Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence

Abstract

Ocular surface squamous neoplasia (OSSN) is a common eye surface tumour, characterized by the growth of abnormal cells on the ocular surface. OSSN includes invasive squamous cell carcinoma (SCC), in which tumour cells penetrate the basement membrane and infiltrate the stroma, as well as non-invasive conjunctival intraepithelial neoplasia, dysplasia, and SCC in-situ thereby presenting a challenge in early detection and diagnosis. Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments, such as topical medicines, whereas advanced invasive lesions may need orbital exenteration, which carries a risk of death. Artificial intelligence (AI) has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management. AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN, aiding ophthalmologists in early detection and diagnosis. AI can also track and monitor lesion progression over time, providing objective measurements to guide treatment decisions. Furthermore, AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response. This manuscript highlights the role of AI in OSSN, specifically focusing on its contributions in early detection and diagnosis, assessment of lesion progression, treatment planning, telemedicine and remote monitoring, and research and data analysis.