the use of artificial intelligence (AI) and machine learning in pathology. However, these technologies have only just begun to be implemented, and no randomized prospective trials have yet shown a benefit of AI-based diagnosis. In this review, we present current concepts, illustrate them with examples from representative publications, and discuss the possibilities and limitations of their use.

Methods: This article is based on the results of a search in PubMed for articles published between January 1950 and January 2020 containing the searching terms "artificial intelligence," "deep learning," and "digital pathology," as well as the authors' own research findings.

Results: Current research on AI in pathology focuses on supporting routine diagnosis and on prognostication, particularly for patients with cancer. Initial data indicate that pathologists can arrive at a diagnosis faster and more accurately with the aid of a computer. In a pilot study on the diagnosis of breast cancer, involving 70 patients, sensitivity for the detection of micrometastases rose from 83.3% (by a pathologist alone) to 91.2% (by a pathologist combined with a computer algorithm). The evidence likewise suggests that AI applied to histomorphological properties of cells during microscopy may enable the inference of certain genetic properties, such as mutations in key genes and deoxyribonucleic acid (DNA) methylation profiles.

Conclusion: Initial proof-of-concept studies for AI in pathology are now available. Randomized, prospective studies are now needed so that these early findings can be confirmed or falsified.

Figure 1

Key developments in artificial intelligence and pathology (– 31). DICOM, digital imaging and communications in medicine; FDA, Food and Drug Administration; FISH, fluorescence in situ hybridization; PACS, picture archiving and communication system

Figure 2

Example of a deep learning model, designed to differentiate colorectal cancer from normal colorectal mucosa. On the left, the conventional histological input image; on the right, highlighting of the tissue according to the result of classification by the artificial intelligence model .First, individual image sections (tiles) are classified by the artificial neural network and then each individual tile is color-coded based on prediction probability: higher probability of the class “tumor“: red; higher probability of the class “normal mucosa”: green (unpublished data, Försch et al.).