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Diagnostics
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Data Analysis in Ophthalmic Diagnostics
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Data Analysis in Ophthalmic Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 18198

Special Issue Editors

Prof. Dr. Yanwu Xu

E-Mail Website
Guest Editor
1. Chinese Academy of Sciences, Beijing, China
2. Singapore Eye Research Institute, Singapore, Singapore
3. School of Future Technol, South China University of Technology, Guangzhou, China
Interests: ophthalmic image analysis; medical image analysis; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Huiying Liu

E-Mail Website
Co-Guest Editor
Institute for Infocomm Research, A*STAR, Singapore
Interests: medical image analysis; computer vision; multimedia

Special Issue Information

Dear Colleagues, 

Data analysis is crucial for modern disease diagnostics. With the development of analytic tools (e.g., linear regression, principal component analysis, GWAS, and content-based image retrieval), the effectiveness and efficiency of diagnostics continue to improve. Eye diseases represent a global health challenge. The cost of blindness to society and individuals is enormous; early and accurate diagnostics and timely intervention can avoid many conditions. Increasing amounts of data can be used for ophthalmic diagnostics, especially when many imaging devices have been deployed to capture the image or video of the eye in high detail, from the front to the back and from the external appearance to the inside. Meanwhile, metadata such as demographic data, genome data, and electronic health records (EHR) provide additional information to facilitate more accurate diagnostics. Significant challenges remain in terms of reliability and repeatability, multiple source fusion, multiple center validation, and the effective transfer of advanced artificial intelligence technologies.

This collection focuses on novel techniques and study reports of data analysis in ophthalmic diagnostics, which include, but are not limited to:

  • Clinical trial reports of data analysis in ophthalmic diagnostics;
  • Clinical validation;
  • Combined data analysis of the eye and other organs;
  • Computer-aided diagnostics of ophthalmic diseases;
  • Image analysis in ophthalmic diagnostics;
  • Metadata analysis for ophthalmic diseases;
  • Fusion of multiple data sources for ophthalmic diagnostics;
  • Ophthalmic image atlas;
  • Population data analysis in ophthalmology;
  • Registration of ophthalmic images;
  • Segmentation of eye structures (e.g., vasculature, lesions, landmarks).

Prof. Dr. Yanwu Xu
Dr. Huiying Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data analysis
  • metadata analysis
  • combined data analysis
  • ophthalmic image analysis
  • computer-aided diagnostics
  • multiple data source fusion

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Published Papers (4 papers)

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Research

23 pages, 5956 KiB  
Article
Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features
by Ahlam Shamsan, Ebrahim Mohammed Senan and Hamzeh Salameh Ahmad Shatnawi
Diagnostics 2023, 13(10), 1706; https://doi.org/10.3390/diagnostics13101706 - 11 May 2023
Cited by 36 | Viewed by 6047
Abstract
Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty [...] Read more.
Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%. Full article
(This article belongs to the Special Issue Data Analysis in Ophthalmic Diagnostics)
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22 pages, 1304 KiB  
Article
Identifying the Key Components in ResNet-50 for Diabetic Retinopathy Grading from Fundus Images: A Systematic Investigation
by Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Roger Tam and Xiaoying Tang
Diagnostics 2023, 13(10), 1664; https://doi.org/10.3390/diagnostics13101664 - 9 May 2023
Cited by 19 | Viewed by 4957
Abstract
Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent components, such as an objective function, a data sampling [...] Read more.
Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent components, such as an objective function, a data sampling strategy, and a data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation; (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically weighted Kappa; (3) utilizing eye pairs boosts the performance of DR grading and; (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves a state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42,670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available online. Full article
(This article belongs to the Special Issue Data Analysis in Ophthalmic Diagnostics)
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17 pages, 9655 KiB  
Article
A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet
by Midhula Vijayan and Venkatakrishnan S
Diagnostics 2023, 13(4), 774; https://doi.org/10.3390/diagnostics13040774 - 17 Feb 2023
Cited by 15 | Viewed by 3343
Abstract
The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Manually diagnosing [...] Read more.
The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Manually diagnosing DR through color fundus images requires a skilled clinician to spot lesions, but this can be challenging, especially in areas with a shortage of trained experts. As a result, there is a push to create computer-aided diagnosis systems for DR to help reduce the time it takes to diagnose the condition. The detection of diabetic retinopathy through automation is challenging, but convolutional neural networks (CNNs) play a vital role in achieving success. CNNs have been proven to be more effective in image classification than methods based on handcrafted features. This study proposes a CNN-based approach for the automated detection of DR using Efficientnet-B0 as the backbone network. The authors of this study take a unique approach by viewing the detection of diabetic retinopathy as a regression problem rather than a traditional multi-class classification problem. This is because the severity of DR is often rated on a continuous scale, such as the international clinical diabetic retinopathy (ICDR) scale. This continuous representation provides a more nuanced understanding of the condition, making regression a more suitable approach for DR detection compared to multi-class classification. This approach has several benefits. Firstly, it allows for more fine-grained predictions as the model can assign a value that falls between the traditional discrete labels. Secondly, it allows for better generalization. The model was tested on the APTOS and DDR datasets. The proposed model demonstrated improved efficiency and accuracy in detecting DR compared to traditional methods. This method has the potential to enhance the efficiency and accuracy of DR diagnosis, making it a valuable tool for healthcare professionals. The model has the potential to aid in the rapid and accurate diagnosis of DR, leading to the improved early detection, and management, of the disease. Full article
(This article belongs to the Special Issue Data Analysis in Ophthalmic Diagnostics)
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20 pages, 1550 KiB  
Article
A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset
by Ayesha Mehboob, Muhammad Usman Akram, Norah Saleh Alghamdi and Anum Abdul Salam
Diagnostics 2022, 12(12), 3084; https://doi.org/10.3390/diagnostics12123084 - 7 Dec 2022
Cited by 15 | Viewed by 2539
Abstract
Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e., mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative Diabetic Retinopathy. The disease has no noticeable symptoms at early stages and may lead to [...] Read more.
Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e., mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative Diabetic Retinopathy. The disease has no noticeable symptoms at early stages and may lead to chronic destruction, thus causing permanent blindness if not detected at an early stage. The proposed research provides deep learning frameworks for autonomous detection of Diabetic Retinopathy at an early stage using fundus images. The first framework consists of cascaded neural networks, spanned in three layers where each layer classifies data into two classes, one is the desired stage and the other output is passed to another classifier until the input image is classified as one of the stages. The second framework takes normalized, HSV and RGB fundus images as input to three Convolutional Neural Networks, and the resultant probabilistic vectors are averaged together to obtain the final output of the input image. Third framework used the Long Short Term Memory Module in CNN to emphasize the network in remembering information over a long time span. Proposed frameworks were tested and compared on the large-scale Kaggle fundus image dataset EYEPAC. The evaluations have shown that the second framework outperformed others and achieved an accuracy of 78.06% and 83.78% without and with augmentation, respectively. Full article
(This article belongs to the Special Issue Data Analysis in Ophthalmic Diagnostics)
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