Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images

Abstract

Background and aimA pterygium is a common ocular surface disease, which not only affects facial appearance but can also grow into the tissue layer, causing astigmatism and vision loss. In this study, an artificial intelligence model was developed for detecting the pterygium that requires surgical treatment. The model was designed using ensemble deep learning (DL).MethodsA total of 172 anterior segment images of pterygia were obtained from the Jiangxi Provincial People’s Hospital (China) between 2017 and 2022. They were divided by a senior ophthalmologist into the non-surgery group and the surgery group. An artificial intelligence model was then developed based on ensemble DL, which was integrated with four benchmark models: the Resnet18, Alexnet, Googlenet, and Vgg11 model, for detecting the pterygium that requires surgical treatment, and Grad-CAM was used to visualize the DL process. Finally, the performance of the ensemble DL model was compared with the classical Resnet18 model, Alexnet model, Googlenet model, and Vgg11 model.ResultsThe accuracy and area under the curve (AUC) of the ensemble DL model was higher than all of the other models. In the training set, the accuracy and AUC of the ensemble model was 94.20% and 0.978, respectively. In the testing set, the accuracy and AUC of the ensemble model was 94.12% and 0.980, respectively.ConclusionThis study indicates that this ensemble DL model, coupled with the anterior segment images in our study, might be an automated and cost-saving alternative for detection of the pterygia that require surgery.

Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma

“…[ 43 , 44 ]. GLSZM, GLRLM, and GLCM are regional textural features and have been applied to emphasize local heterogeneity information, as the ability to distinguish patients with distinct prognoses has already been confirmed for other tumors [ 45 – 47 ]. A previous study demonstrated that heterogeneity in MRI distribution within tumors serves as a valuable biomarker for predicting treatment outcomes in patients with NPC [ 28 ].…”

Mobile Application Based on Convolutional Neural Networks for Pterygium Detection in Anterior Segment Eye Images at Ophthalmological Medical Centers

“…Most of these investigations use models like ResNet50, AlexNet, LSTM, and Inception, which have also been applied to pterygium. Common models include VGG16 [4], [7], [20], [21], ResNet, in some of its variants such as ResNet18, ResNet50, or ResNet101, [4], [20], [21], AlexNet and GoogleNet [4], [20]. Other architectures not as common but also relevant include DenseNet201 [4], the EfficientNet family [22] and the MobileNet family.…”

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

“…Table 2 [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58] shows a summary of 16 papers that used deep learning methods to assist in diagnosing pterygium in chronological order, along with their summary information. Different from conventional methods, the researchers first tried on the pterygium classification task due to the end-toend characteristics of deep learning.…”