Sinha S, Ramesh PV, Nishant P, Morya AK, Prasad R. Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence. World J Methodol 2024; 14(2): 92267 [PMID: 38983656 DOI: 10.5662/wjm.v14.i2.92267]
Corresponding Author of This Article
Arvind Kumar Morya, MBBS, MNAMS, Additional Professor, Department of Ophthalmology, All India Institute of Medical Sciences, Bibi Nagar, Hyderabad 508126, India. bulbul.morya@gmail.com
Research Domain of This Article
Ophthalmology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Sony Sinha, Department of Ophthalmology–Vitreo Retina, Neuro Ophthalmology and Oculoplasty, All India Institute of Medical Sciences, Patna 801507, India
Prasanna Venkatesh Ramesh, Department of Glaucoma and Research, Mahathma Eye Hospital Private Limited, Trichy 620017, India
Prateek Nishant, Department of Ophthalmology, ESIC Medical College, Patna 801113, India
Arvind Kumar Morya, Department of Ophthalmology, All India Institute of Medical Sciences, Hyderabad 508126, India
Ripunjay Prasad, Department of Ophthalmology, RP Eye Institute, Delhi 110001, India
Author contributions: Sinha S, Ramesh PV and Nishant P analyzed data and wrote the manuscript; Sinha S and Nishant P performed research; Nishant P and Prasad R revised the manuscript; Morya AK designed the research.
Conflict-of-interest statement: The authors declare there are no conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Arvind Kumar Morya, MBBS, MNAMS, Additional Professor, Department of Ophthalmology, All India Institute of Medical Sciences, Bibi Nagar, Hyderabad 508126, India. bulbul.morya@gmail.com
Received: January 20, 2024 Revised: February 19, 2024 Accepted: April 12, 2024 Published online: June 20, 2024 Processing time: 145 Days and 17.6 Hours
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.
Core Tip: This is a unique comprehensive review written on a common eye tumour-ocular surface squamous neoplasia (OSSN) and the role of artificial intelligence (AI) in the early diagnosis and management of this ocular condition. This write-up also covers the various intricacies involved in developing AI algorithms based on digital and histopathological images of OSSN.
