Review of Machine Learning Applications Using Retinal Fundus Images

A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning

Purpose

To assess the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) and Vertex AI in executing code-free preprocessing, training machine learning (ML) models, and analyzing the data.

Design

Evaluation of diagnostic test or technology.

Participants

ChatGPT and Vetrex AI as publicly available large language model and ML platform, respectively.

Methods

ChatGPT was employed to improve the resolution of fundus photography images from the Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (Messidor-2) open-source dataset using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique by Fiji software. Subsequently, Vertex AI, an automated ML (AutoML) platform, was utilized to develop 2 classification models. The first model served as a binary classifier for detecting the presence of diabetic retinopathy (DR), while the second determined its severity. Finally, ChatGPT was used to provide scripts for R and Python programming languages for data analysis and was also directly employed in analyzing the data in a code-free method.

Main Outcome Measures

Evaluating the utility of ChatGPT in generating scripts for preprocessing images using Fiji and analyzing data across Python and R and assessing its potential in analyzing data through a code-free method. Investigating the capabilities of Vertex AI to train image classification models for detection of DR and its severity.

Results

Two ML models were trained using 1740 images from the Messidor-2 database. The first model, designed to detect the severity of DR, achieved an area under the precision-recall curve (AUPRC) of 0.81, with a precision rate of 81.81% and recall of 72.83%. The second model, tailored for the detection of the presence of DR, recorded a precision and recall of 84.48% with an AUPRC of 0.90.

Conclusions

ChatGPT and Vertex AI have the potential to enable physicians without coding expertise to preprocess images, analyze data, and train ML models.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Autonomous screening for laser photocoagulation in fundus images using deep learning

BACKGROUND

Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning algorithms has been increasingly used in retinal image analysis, particularly for the screening of referrable DR. An established treatment for proliferative DR is panretinal or focal laser photocoagulation. Training autonomous models to discern laser patterns can be important in disease management and follow-up.

METHODS

A deep learning model was trained for laser treatment detection using the EyePACs dataset. Data was randomly assigned, by participant, into development (n=18 945) and validation (n=2105) sets. Analysis was conducted at the single image, eye, and patient levels. The model was then used to filter input for three independent AI models for retinal indications; changes in model efficacy were measured using area under the receiver operating characteristic curve (AUC) and mean absolute error (MAE).

RESULTS

On the task of laser photocoagulation detection: AUCs of 0.981, 0.95, and 0.979 were achieved at the patient, image, and eye levels, respectively. When analysing independent models, efficacy was shown to improve across the board after filtering. Diabetic macular oedema detection on images with artefacts was AUC 0.932 vs AUC 0.955 on those without. Participant sex detection on images with artefacts was AUC 0.872 vs AUC 0.922 on those without. Participant age detection on images with artefacts was MAE 5.33 vs MAE 3.81 on those without.

CONCLUSION

The proposed model for laser treatment detection achieved high performance on all analysis metrics and has been demonstrated to positively affect the efficacy of different AI models, suggesting that laser detection can generally improve AI-powered applications for fundus images.

Automated segmentation for early detection of uveal melanoma

OBJECTIVE

Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This study explores the potential of machine learning to automate tumour segmentation. We develop and evaluate a machine-learning model for lesion segmentation using ultra-wide-field fundus photography.

METHOD

A retrospective chart review was conducted of patients diagnosed with uveal melanoma, choroidal nevi, or congenital hypertrophy of the retinal pigmented epithelium at a tertiary academic medical centre. Included patients had a single ultra-wide-field fundus photograph (Optos PLC, Dunfermline, Fife, Scotland) of adequate quality to visualize the lesion of interest, as confirmed by a single ocular oncologist. These images were used to develop and test a machine-learning algorithm for lesion segmentation.

RESULTS

A total of 396 images were used to develop a machine-learning algorithm for lesion segmentation. Ninety additional images were used in the testing data set along with images of 30 healthy control individuals. Of the images with successfully detected lesions, the machine-learning segmentation yielded Dice coefficients of 0.86, 0.81, and 0.85 for uveal melanoma, choroidal nevi, and congenital hypertrophy of the retinal pigmented epithelium, respectively. Sensitivities for any lesion detection per image were 1.00, 0.90, and 0.87, respectively. For images without lesions, specificity was 0.93.

CONCLUSION

Our study demonstrates a novel machine-learning algorithm's performance, suggesting its potential clinical utility as a widely accessible method of screening choroidal tumours. Additional evaluation methods are necessary to further enhance the model's lesion classification and diagnostic accuracy.

