Current and future roles of artificial intelligence in retinopathy of prematurity

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.Comment: 28 pages, 8 figures, 2 tables, 235 references, 1 supplementary tabl

NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES

The analysis of retinal vasculature in digital fundus images is important for diagnosing eye related diseases. However, digital colour fundus images suffer from low and varied contrast, and are also affected by noise, requiring the use of fundus angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to 6 time’s higher contrast. However, FFA is an invasive method that requires contrast agents to be injected and this can lead other physiological problems. A reported digital image enhancement technique named RETICA that combines Retinex and ICA (Independent Component Analysis) techniques, reduces varied contrast, and enhances the low contrast blood vessels of model fundus images

Automated detection of proliferative diabetic retinopathy from retinal images

Diabetic retinopathy (DR) is a retinal vascular disease associated with diabetes and it is one of the most common causes of blindness worldwide. Diabetic patients regularly attend retinal screening in which digital retinal images are captured. These images undergo thorough analysis by trained individuals, which can be a very time consuming and costly task due to the large diabetic population. Therefore, this is a field that would greatly benefit from the introduction of automated detection systems. This project aims to automatically detect proliferative diabetic retinopathy (PDR), which is the most advanced stage of the disease and poses a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. Their tortuous, convoluted and obscure appearance can make them difficult to detect. In this thesis, we present a methodology based on the novel approach of creating two different segmented vessel maps. Segmentation methods include a standard line operator approach and a novel modified line operator approach. The former targets the accurate segmentation of new vessels and the latter targets the reduction of false responses to non-vessel edges. Both generated binary vessel maps hold vital information which is processed separately using a dual classification framework. Features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. The proposed methodology, using a dataset of 60 images, achieves a sensitivity of 100.00% and a specificity of 92.50% on a per image basis and a sensitivity of 87.93% and a specificity of 94.40% on a per patch basis. The thesis also presents an investigation into the search for the most suitable features for the classification of PDR. This entails the expansion of the feature vector, followed by feature selection using a genetic algorithm based approach. This provides an improvement in results, which now stand at a sensitivity and specificity 3 of 100.00% and 97.50% respectively on a per image basis and 91.38% and 96.00% respectively on a per patch basis. A final extension to the project sees the framework of dual classification further explored, by comparing the results of dual SVM classification with dual ensemble classification. The results of the dual ensemble approach are deemed inferior, achieving a sensitivity and specificity of 100.00% and 95.00% respectively on a per image basis and 81.03% and 95.20% respectively on a per patch basis

A Multi-Anatomical Retinal Structure Segmentation System For Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue to detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This thesis proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogenous anatomical structures

Developing and deploying data mining techniques in healthcare

Improving healthcare is a top priority for all nations. US healthcare expenditure was $3 trillion in 2014. In the same year, the share of GDP assigned to healthcare expenditure was 17.5%. These statistics shows the importance of making improvement in healthcare delivery system. In this research, we developed several data mining methods and algorithms to address healthcare problems. These methods can also be applied to the problems in other domains.The first part of this dissertation is about rare item problem in association analysis. This problem deals with the discovering rare rules, which include rare items. In this study, we introduced a novel assessment metric, called adjusted support to address this problem. By applying this metric, we can retrieve rare rules without over-generating association rules. We applied this method to perform association analysis on complications of diabetes.The second part of this dissertation is developing a clinical decision support system for predicting retinopathy. Retinopathy is the leading cause of vision loss among American adults. In this research, we analyzed data from more than 1.4 million diabetic patients and developed four sets of predictive models: basic, comorbid, over-sampled, and ensemble models. The results show that incorporating comorbidity data and oversampling improved the accuracy of prediction. In addition, we developed a novel "confidence margin" ensemble approach that outperformed the existing ensemble models. In ensemble models, we also addressed the issue of tie in voting-based ensemble models by comparing the confidence margins of the base predictors.The third part of this dissertation addresses the problem of imbalanced data learning, which is a major challenge in machine learning. While a standard machine learning technique could have a good performance on balanced datasets, when applied to imbalanced datasets its performance deteriorates dramatically. This poor performance is rather troublesome especially in detecting the minority class that usually is the class of interest. In this study, we proposed a synthetic informative minority over-sampling (SIMO) algorithm embedded into support vector machine. We applied SIMO to 15 publicly available benchmark datasets and assessed its performance in comparison with seven existing approaches. The results showed that SIMO outperformed all existing approaches

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

Explainable AI for retinal OCT diagnosis

Artificial intelligence methods such as deep learning are leading to great progress in complex tasks that are usually associated with human intelligence and experience. Deep learning models have matched if not bettered human performance for medical diagnosis tasks including retinal diagnosis. Given a sufficient amount of data and computational resources, these models can perform classification and segmentation as well as related tasks such as image quality improvement. The adoption of these systems in actual healthcare centers has been limited due to the lack of reasoning behind their decisions. This black box nature along with upcoming regulations for transparency and privacy exacerbates the ethico-legal challenges faced by deep learning systems. The attribution methods are a way to explain the decisions of a deep learning model by generating a heatmap of the features which have the most contribution to the model's decision. These are generally compared in quantitative terms for standard machine learning datasets. However, the ability of these methods to generalize to specific data distributions such as retinal OCT has not been thoroughly evaluated. In this thesis, multiple attribution methods to explain the decisions of deep learning models for retinal diagnosis are compared. It is evaluated if the methods considered the best for explainability outperform the methods with a relatively simpler theoretical background. A review of current deep learning models for retinal diagnosis and the state-of-the-art explainability methods for medical diagnosis is provided. A commonly used deep learning model is trained on a large public dataset of OCT images and the attributions are generated using various methods. A quantitative and qualitative comparison of these approaches is done using several performance metrics and a large panel of experienced retina specialists. The initial quantitative metrics include the runtime of the method, RMSE, and Spearman's rank correlation for a single instance of the model. Later, two stronger metrics - robustness and sensitivity are presented. These evaluate the consistency amongst different instances of the same model and the ability to highlight the features with the most effect on the model output respectively. Similarly, the initial qualitative analysis involves the comparison between the heatmaps and a clinician's markings in terms of cosine similarity. Next, a panel of 14 clinicians rated the heatmaps of each method. Their subjective feedback, reasons for preference, and general feedback about using such a system are also documented. It is concluded that the explainability methods can make the decision process of deep learning models more transparent and the choice of the method should account for the preference of the domain experts. There is a high degree of acceptance from the clinicians surveyed for using such systems. The future directions regarding system improvements and enhancements are also discussed