Are synthetic cytology images ready for prime time? A comparative assessment of real and synthetic urine cytology images.

Introduction: The use of synthetic data in pathology has, to date, predominantly been augmenting existing pathology data to improve supervised machine learning algorithms. We present an alternative use case-using synthetic images to augment cytology training when the availability of real-world examples is limited. Moreover, we compare the assessment of real and synthetic urine cytology images by pathology personnel to explore the usefulness of this technology in a real-world setting.

Is it real or not? Toward artificial intelligence-based realistic synthetic cytology image generation to augment teaching and quality assurance in pathology.

Introduction: Urine cytology offers a rapid and relatively inexpensive method to diagnose urothelial neoplasia. In our setting of a public sector laboratory in South Africa, urothelial neoplasia is rare, compromising pathology training in this specific aspect of cytology. Artificial intelligence-based synthetic image generation-specifically the use of generative adversarial networks (GANs)-offers a solution to this problem.

The Dynamics of Pathology Dataset Creation Using Urine Cytology as an Example.

Introduction: Dataset creation is one of the first tasks required for training AI algorithms but is underestimated in pathology. High-quality data are essential for training algorithms and data should be labelled accurately and include sufficient morphological diversity. The dynamics and challenges of labelling a urine cytology dataset using The Paris System (TPS) criteria are presented.

The Utility of Unsupervised Machine Learning in Anatomic Pathology.

Objectives: Developing accurate supervised machine learning algorithms is hampered by the lack of representative annotated datasets. Most data in anatomic pathology are unlabeled and creating large, annotated datasets is a time consuming and laborious process. Unsupervised learning, which does not require annotated data, possesses the potential to assist with this challenge.

Challenges Developing Deep Learning Algorithms in Cytology.

Background: The incorporation of digital pathology into routine pathology practice is becoming more widespread. Definite advantages exist with respect to the implementation of artificial intelligence (AI) and deep learning in pathology, including cytopathology. However, there are also unique challenges in this regard.

Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries.

The application of artificial intelligence technologies to anatomic pathology has the potential to transform the practice of pathology, but, despite this, many pathologists are unfamiliar with how these models are created, trained, and evaluated. In addition, many pathologists may feel that they do not possess the necessary skills to allow them to embark on research into this field. This article aims to act as an introductory tutorial to illustrate how to create, train, and evaluate simple artificial learning models (neural networks) on histopathology data sets in the programming language Python using the popular freely available, open-source libraries Keras, TensorFlow, PyTorch, and Detecto.

Brief Report: HIV-Associated Hodgkin Lymphoma Involving the Bone Marrow Identifies a Very High-Risk Subpopulation in the Era of Widescale Antiretroviral Therapy Use in Johannesburg, South Africa.

Background: The incidence of HIV-associated Hodgkin lymphoma (HIV-HL) has not dropped in the era of widespread antiretroviral therapy (ART), and there have reportedly been shifts in the most prevalent variants encountered. In this study, factors of interest in cases of HIV-HL diagnosed before and after the widespread availability of ART in Johannesburg, South Africa, were compared.

Methods: All cases of HIV-HL diagnosed in 2007 and 2017 were extracted from the laboratory information system, and pertinent factors compared.

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice.

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting.

Extraoral plasmablastic lymphomas in a high human immunodeficiency virus endemic area.

Aims: Plasmablastic lymphoma (PBL) occurs mainly in immunocompromised individuals, usually secondary to human immunodeficiency virus (HIV) infection. It classically occurs intraorally, but has been described in extraoral locations. The aim of this study was to define the immunophenotype and Epstein-Barr virus (EBV) status in a large single-centre cohort of extraoral PBL (EPBL) in South Africa, a high-prevalence HIV setting.