Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology

Abstract

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

“…Moreover, the combined power of an expert pathologist and AI system has demonstrated reduced diagnostic errors and superior results compared to either pathologist or machine alone. 40 It has been predicted that researchers will endeavor to use AI to more accurately quantitatively grade immunohistochemistry stains and to reduce the time pathologists spend screening (eg, in cytopathology) by identifying ROIs. 14 This would free up pathologists' time and allow them to focus on higher-level diagnostic tasks such as integrating molecular data which can aid clinicians in treatment decision-making.…”

The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues

“…The development of automated software to extract relevant data from whole slide images has resulted in the rapid expansion of computerized analysis, often referred to as quantitative digital pathology. 18,19 Whole slide images offer an advantage as they are amenable to precise and reproducible data extraction that can be efficiently analyzed with automated open-source and commercially available software developed for image analysis and stereology. For example, the accumulation of versican was measured in tissue samples obtained from the lungs of mice exposed to either vehicle control or H1N1 influenza virus strain, A/PR/8/34 (PR/8), using WSDI and image analysis ( Fig.…”

“…The integration of AI with image analysis for detection, segmentation, feature extraction, and tissue classification of digital images has resulted in an even more rapid evolution and expansion of digital pathology. 18,19,[56][57][58] Although the promise of AI has garnered substantial attention and excitement, it is important to point out that the use of AI to obtain objective data from cells and tissues dates back over 40 years. 59,60 AI, computer-based systems that automate decision-making processes, was initially defined as the overarching concept of the thinking machine.…”

“…59,60 AI, computer-based systems that automate decision-making processes, was initially defined as the overarching concept of the thinking machine. 19,58 When first described, machine learning was defined as giving computers the ability to learn without having to be specifically programmed to provide an output. 19,58 Frequently, this is accomplished through supervised learning, wherein the AI system uses algorithms to analyze training sets of data (e.g.…”

“…19,58 When first described, machine learning was defined as giving computers the ability to learn without having to be specifically programmed to provide an output. 19,58 Frequently, this is accomplished through supervised learning, wherein the AI system uses algorithms to analyze training sets of data (e.g. digital images), learns from those data and adjusts its algorithms, and then based on that learning interprets or makes predictions about new data.…”