Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025
Classification of ocular surface diseases: Deep learning for distinguishing ocular surface squamous neoplasia from pterygium.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ramezani Farshid, Azimi Hossein, Delfanian Behrouz, Amanollahi Mobina, Saeidian Jamshid, Masoumi Ahmad, Farrokhpour Hossein, Khalili Pour Elias, Khodaparast Mehdi
Аннотация
PURPOSE: Given the significance and potential risks associated with Ocular Surface Squamous Neoplasia (OSSN) and the importance of its differentiation from other conditions, we aimed to develop a Deep Learning (DL) model differentiating OSSN from pterygium (PTG) using slit photographs.
METHODS: A dataset comprising slit photographs of 162 patients including 77 images of OSSN and 85 images of PTG was assembled. After manual segmentation of the images, a Python-based transfer learning approach utilizing the EfficientNet B7 network was employed for automated image segmentation. GoogleNet, a pre-trained neural network was used to categorize the images into OSSN or PTG. To evaluate the performance of our DL model, K-Fold 10 Cross Validation was implemented, and various performance metrics were measured.
RESULTS: There was a statistically significant difference in mean age between the OSSN (63.23 ± 13.74 years) and PTG groups (47.18 ± 11.53) (P-value =.000). Furthermore, 84.41% of patients in the OSSN group and 80.00% of the patients in the PTG group were male. Our classification model, trained on automatically segmented images, demonstrated reliable performance measures in distinguishing OSSN from PTG, with an Area Under Curve (AUC) of 98%, sensitivity, F1 score, and accuracy of 94%, and a Matthews Correlation Coefficient (MCC) of 88%.
CONCLUSIONS: This study presents a novel DL model that effectively segments and classifies OSSN from PTG images with a relatively high accuracy. In addition to its clinical use, this model can be potentially used as a telemedicine application.
METHODS: A dataset comprising slit photographs of 162 patients including 77 images of OSSN and 85 images of PTG was assembled. After manual segmentation of the images, a Python-based transfer learning approach utilizing the EfficientNet B7 network was employed for automated image segmentation. GoogleNet, a pre-trained neural network was used to categorize the images into OSSN or PTG. To evaluate the performance of our DL model, K-Fold 10 Cross Validation was implemented, and various performance metrics were measured.
RESULTS: There was a statistically significant difference in mean age between the OSSN (63.23 ± 13.74 years) and PTG groups (47.18 ± 11.53) (P-value =.000). Furthermore, 84.41% of patients in the OSSN group and 80.00% of the patients in the PTG group were male. Our classification model, trained on automatically segmented images, demonstrated reliable performance measures in distinguishing OSSN from PTG, with an Area Under Curve (AUC) of 98%, sensitivity, F1 score, and accuracy of 94%, and a Matthews Correlation Coefficient (MCC) of 88%.
CONCLUSIONS: This study presents a novel DL model that effectively segments and classifies OSSN from PTG images with a relatively high accuracy. In addition to its clinical use, this model can be potentially used as a telemedicine application.