Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How Immunohistochemistry and AI Algorithms Can Help?

Juvenile onset recurrent respiratory papillomatosis: What do we know in 2024 ?

Juvenile onset recurrent respiratory papillomatosis is a lifelong benign squamous lesion associated with HPV infection, particularly HPV6 and HPV11 genotypes. These lesions are rare, but can lead to laryngeal obturations, which can cause disabling dyspnea, or transform into squamous cell carcinoma. The aim here is to provide an epidemiological, biological and clinical overview of this pathology, particularly in children, in order to understand the issues at stake in terms of research and the development of medical and therapeutic management tools.

Multi-modality artificial intelligence in digital pathology

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.