PloS one · 2022
A novel combination of corneal confocal microscopy, clinical features and artificial intelligence for evaluation of ocular surface pain.
Наблюдательное исследованиеСтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Kundu Gairik, Shetty Rohit, D'Souza Sharon, Khamar Pooja, Nuijts Rudy M M A, Sethu Swaminathan, Roy Abhijit Sinha
Аннотация
OBJECTIVES: To analyse various corneal nerve parameters using confocal microscopy along with systemic and orthoptic parameters in patients presenting with ocular surface pain using a random forest artificial intelligence (AI) model.
DESIGN: Observational, cross-sectional.
METHODS: Two hundred forty eyes of 120 patients with primary symptom of ocular surface pain or discomfort and control group of 60 eyes of 31 patients with no symptoms of ocular pain were analysed. A detailed ocular examination included visual acuity, refraction, slit-lamp and fundus. All eyes underwent laser scanning confocal microscopy (Heidelberg Engineering, Germany) and their nerve parameters were evaluated. The presence or absence of orthoptic issues and connective tissue disorders were included in the AI. The eyes were grouped as those (Group 1) with symptom grade higher than signs, (Group 2) with similar grades of symptoms and signs, (Group3) without symptoms but with signs, (Group 4) without symptoms and signs. The area under curve (AUC), accuracy, recall, precision and F1-score were evaluated.
RESULTS: Over all, the AI achieved an AUC of 0.736, accuracy of 86%, F1-score of 85.9%, precision of 85.6% and recall of 86.3%. The accuracy was the highest for Group 2 and least for Group 3 eyes. The top 6 parameters used for classification by the AI were microneuromas, immature and mature dendritic cells, presence of orthoptic issues and nerve fractal dimension parameter.
CONCLUSIONS: This study demonstrated that various corneal nerve parameters, presence or absence of systemic and orthoptic issues coupled with AI can be a useful technique to understand and correlate the various clinical and imaging parameters of ocular surface pain.
DESIGN: Observational, cross-sectional.
METHODS: Two hundred forty eyes of 120 patients with primary symptom of ocular surface pain or discomfort and control group of 60 eyes of 31 patients with no symptoms of ocular pain were analysed. A detailed ocular examination included visual acuity, refraction, slit-lamp and fundus. All eyes underwent laser scanning confocal microscopy (Heidelberg Engineering, Germany) and their nerve parameters were evaluated. The presence or absence of orthoptic issues and connective tissue disorders were included in the AI. The eyes were grouped as those (Group 1) with symptom grade higher than signs, (Group 2) with similar grades of symptoms and signs, (Group3) without symptoms but with signs, (Group 4) without symptoms and signs. The area under curve (AUC), accuracy, recall, precision and F1-score were evaluated.
RESULTS: Over all, the AI achieved an AUC of 0.736, accuracy of 86%, F1-score of 85.9%, precision of 85.6% and recall of 86.3%. The accuracy was the highest for Group 2 and least for Group 3 eyes. The top 6 parameters used for classification by the AI were microneuromas, immature and mature dendritic cells, presence of orthoptic issues and nerve fractal dimension parameter.
CONCLUSIONS: This study demonstrated that various corneal nerve parameters, presence or absence of systemic and orthoptic issues coupled with AI can be a useful technique to understand and correlate the various clinical and imaging parameters of ocular surface pain.