Reliable feature selection for automated angle closure glaucoma mechanism detection

Assessment of angle closure disease in the age of artificial intelligence: A review

Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.

A multistart tabu search-based method for feature selection in medical applications

In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods.

RPE layer detection and baseline estimation using statistical methods and randomization for classification of AMD from retinal OCT

BACKGROUND AND OBJECTIVE

Age-related macular degeneration (AMD) is a condition of the eye that affects the aged people. Optical coherence tomography (OCT) is a diagnostic tool capable of analyzing and identifying the disease affected retinal layers with high resolution. The objective of this work is to extract the retinal pigment epithelium (RPE) layer and the baseline (natural eye curvature, particular to every patient) from retinal spectral-domain OCT (SD-OCT) images. It uses them to find the height of drusen (abnormalities) in the RPE layer and classify it as AMD or normal.

METHODS

In the proposed work, the contrast enhancement based adaptive denoising technique is used for speckle elimination. Pixel grouping and iterative elimination based on the knowledge of typical layer intensities and positions are used to obtain the RPE layer. Using this estimate, randomization techniques are employed, followed by polynomial fitting and drusen removal to arrive at a baseline estimate. The classification is based on the drusen height obtained by taking the difference between the RPE and baseline levels. We have used a patient, wise classification approach where a patient is classified diseased if more than a threshold number of patient images have drusen of more than a certain height. Since all slices of an affected patient will not show drusen, we are justified in adopting this technique.

RESULTS

The proposed method is tested on a public data set of 2130 images/slices, which belonged to 30 patient volumes (15 AMD and 15 Normal) and achieved an overall accuracy of 96.66%, with no false positives. In comparison with existing works, the proposed method achieved higher overall accuracy and a better baseline estimate.

CONCLUSIONS

The proposed work focuses on AMD/normal classification using a statistical approach. It does not require any training. The proposed method modifies the motion restoration paradigm to obtain an application-specific denoising algorithm. The existing RPE detection algorithm is modified significantly to make it robust and applicable even to images where the RPE is not very evident/there is a significant amount of perforations (drusen). The baseline estimation algorithm employs a powerful combination of randomization, iterative polynomial fitting, and pixel elimination in contrast to mere fitting techniques. The main highlight of this work is, it achieved an exact estimation of the baseline in the retinal image compared to the existing methods.

A review of feature selection methods in medical applications

Feature selection is a preprocessing technique that identifies the key features of a given problem. It has traditionally been applied in a wide range of problems that include biological data processing, finance, and intrusion detection systems. In particular, feature selection has been successfully used in medical applications, where it can not only reduce dimensionality but also help us understand the causes of a disease. We describe some basic concepts related to medical applications and provide some necessary background information on feature selection. We review the most recent feature selection methods developed for and applied in medical problems, covering prolific research fields such as medical imaging, biomedical signal processing, and DNA microarray data analysis. A case study of two medical applications that includes actual patient data is used to demonstrate the suitability of applying feature selection methods in medical problems and to illustrate how these methods work in real-world scenarios.

Anterior Segment Imaging for Angle Closure

PURPOSE

To summarize the role of anterior segment imaging (AS-imaging) in angle closure diagnosis and management, and the possible advantages over the current standard of gonioscopy.

DESIGN

Literature review and perspective.

METHODS

Review of the pertinent publications with interpretation and perspective in relation to the use of AS-imaging in angle closure assessment focusing on anterior segment optical coherence tomography and ultrasound biomicroscopy.

RESULTS

Several limitations have been encountered with the reference standard of gonioscopy for angle assessment. AS-imaging has been shown to have performance in angle closure detection compared to gonioscopy. Also, imaging has greater reproducibility and serves as better documentation for long-term follow-up than conventional gonioscopy. The qualitative and quantitative information obtained from AS-imaging enables better understanding of the underlying mechanisms of angle closure and provides useful parameters for risk assessment and possible prediction of the response to laser and surgical intervention. The latest technologies-including 3-dimensional imaging-have allowed for the assessment of the angle that simulates the gonioscopic view. These advantages suggest that AS-imaging has a potential to be a reference standard for the diagnosis and monitoring of angle closure disease in the future.

CONCLUSIONS

Although gonioscopy remains the primary method of angle assessment, AS-imaging has an increasing role in angle closure screening and management. The test should be integrated into clinical practice as an adjunctive tool for angle assessment. It is arguable that AS-imaging should be considered first-line screening for patients at risk for angle closure.

Qualitative evaluation of anterior segment in angle closure disease using anterior segment optical coherence tomography

PURPOSE

To evaluate different mechanisms of primary angle closure (PAC) and to quantify anterior chamber (AC) parameters in different subtypes of angle closure disease using anterior segment optical coherence tomography (AS-OCT).

METHODS

In this prospective study, 115 eyes of 115 patients with angle closure disease were included and categorized into three groups: 1) fellow eyes of acute angle closure (AAC; 40 eyes); 2) primary angle closure glaucoma (PACG; 39 eyes); and 3) primary angle closure suspect (PACS; 36 eyes). Complete ophthalmic examination including gonioscopy, A-scan biometry, and AS-OCT were performed. Based on the AS-OCT images, 4 mechanisms of PAC including pupillary block, plateau iris configuration, thick peripheral iris roll (PIR), and exaggerated lens vault were evaluated. Angle, AC, and lens parameter variables were also evaluated among the three subtypes.

RESULTS

There was a statistically significant difference in the mechanism of angle closure among the three groups (p = 0.03). While the majority of fellow eyes of AAC and of PACS eyes had pupillary block mechanism (77.5% and 75%, respectively), only 48.7% of PACG eyes had dominant pupillary block mechanism (p = 0.03). The percentage of exaggerated lens vault and plateau iris mechanisms was higher in PACG eyes (25.5% and 15.4%, respectively). Fellow eyes of AAC had the shallowest AC (p = 0.01), greater iris curvature (p = 0.01), and lens vault (p = 0.02) than PACS and PACG eyes. Iris thickness was not significantly different among the three groups (p = 0.45).

CONCLUSION

Using AS-OCT, we found that there was a statistically significant difference in the underlying PAC mechanisms and quantitative AC parameters among the three subtypes of angle closure disease.

New developments in anterior segment optical coherence tomography for glaucoma

PURPOSE OF REVIEW

The aim of the present review was to summarize the new developments in anterior segment optical coherence tomography (AS-OCT) for glaucoma.

RECENT FINDINGS

Recent years have demonstrated significant advances in the measurement of glaucoma through the use of AS-OCT. Furthermore, a more widespread use of AS-OCT in the clinical study of various glaucomas warrants review, which includes angel assessment, trabecular meshwork and Schlemm's canal assessment, and assessment of the filtering bleb and tube.

SUMMARY

AS-OCT was recently developed and has become a crucial tool in glaucoma clinical practice. AS-OCT is a noncontact imaging device that provides the detailed structure of the anterior part of the eyes. In this review, the author will discuss the various clinical applications of AS-OCT for glaucoma disease, such as angle assessment, trabecular meshwork and Schlemm's canal assessment, or assessment of the filtering bleb and tube.