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

作　　者：Sony Sinha Prasanna Venkatesh Ramesh Prateek Nishant Arvind Kumar Morya Ripunjay Prasad 

机构地区：[1]Department of Ophthalmology–Vitreo Retina,Neuro Ophthalmology and Oculoplasty,All India Institute of Medical Sciences,Patna 801507,India [2]Department of Glaucoma and Research,Mahathma Eye Hospital Private Limited,Trichy 620017,India [3]Department of Ophthalmology,ESIC Medical College,Patna 801113,India [4]Department of Ophthalmology,All India Institute of Medical Sciences,Hyderabad 508126,India [5]Department of Ophthalmology,RP Eye Institute,Delhi 110001,India

出　　处：《World Journal of Methodology》2024年第2期51-64,共14页世界方法学杂志

摘　　要：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.

关 键 词：Conjunctival neoplasm Early detection of cancer Machine learning Deep neural network Precision medicine 

分 类 号：R739.41[医药卫生—肿瘤]