Advancements in digital pathology and artificial intelligence (AI) have enormous transformative potential for nonclinical toxicologic pathology and are already changing the ways in which pathologists work. However, due to the rapid evolution of digital pathology and AI, the toxicologic pathology community would benefit from an update on these advancements, which can be used to aid drug development. Here we identify key articles published on the use of digital pathology and AI in the field and provide current regulatory statuses and guidelines. For digital pathology, we outline the requirements for equipment, validation processes, workflows, and archiving. Challenges to achieve system interoperability and to establish harmonization through Digital Imaging and Communications in Medicine compatibility are also discussed. For AI, we highlight considerations for model development, including the determination of ground truth, problems that may arise due to bias, and how the accuracy and precision of AI algorithms can be assessed. Finally, we discuss the challenges and potential for AI-assisted toxicologic pathology, picturing a future where technology and scientific expertise work hand-in-hand to improve the quality and efficiency of nonclinical drug safety evaluation. This publication is a deliverable of the European Innovative Medicines Initiative 2 Joint Undertaking, "Bigpicture."

Digital toxicologic histopathology has been broadly adopted in preclinical compound development for informal consultation and peer review. There is now increased interest in implementing the technology for good laboratory practice-regulated study evaluations. However, the implementation is not straightforward because systems and work processes require qualification and validation, with consideration also given to security. As a result of the high-throughput, high-volume nature of safety evaluations, computer performance, ergonomics, efficiency, and integration with laboratory information management systems are further key considerations. The European Society of Toxicologic Pathology organized an international expert workshop with participation by toxicologic pathologists, quality assurance/regulatory experts, and information technology experts to discuss qualification and validation of digital histopathology systems in a good laboratory practice environment, and to share the resulting conclusions broadly in the toxicologic pathology community.

The 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxicologic pathology would improve the daily workflow and future impact of toxicologic pathologists globally. Now 2 years later, the authors of this review consider whether, in their opinion, there is any evidence that supports that thesis. Specifically, we consider the opportunities and challenges for applying ML (the study of computer algorithms that are able to learn from example data and extrapolate the learned information to unseen data) algorithms in toxicologic pathology and how regulatory bodies are navigating this rapidly evolving field. Although we see similarities with the "Last Mile" metaphor, the weight of evidence suggests that toxicologic pathologists should approach ML with an equal dose of skepticism and enthusiasm. There are increasing opportunities for impact in our field that leave the authors cautiously excited and optimistic. Toxicologic pathologists have the opportunity to critically evaluate ML applications with a "call-to-arms" mentality. Why should we be late adopters? There is ample evidence to encourage engagement, growth, and leadership in this field.

When you want to build an application, one of the first decisions you must make is where you are going to store data. Whether you store the data in SharePoint, Excel, or SQL Server, you then must design your lists, worksheets, or tables before getting too far into your application construction likely. Common Data Services (CDS) provides an easier place to store your data and contains some of the common plumbing you will need to build an intelligent application.

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]

Modern business processes have a key role in operating, controlling, and managing large organizations. The management and monitoring of business processes can be problematic since their structural complexity and large volume of involved data makes efficient monitoring and decision making hard. This paper presents a platform for an intelligent monitoring business processes platform. An approach to using CBR for the reuse of knowledge to the monitoring of business workflows is presented. The CBRWIMS platform and its architecture is presented. An overview of the evaluation of the approach and the platform is presented as applied to a real business workflow case study. This shows that CBR-WIMS can assist business workflow managers in the monitoring and intelligent decision support of real business workflows.

A business process is the combination of a set of activities with logical order and dependence, whose objective is to produce a desired goal. Business process modeling (BPM) using knowledge of the available process modeling techniques enables a common understanding and analysis of a business process. Industry and academics use informal and formal techniques respectively to represent business processes (BP), having the main objective to support an organization. Despite both are aiming at BPM, the techniques used are quite different in their semantics. While carrying out literature research, it has been found that there is no general representation of business process modeling is available that is expressive than the commercial modeling tools and techniques. Therefore, it is primarily conceived to provide an ontology mapping of modeling terms of Business Process Modeling Notation (BPMN), Unified Modeling Language (UML) Activity Diagrams (AD) and Event Driven Process Chains (EPC) to temporal logic. Being a formal system, first order logic assists in thorough understanding of process modeling and its application. However, our contribution is to devise a versatile conceptual categorization of modeling terms/constructs and also formalizing them, based on well accepted business notions, such as action, event, process, connector and flow. It is demonstrated that the new categorization of modeling terms mapped to formal temporal logic, provides the expressive power to subsume business process modeling techniques i.e. BPMN, UML AD and EPC.

