Author Archive:

Abstract: Hypertension is classified as a non-communicable disease and is commonly known as “The Silent Killer” due to its minimal or absent symptoms. This condition is characterized by blood pressure exceeding the normal range. Commonly used methods to determine hypertension conditions currently involve examinations at healthcare facilities or self-monitoring using digital blood pressure monitors. However, both methods have limitations, such as requiring time to visit healthcare centers and the relatively high cost of devices for some segments of the population. As an alternative, the iridology approach offers a more accessible and affordable method for classifying hypertension conditions through visual analysis of iris images. One of the indicators of hypertension in iridology is the appearance of a gray or cloudy ring around the iris, known as the sodium ring. This ring indicates a metabolic imbalance caused by excessive salt consumption. However, further medical examination is necessary to prevent potential complications. This study utilized 300 iris images [...]

Abstract:

Obesity, characterized by excessive fat accumulation, arises from an imbalance between energy consumption and expenditure. It is a major risk factor for cardiovascular diseases, type 2 diabetes, and metabolic syndrome. In this retrospective case-control study, we examined the relationship between obesity and iris features frequently utilized in iridology, a practice that studies iris features to assess health conditions. We analyzed the correlation between iris markers and obesity obesity-related test results in 197 adults (99 obese, 98 normal-weight) using anonymized medical records and iris images. Obesity was defined as a BMI of 25 or higher. Trained practitioners examined iris images for markers such as lacunae around the autonomic nerve wreath (ANW), toxic spots around ANW, protrusion around ANW, and lacunae in the cardiac region. They evaluated each marker using a 0-2 grading system. Obese individuals showed more pronounced features, including lacunae and toxic spots around ANW. Normal-weighted individuals showed thicker and more protruded ANW (suggesting [...]

Abstract:

Diabetes is a chronic disease characterised by disorders in the production or use of insulin in the body. Insulin is a hormone produced by pancreatic beta cells, essential in regulating blood glucose levels. One way to detect diabetes is to use iridology. Iridology is a field of study that can detect bodily abnormalities through the eye’s iris. Iridologists analyse the characteristics of the iris using a biomicroscope. This causes the results of analysis and diagnosis of disease to be subjective and take a long time. This research aims to build a program that is capable of detecting diabetes from iris images using k-Nearest Neighbor (k-NN) by comparing two texture feature extraction methods, namely Gray Level Co-Occurance Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). For GLCM, the features used are contrast, dissimilarity, homogeneity, energy, and correlation, while for GLRLM, the features used are short-run emphasis (SRE), long-run emphasis (LRE), gray-level non-uniformity (GLN), run [...]

Abstract:

The extensiveness of stress-related physiological and other psychological conditions such as anxiety and depression requires new diagnostic approaches. This research will aim at finding out the efficiency of using iris analysis in diagnosing such conditions as a complementary to the current methods that are either invasive or rely on the patient’s perception. Based on the interviews with ophthalmologists and psychologists, it was discovered that the movements of the iris and pupil controlled by the autonomic nervous system may contain information about stress and mental health disorders. Ophthalmologists noted that iris could be used as a bio-marker while clinical psychologists noted that speech based assessment was still the common practice in the assessment of mental health. Although iris analysis cannot be used as the sole diagnostic tool, this research shows that it could be a valuable adjunct when combined with standard approaches. The proposed system will employ high quality of iris image capture and the [...]

Abstract: Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such [...]

Abstract

This cross-sectional study aims to detect Diabetic Retinopathy (DR) in patients who have had retinal scans and ophthalmological exams. The research makes use of tailored retinal images together with the OPF (Optimum-Path Forest) and RBM (Restricted Boltzmann Machine) models to categorize images according to the presence or absence of DR. In this work, features were extracted from the retinal images using both the RBM and OPF models. In particular, after a thorough system training phase, RBM was able to extract between 500 and 1000 features from the images. The study included fifteen distinct trial series, each with thirty cycles of repetition. The research comprised 122 eyes, or 73 diabetic patients, with a gender distribution that was reasonably balanced and an average age of 59.7 years. Remarkably, the RBM-1000 model stood out as the top performer, with the highest overall accuracy of 89.47% in diagnosis. In terms of specificity, the RBM-1000 and OPF-1000 models surpassed [...]

Abstract: Chronic renal disease is the term used to describe the progressive loss of kidney function. In the early phases of chronic renal illness, there might not be many symptoms. Chronic renal disease may not be apparent until your kidney function is seriously impaired. The iris is a colorful muscle located inside the eye. Because it contains a range of distinct textural information that is difficult to change or disturb, it is the best feature to predict health difficulties. Iridological science attributes iris apparitions to the body’s tissue weaknesses. It exhibits toxicity to organs or tissues, inflammation, or weakening. Thus, iridology could serve as a stand-in for early chronic kidney disease diagnosis. This method can reveal the state of the organ in the body before symptoms of illness appear. Image processing techniques in diagnosing diseases are accurate and could be used in the biomedical field. Diagnosis using Iris image analysis is one of the best [...]

Abstract Diabetes is a condition that commonly affects human beings. Diabetes can be diagnosed using a variety of approaches, one of which is blood pressure monitoring. However, this method is inefficient because it requires a blood sample and might be time-consuming. Iridology is a health assessment technique that examines the iris of the eye. As a result, many diabetes cases remain undiagnosed in their early stages. This paper presents a diabetes prediction system through the analysis of iris images based on machine learning techniques. Initially, iris images were collected from the diabetes iridology database and IIT Delhi iris dataset. Next, pre-processing is performed by Gaussian Amended Wiener Filter (GAWF) to reduce noise and enhance contrast in the images. Then, an improved Pyramid Scene Parsing Network (PSPNet) model is employed to segment the Inner Iris Zone from the iris images. After that, Depthwise Separable Convolutional Dense Net (DSC-DenseNet) is utilized to extract relevant features from these [...]

Abstract

The increasing demand for non-invasive, rapid, and cost-effective disease diagnostics has driven advancements in integrating Iridology with computer vision and Artificial Intelligence (AI). This review examines research conducted from 2009 to 2024 on iris-based disease detection. The key findings showed the significant role of Machine Learning (ML) and Deep Learning (DL) in enhancing diagnostic accuracy and efficiency. Iridology-based intelligent systems show great promise for early detection of hidden diseases and organ dysfunctions, offering transformative potential for healthcare.

Conclusion

Research in complementary medical diagnostics, particularly in iris-based disease detection, has demonstrated the potential of leveraging image processing, computer vision, and artificial intelligence. Recent trends emphasize the increasing adoption of machine learning and deep learning techniques, often used in combination, due to their effectiveness in analyzing medical images. These approaches not only increase diagnostic accuracy but also reduce training time and enhance interface responsiveness.Preprocessing of iris images prior to AI-based classification is a critical step in [...]