Artificial intelligence derived grading of mustard gas induced corneal injury and opacity
Abstract 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 m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08042-x |
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| author | Rajnish Kumar Devansh M. Sinha Nishant R. Sinha Ratnakar Tripathi Nathan Hesemann Suneel Gupta Anil Tiwari Rajiv R. Mohan |
| author_facet | Rajnish Kumar Devansh M. Sinha Nishant R. Sinha Ratnakar Tripathi Nathan Hesemann Suneel Gupta Anil Tiwari Rajiv R. Mohan |
| author_sort | Rajnish Kumar |
| collection | DOAJ |
| description | Abstract 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 an objective grading of corneal injury and opacity levels in live rabbits following ocular exposure of sulfur mustard (SM). An automated method to grade corneal injury minimizes diagnostic errors and enhances translational application of preclinical research in better human eyecare. SM induced corneal injury and opacity from 401 in-house rabbit corneal images captured with a clinical stereomicroscope were used. Three independent subject matter specialists classified corneal images into four health grades: healthy, mild, moderate, and severe. Mask-RCNN was employed for precise corneal segmentation and extraction, followed by classification using baseline convolutional neural network and transfer learning algorithms, including VGG16, ResNet101, DenseNet121, InceptionV3, and ResNet50. The ResNet50-based model demonstrated the best performance, achieving 87% training accuracy, and 85% and 83% prediction accuracies on two independent test sets. This deep learning framework, combining Mask-RCNN with ResNet50 allows reliable and uniform grading of SM-induced corneal injury and opacity levels in affected eyes. |
| format | Article |
| id | doaj-art-3fd032f933cd418aafbfdad1ae2c587d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3fd032f933cd418aafbfdad1ae2c587d2025-08-20T03:37:30ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-08042-xArtificial intelligence derived grading of mustard gas induced corneal injury and opacityRajnish Kumar0Devansh M. Sinha1Nishant R. Sinha2Ratnakar Tripathi3Nathan Hesemann4Suneel Gupta5Anil Tiwari6Rajiv R. Mohan7Harry S. Truman Memorial Veterans’ HospitalDepartment of Veterinary Medicine & Surgery, College of Veterinary Medicine, University of MissouriHarry S. Truman Memorial Veterans’ HospitalHarry S. Truman Memorial Veterans’ HospitalHarry S. Truman Memorial Veterans’ HospitalHarry S. Truman Memorial Veterans’ HospitalDepartment of Veterinary Medicine & Surgery, College of Veterinary Medicine, University of MissouriHarry S. Truman Memorial Veterans’ HospitalAbstract 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 an objective grading of corneal injury and opacity levels in live rabbits following ocular exposure of sulfur mustard (SM). An automated method to grade corneal injury minimizes diagnostic errors and enhances translational application of preclinical research in better human eyecare. SM induced corneal injury and opacity from 401 in-house rabbit corneal images captured with a clinical stereomicroscope were used. Three independent subject matter specialists classified corneal images into four health grades: healthy, mild, moderate, and severe. Mask-RCNN was employed for precise corneal segmentation and extraction, followed by classification using baseline convolutional neural network and transfer learning algorithms, including VGG16, ResNet101, DenseNet121, InceptionV3, and ResNet50. The ResNet50-based model demonstrated the best performance, achieving 87% training accuracy, and 85% and 83% prediction accuracies on two independent test sets. This deep learning framework, combining Mask-RCNN with ResNet50 allows reliable and uniform grading of SM-induced corneal injury and opacity levels in affected eyes.https://doi.org/10.1038/s41598-025-08042-xArtificial intelligenceCorneaFibrosisSulfur mustardPathology |
| spellingShingle | Rajnish Kumar Devansh M. Sinha Nishant R. Sinha Ratnakar Tripathi Nathan Hesemann Suneel Gupta Anil Tiwari Rajiv R. Mohan Artificial intelligence derived grading of mustard gas induced corneal injury and opacity Scientific Reports Artificial intelligence Cornea Fibrosis Sulfur mustard Pathology |
| title | Artificial intelligence derived grading of mustard gas induced corneal injury and opacity |
| title_full | Artificial intelligence derived grading of mustard gas induced corneal injury and opacity |
| title_fullStr | Artificial intelligence derived grading of mustard gas induced corneal injury and opacity |
| title_full_unstemmed | Artificial intelligence derived grading of mustard gas induced corneal injury and opacity |
| title_short | Artificial intelligence derived grading of mustard gas induced corneal injury and opacity |
| title_sort | artificial intelligence derived grading of mustard gas induced corneal injury and opacity |
| topic | Artificial intelligence Cornea Fibrosis Sulfur mustard Pathology |
| url | https://doi.org/10.1038/s41598-025-08042-x |
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