Deep learning-based fundus image analysis for cardiovascular disease: a review

It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.

DCAM-NET:A novel domain generalization optic cup and optic disc segmentation pipeline with multi-region and multi-scale convolution attention mechanism

Fundus images are an essential basis for diagnosing ocular diseases, and using convolutional neural networks has shown promising results in achieving accurate fundus image segmentation. However, the difference between the training data (source domain) and the testing data (target domain) will significantly affect the final segmentation performance. This paper proposes a novel framework named DCAM-NET for fundus domain generalization segmentation, which substantially improves the generalization ability of the segmentation model to the target domain data and enhances the extraction of detailed information on the source domain data. This model can effectively overcome the problem of poor model performance due to cross-domain segmentation. To enhance the adaptability of the segmentation model to target domain data, this paper proposes a multi-scale attention mechanism module (MSA) that functions at the feature extraction level. Extracting different attribute features to enter the corresponding scale attention module further captures the critical features in channel, position, and spatial regions. The MSA attention mechanism module also integrates the characteristics of the self-attention mechanism, it can capture dense context information, and the aggregation of multi-feature information effectively enhances the generalization of the model when dealing with unknown domain data. In addition, this paper proposes the multi-region weight fusion convolution module (MWFC), which is essential for the segmentation model to extract feature information from the source domain data accurately. Fusing multiple region weights and convolutional kernel weights on the image to enhance the model adaptability to information at different locations on the image, the fusion of weights deepens the capacity and depth of the model. It enhances the learning ability of the model for multiple regions on the source domain. Our experiments on fundus data for cup/disc segmentation show that the introduction of MSA and MWFC modules in this paper effectively improves the segmentation ability of the segmentation model on the unknown domain. And the performance of the proposed method is significantly better than other methods in the current domain generalization segmentation of the optic cup/disc.

Development of a Computer System for Automatically Generating a Laser Photocoagulation Plan to Improve the Retinal Coagulation Quality in the Treatment of Diabetic Retinopathy

In this article, the development of a computer system for high-tech medical uses in ophthalmology is proposed. An overview of the main methods and algorithms that formed the basis of the coagulation plan planning system is presented. The system provides the formation of a more effective plan for laser coagulation in comparison with the use of existing coagulation techniques. An analysis of monopulse- and pattern-based laser coagulation techniques in the treatment of diabetic retinopathy has shown that modern treatment methods do not provide the required efficacy of medical laser coagulation procedures, as the laser energy is nonuniformly distributed across the pigment epithelium and may exert an excessive effect on parts of the retina and anatomical elements. The analysis has shown that the efficacy of retinal laser coagulation for the treatment of diabetic retinopathy is determined by the relative position of coagulates and parameters of laser exposure. In the course of the development of the computer system proposed herein, main stages of processing diagnostic data were identified. They are as follows: the allocation of the laser exposure zone, the evaluation of laser pulse parameters that would be safe for the fundus, mapping a coagulation plan in the laser exposure zone, followed by the analysis of the generated plan for predicting the therapeutic effect. In the course of the study, it was found that the developed algorithms for placing coagulates in the area of laser exposure provide a more uniform distribution of laser energy across the pigment epithelium when compared to monopulse- and pattern-based laser coagulation techniques.

Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys

Artificial intelligence (AI) has expanded by finding applications in medical diagnosis for clinical support systems [...]

Prediction of complications of type 2 Diabetes: A Machine learning approach

AIM

To construct predictive models of diabetes complications (DCs) by big data machine learning, based on electronic medical records.

METHODS

Six groups of DCs were considered: eye complications, cardiovascular, cerebrovascular, and peripheral vascular disease, nephropathy, diabetic neuropathy. A supervised, tree-based learning approach (XGBoost) was used to predict the onset of each complication within 5 years (task 1). Furthermore, a separate prediction for early (within 2 years) and late (3-5 years) onset of complication (task 2) was performed. A dataset of 147.664 patients seen during 15 years by 23 centers was used. External validation was performed in five additional centers. Models were evaluated by considering accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

RESULTS

For all DCs considered, the predictive models in task 1 showed an accuracy > 70 %, and AUC largely exceeded 0.80, reaching 0.97 for nephropathy. For task 2, all predictive models showed an accuracy > 70 % and an AUC > 0.85. Sensitivity in predicting the early occurrence of the complication ranged between 83.2 % (peripheral vascular disease) and 88.5 % (nephropathy).

CONCLUSIONS

Machine learning approach offers the opportunity to identify patients at greater risk of complications. This can help overcoming clinical inertia and improving the quality of diabetes care.