Briefly presenting a generalization of Allen's interval-based approach to temporal reasoning, this paper will see point & typed-based structure of time intervals as an intended model of point & interval-based time theory to illustrate a Consistency Checker for Uncertain or Incomplete Temporal System which can be used to check whether there are circuit(s) among the temporal intervals and whether the temporal intervals are consistent or not, and this paper also succinctly discourses the future work about how to find the best solution of this checker. 1. Background To illustrate the temporal references in daily life, it is a truth universally acknowledged that temporal references play an important role in common universal references, which can be expressed with points or intervals that can be defined in temporal language such as that 'A before B' or 'A during B' and so on. Maintaining knowledge about temporal intervals, James Allen introduces a temporal logic based on intervals and their qualitative relationships in time (1). Paper (2), detailed describing the temporal intervals, specifically indicates the time theory of thirteen relationships. Paper(3) see point&typed-based structure of time intervals as an intended model of point&interval-based time theory which will be used as basic theory in this paper.This paper will introduce a Consistency Checker for Uncertain or Incomplete Temporal System whose relative statistics can be found among (1, 2). Analogous to the 13 relations introduced by Allen, accordingly, 30 exclusive temporal relations over time elements including both time points and time intervals can be concluded, which can be derived from the single Meets order relation and classified into the following 4 groups:

Although histopathology is considered the gold standard for assessing testicular toxicity in the nonclinical setting, identification of noninvasive biomarkers for testicular injury are desirable to improve safety monitoring capabilities for clinical trials. Inhibin B has been investigated as a noninvasive biomarker for testicular toxicity. This study investigates the correlation of Inhibin B in Wistar Han rats with the onset and reversibility of testicular histopathology from classical testicular toxicants carbendazim, cetrorelix acetate (CTX), and 1,2-dibromo-3-chloropropane (DBCP). The dose regimen included Interim (day 8), Drug (day 29), and nondosing Recovery (day 58) Phases. Inhibin B was not effective at predicting the onset of carbendazim- or CTX-mediated testicular pathology in rats. Inhibin B was reduced by DBCP administration at the end of the Drug Phase only, acting as a leading indicator of the onset of testicular toxicity before the onset of germ cell depletion. However, since Inhibin B was only decreased at the end of the Dosing Phase and not at the Recovery Phase, when the onset of testicular pathology occurred, it is unclear if monitoring Inhibin B would provide sufficient advanced warning for the onset of testicular pathology. Furthermore, follicle stimulating hormone was decreased following CTX and DBCP administration in the Interim Phase and CTX in the Drug Phase. Inhibin B has limited predictive capacity as a leading testicular biomarker in rats.

We introduce in this paper a formalism for representing flexible temporal causal relationships between events and their effects. A formal characterization of the so-called (most) General Temporal Constraint (GTC) is formulated, which guarantees the common-sense assertion that “the beginning of the effect cannot precede the beginning of its causal event”. It is shown that there are actually in total 8 possible temporal causal relationships which satisfy the GTC. These include cases where, (1) the effect becomes true immediately after the end of the event and remains true for some time after the event; (2) the effect holds only over the same time over which the event is in progress; (3) the beginning of the effect coincides with the beginning of the event, and the effect ends before the event completes; (4) the beginning of the effect coincides with the beginning of the event, and the effect remains true for some time after the event; (5) the effect only holds over some time during the progress of the event; (6) the effect becomes true during the progress of the event and remains true until the event completes; (7) the effect becomes true during the progress of the event and remains true for some time after the event; and (8) where there is a time delay between the event and its effect. We shall demonstrate that the introduced formulation is versatile enough to subsume those existing representative formalisms in the literature.

Digital Pathology Systems (DPS) are dynamic, image-based computer systems that enable the acquisition, management, and interpretation of pathology information generated from digitized glass slides. This article provides a roadmap for (1) qualification of a whole slide scanner (WSS) during a validation project, (2) validation of software required to generate the whole slide image (WSI), and (3) an introduction to visual digital image evaluation and image analysis. It describes a validation approach that can be utilized when validating a DPS. It is not the intent of this article to provide guidance on when validation of DPS is required. Rather, the article focuses on technical aspects of validation of the WSS system (WSS, IT infrastructure, and associated software) portion of a DPS and covers the processes of setting up the WSS for scanning a glass slide through saving a WSI on a server. Validation of a computerized system, such as a DPS, for use in a regulated nonclinical environment is governed by Code of Federal Regulations (CFR) Title 21 part 11: Electronic Records; Electronic Signature and predicate rules associated with Good Laboratory Practices documents including 21 CFR part 58. Similar regulation and predicate rules apply in the European Union and Japan.

Business processes have a key role in operating, controlling, and managing large modern organizations. Managing business processes presents a challenge related to the temporal complexity, uncertainty and large volume of data generated. This paper presents new developments and evaluation of an enhanced approach for the intelligent monitoring of business processes using Case-Based Reasoning in the CBR-WIMS platform. A short overview of the CBR-WIMS approach, based on the representation of business process cases as graphs, comprising process events and their temporal relationships is presented. The enhanced similarity measure based on the Maximum Common Sub-graph is presented and an evaluation of its effectiveness and efficiency is shown and discussed. The evaluation uses historical data from a real business process. The paper also discusses the use of a clustering technique in CBR-WIMS. This allows the semi-automatic tagging of cases and so provides enhanced explanation and context to users, increasing the confidence and usability of retrieved solutions and advice. A set of experiments are presented and discussed showing the added value that this enhancement brings to the intelligent monitoring and management of real business processes in an organisation.

