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

The State of Telepathology in Africa in the Age of Digital Pathology Advancements: A Bibliometric Analysis and Literature Review

Telepathology emerges as a vital tool, offering significant promise for enhancing pathology services in Africa, a region historically challenged by healthcare access and resource limitations. This review explores the development, adoption, and impacts of telepathology in Africa through a comprehensive bibliometric analysis and literature review. A methodical search in PubMed for publications up to 2024 revealed 119 pertinent studies, out of which 47 met the inclusion criteria for a focused review on telepathology's role in African healthcare settings. This research has charted a clear trajectory of growing interest in telepathology, as evidenced by the annual increase in related publications and robust international collaboration. It underscores the expanding utility of telepathology in diagnostics, education, and research within Africa, particularly in domains like dermatopathology, neuropathology, and, notably, oncology. The integration of artificial intelligence into telepathology presents new frontiers for enhancing diagnostic accuracy and efficiency. However, the review also identifies persistent challenges such as infrastructural inadequacies, a shortage of skilled professionals, and regulatory hurdles. The study highlights the indispensable role of international partnerships in advancing telepathology in the region. This review proposes a strategic pivot toward "leapfrogging," an approach that allows Africa to skip traditional developmental hurdles by directly adopting cutting-edge technologies and practices.

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.

MATERIALS AND METHODS

Synthetic urine cytology images were generated using a custom-trained conditional StyleGAN3 model. A morphologically balanced 60-image data set of real and synthetic urine cytology images was created for an online image survey system to allow for the assessment of the differences in visual perception between real and synthetic urine cytology images by pathology personnel.

RESULTS

A total of 12 participants were recruited to answer the 60-image survey. The study population had a median age of 36.5 years and a median of 5 years of pathology experience. There was no significant difference in diagnostic error rates between real and synthetic images, nor was there a significant difference between subjective image quality scores between real and synthetic images when assessed on an individual observer basis.

CONCLUSIONS

The ability of Generative Adversarial Networks technology to generate highly realistic urine cytology images was demonstrated. Furthermore, there was no difference in how pathology personnel perceived the subjective quality of synthetic images, nor was there a difference in diagnostic error rates between real and synthetic urine cytology images. This has important implications for the application of Generative Adversarial Networks technology to cytology teaching and learning.

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.

MATERIALS AND METHODS

A limited, but morphologically diverse, dataset of 1000 malignant urothelial cytology images was used to train a StyleGAN3 model to create completely novel, synthetic examples of malignant urine cytology using computer resources within reach of most pathology departments worldwide.

RESULTS

We have presented the results of our trained GAN model, which was able to generate realistic, morphologically diverse examples of malignant urine cytology images when trained using a modest dataset. Although the trained model is capable of generating realistic images, we have also presented examples for which unrealistic and artifactual images were generated-illustrating the need for manual curation when using this technology in a training context.

CONCLUSIONS

We have presented a proof-of-concept illustration of creating synthetic malignant urine cytology images using machine learning technology to augment cytology training when real-world examples are sparse. We have shown that despite significant morphologic diversity in terms of staining variations, slide background, variations in the diagnostic malignant cellular elements, the presence of other nondiagnostic cellular elements, and artifacts, visually acceptable and varied results are achievable using limited data and computing resources.