Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology

Abstract

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

“…( digital pathology [Title]) AND ( artificial intelligence [Title]) currently reports 17 works [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

“…The first aspect is that when scholars talk about artificial intelligence in digital pathology, they refer more to the aspects of imaging and essentially histological imaging. The second aspect is that scholars begin to identify interesting perspectives—for example, in oncology [ 15 , 25 ] or in toxicology [ 14 , 24 ]. The last aspect, in line with our objective, is that scholars are interrogating the work to be completed in a prospective way [ 25 , 26 , 27 , 28 ].…”

“…Important perspectives have been identified for example: Through a review [ 15 ] on immuno-oncology. In a Special Issue [ 14 ] and in an opinion article [ 24 ] of a working group in pathological diagnostics in toxicology. Through a report [ 22 ] for the prediction of positive lymph nodes from primary tumors in bladder cancer.…”

“…In a Special Issue [ 14 ] and in an opinion article [ 24 ] of a working group in pathological diagnostics in toxicology.…”

“…There is currently optimism about the opportunities of ANNs, as has been highlighted above in the studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. There are several difficulties with deploying AI tools in practice depending on the expectance and the objectives of the pathologist.…”