Application of a Deep Learning System in Pterygium Grading and Further Prediction of Recurrence with Slit Lamp Photographs

A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images

In this article, various convolutional neural network (CNN) architectures for the detection of pterygium in the anterior segment of the eye are explored and compared. Five CNN architectures (ResNet101, ResNext101, Se-ResNext50, ResNext50, and MobileNet V2) are evaluated with the objective of identifying one that surpasses the precision and diagnostic efficacy of the current existing solutions. The results show that the Se-ResNext50 architecture offers the best overall performance in terms of precision, recall, and accuracy, with values of 93%, 92%, and 92%, respectively, for these metrics. These results demonstrate its potential to enhance diagnostic tools in ophthalmology.

Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2

AIM

To evaluate the application of an intelligent diagnostic model for pterygium.

METHODS

For intelligent diagnosis of pterygium, the attention mechanisms-SENet, ECANet, CBAM, and Self-Attention-were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University. Conventional classification models-VGG16, ResNet50, MobileNetV2, and EfficientNetB7-were trained on the same dataset for comparison. To evaluate model performance in terms of accuracy, Kappa value, test time, sensitivity, specificity, the area under curve (AUC), and visual heat map, 470 test images of the anterior segment of the pterygium were used.

RESULTS

The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%, and the Kappa value of the model was 88.92%. The testing time using the model was 9ms/image in the server and 138ms/image in the local computer. The sensitivity, specificity, and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%, 100%, and 100%, respectively; using anterior segment images in the observation period were 88.30%, 95.32%, and 96.70%, respectively; and using the anterior segment images in the surgery period were 88.18%, 94.44%, and 97.30%, respectively.

CONCLUSION

The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.

Unravelling the molecular tapestry of pterygium: insights into genes for diagnostic and therapeutic innovations

Pterygium, an ocular surface disorder, manifests as a wing-shaped extension from the corneoscleral limbus onto the cornea, impacting vision and causing inflammation. With a global prevalence of 12%, varying by region, the condition is linked to UV exposure, age, gender, and socioeconomic factors. This review focuses on key genes associated with pterygium, shedding light on potential therapeutic targets. Matrix metalloproteinases (MMPs), especially MMP2 and MMP9, contribute to ECM remodelling and angiogenesis in pterygium. Vascular endothelial growth factor (VEGF) plays a crucial role in angiogenesis and is elevated in pterygium tissues. B-cell lymphoma-2, S100 proteins, DNA repair genes (hOGG1, XRCC1), CYP monooxygenases, p53, and p16 are implicated in pterygium development. A protein-protein interaction network analysis highlighted 28 edges between the aforementioned proteins, except for VEGF, indicating a high level of interaction. Gene ontology, microRNA and pathway analyses revealed the involvement of processes such as base excision repair, IL-17 and p53 signalling, ECM disassembly, oxidative stress, hypoxia, metallopeptidase activity and others that are essential for pterygium development. In addition, miR-29, miR-125, miR-126, miR-143, miR-200, miR-429, and miR-451a microRNAs were predicted, which were shown to have a role in pterygium development and disease severity. Identification of these molecular mechanisms provides insights for potential diagnostic and therapeutic strategies for pterygium.

Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology-primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.

DBPF-net: dual-branch structural feature extraction reinforcement network for ocular surface disease image classification

Pterygium and subconjunctival hemorrhage are two common types of ocular surface diseases that can cause distress and anxiety in patients. In this study, 2855 ocular surface images were collected in four categories: normal ocular surface, subconjunctival hemorrhage, pterygium to be observed, and pterygium requiring surgery. We propose a diagnostic classification model for ocular surface diseases, dual-branch network reinforced by PFM block (DBPF-Net), which adopts the conformer model with two-branch architectural properties as the backbone of a four-way classification model for ocular surface diseases. In addition, we propose a block composed of a patch merging layer and a FReLU layer (PFM block) for extracting spatial structure features to further strengthen the feature extraction capability of the model. In practice, only the ocular surface images need to be input into the model to discriminate automatically between the disease categories. We also trained the VGG16, ResNet50, EfficientNetB7, and Conformer models, and evaluated and analyzed the results of all models on the test set. The main evaluation indicators were sensitivity, specificity, F1-score, area under the receiver operating characteristics curve (AUC), kappa coefficient, and accuracy. The accuracy and kappa coefficient of the proposed diagnostic model in several experiments were averaged at 0.9789 and 0.9681, respectively. The sensitivity, specificity, F1-score, and AUC were, respectively, 0.9723, 0.9836, 0.9688, and 0.9869 for diagnosing pterygium to be observed, and, respectively, 0.9210, 0.9905, 0.9292, and 0.9776 for diagnosing pterygium requiring surgery. The proposed method has high clinical reference value for recognizing these four types of ocular surface images.

Modified Limbal-Conjunctival Autograft Surgical Technique: Long-Term Results of Recurrence and Complications

PURPOSE

The aim of this study was to report the recurrence and complication rates of a modified limbal-conjunctival autograft surgical technique for pterygium excision.

METHODS

This was a retrospective, single-surgeon, single-operating environment, consecutive case series of 176 eyes in 163 patients with a biopsy-proven diagnosis of pterygium. All patients underwent excision using a 23-gauge needle to "behead" the pterygium head, followed by a limbal-conjunctival autograft including ∼50% of the palisades of Vogt. Outcomes measured included recurrence, defined as any conjunctival fibrovascular growth, and complication rates. Correlations between preoperative patient characteristics, pterygium morphology, and intraoperative factors (width of corneal extension, conjunctival defect, and graft) with postoperative recurrence were examined using logistic regression models.

RESULTS

The median age was 59.5 years and 122 eyes (69.3%) had primary pterygium (type I: 17%, II: 37.5%, and III: 45.5%). Kaplan-Meier analysis demonstrated the median pterygium-free follow-up period to be 723 days (range 46-7230 days). Recurrence was observed in 3 eyes of 2 patients (1.7%). No postoperative graft-related complications were observed. Postoperative symptomatology was transient. Age demonstrated a negative correlation with recurrence (odds ratio 0.888, 95% CI, 0.789-0.998, P = 0.046). However, no other correlations with preoperative or intraoperative factors, including whether pterygium was primary or recurrent, were identified (all P > 0.05).

CONCLUSIONS

This modified limbal-conjunctival autograft technique represents an effective alternative that offers a very low recurrence rate and avoids extensive dissection or antimetabolites, with minimal complications and transient postoperative symptomatology, over a long-term follow-up period. This technique is relatively simple and successful for both primary and recurrent pterygia. Future comparative studies with other surgical techniques may determine which are superior.

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

Artificial intelligence-assisted diagnosis of ocular surface diseases

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

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

Background and aim

A 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).

Methods

A 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.

Results

The 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.

Conclusion

This 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.

Advances in artificial intelligence applications for ocular surface diseases diagnosis

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.