Business process engineering and modelling is an important practical activity, presently based on a variety of methodologies. These are practically derived methods, grounded in concepts such as "action", "event", "process" and "model", which can vary according to methodology. In this paper, we formalise these notions of business process, actions and business models, by relating them to well-established theories of time and temporal activity. The ontology of the terms employed in business process engineering is discussed, and formal definitions of the main terms are given. The term "temporal model" of a business process is introduced, and a formal definition of a sub-process is given with relation to the temporal model. A sub-process can be viewed as a module of a business process which can be replaced or altered without affecting the rest of the process. It is shown that existing business process models using business process modelling can be given a logical foundation based on formal temporal theory.

This paper presents an approach for the intelligent diagnosis and monitoring of business workflows based on operation data in the form of temporal log data. The representation of workflow related case knowledge in this research using graphs is explained. The workflow process is orchestrated by a software system using BPEL technologies within a service-oriented architecture. Workflow cases are represented in terms of events and their corresponding temporal relationships. The matching and CBR retrieval mechanisms used in this research are explained and the architecture of an integrated intelligent monitoring system is shown. The paper contains an evaluation of the approach based on experiments on real data from a university quality assurance exam moderation system. The experiments and the evaluation of the approach is presented and is shown that a graph matching based similarity measure is capable to diagnose problems within business workflows. Finally, further work on the system and the extension to a full intelligent monitoring and process optimisation system is presented.

In this paper, we present current research using the ShapeCBR system that automates the process of creation and selection of cases to populate a CBR system for retrieval of 3D shapes to assist with the design of metal castings. The special feature of this system is that similarity is derived primarily from graph matching algorithms. The particular problem of such a system is that it does not operate on simple search indices that may be derived from single cases and then used for visualisation and principal component analyses. Rather, the system is built on a similarity metric defined directly over pairs of cases and is primarily structural. An overview of previous research in this area is presented. This demonstrates the feasibility of a CBR approach to the design of metal castings. This paper describes further research into the use of the traditional componentisation as used in method engineering to provide a shape representation suitable for efficient retrieval of design knowledge. The paper presents current work aiming mainly at enhancing the efficiency and accuracy of the similarity metrics used in the ShapeCBR system. The architecture of the ShapeCBR system is presented. Finally, performance measures for the CBR system and the metrics used are given, and the results of trials of the system are presented and compared to results obtained from previous research.

Complete and Absolute temporal knowledge is usually not always available for many knowledge based systems, notably in the domain of Artificial Intelligence. Based on a time theory that takes both points and intervals as primitive, this paper introduces a graphical representation for uncertain and incomplete temporal knowledge, which allows logical expressions of both absolute and relative temporal relations, including both logical conjunctions and disjunctions. The consistency of any given collection of uncertain and incomplete temporal knowledge depends on if there is at one temporal scenario that is temporal consistent, where a consistency checker for temporal scenarios is provided.

This paper aims to answer the following research question: “Can the standard CBR models be generalised to a unified (problem : solution) space to allow flexible query modes?“ In the standard Case-Based Reasoning (CBR) model, a case is represented as a pair. The problem space and solution space are treated as separate, and nearest neighbours are retrieved using a metric defined on the problem space. The standard method applies to domains where the similarity assumption is valid: that cases which are near in the problem space are also near in the solution space. This presumes that a metric is also defined in the solution space. In this paper a generalisation of the standard CBR retrieval method, which integrates solution space and retrieval space into a single query space is proposed. The retrieval method is proposed by means of the concept of nearest neighbours to a constraint region. It is shown that the standard CBR retrieval is a special case of this more general model. The advantages of the general model are explained in connection with its more general query modes. Whereas the standard model is only queryable by specification of problem “inputs”, the more general model is capable of retrieving general queries on the unified problem-solution space. The advantages of this flexible query form are explained in the paper, by means of a variety of illustrative examples.

Modern business processes have a key role in operating, controlling, and managing large organizations. The management and monitoring of business processes can be problematic since their structural complexity and large volume of involved data makes efficient monitoring and decision making hard. This paper presents a platform for an intelligent monitoring business processes platform. An approach to using CBR for the reuse of knowledge to the monitoring of business workflows is presented. The CBRWIMS platform and its architecture is presented. An overview of the evaluation of the approach and the platform is presented as applied to a real business workflow case study. This shows that CBR-WIMS can assist business workflow managers in the monitoring and intelligent decision support of real business workflows.

The representation and manipulation of natural human understanding of temporal phenomena is a fundamental field of study in Computer Science, which aims both to emulate human thinking, and to use the methods of human intelligence to underpin engineering solutions. In particular, in the domain of Artificial Intelligence, temporal knowledge may be uncertain and incomplete due to the unavailability of complete and absolute temporal information. This paper introduces an inferential framework for deriving logical explanations from partial temporal information. Based on a graphical representation which allows expression of both absolute and relative temporal knowledge in incomplete forms, the system can deliver a verdict to the question if a given set of statements is temporally consistent or not, and provide understandable logical explanation of analysis by simplified contradiction and rule based reasoning.