Artificial intelligence derived grading of mustard gas induced corneal injury and opacity

• Published in: Scientific reports Vol. 15; no. 1; pp. 20359 - 13
• Main Authors: Kumar, Rajnish, Sinha, Devansh M., Sinha, Nishant R., Tripathi, Ratnakar, Hesemann, Nathan, Gupta, Suneel, Tiwari, Anil, Mohan, Rajiv R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 631/114; 692/699/3161/3163; Algorithms; Animals; Artificial Intelligence; Cornea; Cornea - drug effects; Cornea - pathology; Corneal Injuries - chemically induced; Corneal Injuries - diagnosis; Corneal Injuries - pathology; Corneal Opacity - chemically induced; Corneal Opacity - diagnosis; Corneal Opacity - pathology; Disease Models, Animal; Fibrosis; Humanities and Social Sciences; Humans; multidisciplinary; Mustard Gas - adverse effects; Mustard Gas - toxicity; Neural Networks, Computer; Pathology; Rabbits; Science; Science (multidisciplinary); Sulfur mustard; Pathology; Cornea; Artificial intelligence; Fibrosis; Sulfur mustard

Artificial intelligence (AI) has emerged as a transformative tool in ophthalmology for disease diagnosis and prognosis. However, use of AI for assessing corneal damage due to chemical injury in live rabbits remains lacking. This study aimed to develop an AI-derived clinical classification model for...