Interleaved Optical Coherence Tomography: Clinical and Laboratory Biomarkers in Patients with Diabetic Macular Edema

Optical Coherence Tomography Measurements in Type 1 Diabetic Subjects with Low and Moderate Daily Physical Activity

Background: Physical activity is nowadays recognized as a protective factor against cardiovascular conditions, being cost-effective and easy to implement. Through its positive effects on hemodynamic and oxidative stress, different intensities in daily physical activity could influence diabetic macular edema (DME) in type 1 Diabetes Mellitus (DM). Methods: With the help of a spectral domain optical coherence tomography (OCT) device, we studied the macular thickness and ETDRS map parameters in type 1 DM patients who were classified into two groups: low and moderate intensity routine physical activity status, using the international physical activity questionnaire (IPAQ). All subjects received comparable anti-VEGF treatment. Results: Having a long disease evolution, patients with type 1 DM (T1DM) with moderate physical activity displayed better OCT measurements in specific retinal sectors than their counterparts with low physical activity. Variables such as age and body mass index (BMI) can influence the level of physical activity in T1DM patients. Conclusions: This study showed a lower prevalence of DME in T1DM subjects with moderate physical activity levels, revealing lower values for ETDRS OCT parameters in specific retinal sectors. The macular volumes (mm3) were significantly lower in the right eye for this group of subjects. Abbreviations: BMI = body mass index, CMT = central macular thickness, DM = diabetes mellitus, DME = diabetic macular edema, DR = diabetic retinopathy, FT = foveal thickness, II = inferior inner thickness, IO = inferior outer thickness, IPAQ = international physical activity questionnaire, LE = left eye, OCT = optical coherence tomography, MMT = maximal macular thickness, mMT = minimal macular thickness, MV = macular volume, NI = nasal inner thickness, NO = nasal outer thickness, QoL = quality of life, RE = right eye, SI = superior inner thickness, SO = superior outer thickness, T1DM = type 1 diabetes mellitus, T2DM = type 2 diabetes mellitus, TI = temporal inner thickness, TO = temporal outer thickness.

Attitude and perception toward clinical trials in India among patients and patient bystanders visiting the Indian Ophthalmology Clinical Trial Network: A multi-centric, cross-sectional survey

Purpose

Advances in patient treatment depend heavily on clinical trials (CTs). Patient volunteers for CT are tougher to recruit and retain. In order to administer CTs effectively, it is necessary to comprehend how the public views and perceives participating in them. The study assessed the perception and attitudes of patients and bystanders toward CTs in India.

Methods

This was a multi-centric, cross-sectional study among patients and bystanders using a questionnaire that consisted of socio-demographic characteristics and questions on knowledge and attitude toward participation in CTs. The minimum sample size estimated for the survey was 750.

Results

A total of 1260 respondents (patients and bystanders) had participated in the survey. 42% of total respondents were aware about CTs. Unawareness regarding (i) voluntary power of an individual to participate in a CT (only 47%), (ii) entitled benefits of free treatment and medical insurance during enrolment in a CT (only 47%), and (iii) only 16% of the respondents knew involvement of human subjects in CT were the major highlights among those who had prior knowledge about CTs. Education was the most pervasive factor in shaping positive perception among the respondents. Occupation was another ubiquitous factor in shaping their perception regarding CTs.

Conclusion

The majority of respondents were not aware of CTs. The major concerns observed were time consumption and harmful nature of CTs that influenced their unwillingness to participate in CTs. Initiatives such as awareness campaigns and survey assessments that would result in scientifically effective health service policies would be strategic methods to enhance CT participation.

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Diabetic Macular Edema (DME) represents a significant visual impairment among individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially resulting in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that produces high-resolution retinal images, plays a vital role in the clinical assessment of this condition. Physicians typically rely on OCT B-Scan images to evaluate DME severity. However, manual interpretation of these images is susceptible to errors, which can lead to detrimental consequences, such as misdiagnosis and improper treatment strategies. Hence, there is a critical need for more reliable diagnostic methods. This study aims to address this gap by proposing an automated model based on Generative Adversarial Networks (GANs) to generate OCT B-Scan images of DME. The model synthesizes images from patients' baseline OCT B-Scan images, which could potentially enhance the robustness of DME detection systems. We employ five distinct GANs in this study: Deep Convolutional GAN, Conditional GAN, CycleGAN, StyleGAN2, and StyleGAN3, drawing comparisons across their performance. Subsequently, the hyperparameters of the best-performing GAN are fine-tuned using Particle Swarm Optimization (PSO) to produce more realistic OCT images. This comparative analysis not only serves to improve the detection of DME severity using OCT images but also provides insights into the appropriate choice of GANs for the effective generation of realistic OCT images from the baseline OCT datasets.

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.