Advances in AI-based cancer cytopathology

Abstract

Cytopathological examination plays a crucial role in cancer diagnosis as it reflects the cellular pathology of cancer. However, this process traditionally relies on the visual examination by cytopathologists. Recent advancements in computer and digital imaging technologies have enabled the application of artificial intelligence (AI)-based models to identify tumor cells in images, thereby assisting cytopathologists in achieving enhanced performance. AI-based models can improve the accuracy and reproducibility of image evaluation and streamline clinical workflows. Moreover, AI-based models can analyze a diverse range of sample types, including peripheral blood, urine, ascites, and bone marrow. AI-based cytopathological recognition can help clinicians screen and diagnose cancer, predict prognosis and recurrence of cancers, such as leukemia, cervical cancer, urothelial carcinoma, and gastric cancer. Additionally, AI-based models can predict the types of mutations in leukemia. A growing number of studies emphasize the potential of computational image analysis and deep learning-based AI to build novel diagnostic tools that are conducive to the biomedical field. This review describes the recent developments in AI-based cytopathological recognition and offers a perspective on how AI tools of cytopathology can help improve cancer diagnosis and prognosis prediction. Future developments in AI model applications can further contribute to the improvement of human health.