Author Archive: daprof

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 [...]

Research Objectives: 

This review is divided into four sections, reflecting the three main components of human eye topography: the pupil, collarette, and iris. The fourth section focuses on inherited and acquired traits related to cardiovascular weakness in constitutional classifications. The human eye is connected to the autonomic nervous system (ANS), which controls involuntary bodily functions, through both the sympathetic and parasympathetic branches. This study investigates pupillary deformations, protrusions, collarette and iris anomalies, and their potential connections to cardiovascular pathologies, as explored by several past authors, researchers, and investigators. The balance between the sympathetic and parasympathetic systems is crucial for regulating heart rate and other physiological functions. Any disruption in this balance can lead to cardiac issues.

Full Abstract: https://www.researchgate.net/publication/387271440_Iridodiagnostics_and_Cardiovascular_Diseases_Review_Part_1_The_Pupils

ABSTRACT

Iridology is a fairly new scientific discipline interested in the study of the beauty-contact areas of the body that are termed the iris. It can even assess problems with a variety of biological activities. Since the nervous system and brain connect the body’s tissues and organs, the condition of each organ may be immediately revealed through the iris, and in this way, the cardia characteristics were retrieved through eye and feature expression and image pre-processing methods. In the system proposed, Explainable AI is now combined with machine learning techniques like K nearest neighbor and the random forest algorithm. If they unite, they will feel even stronger, more efficient, and self-reliant. With this component, estimating the probability of coronary artery disease becomes possible.

CONCLUSION AND FUTURE WORK

To sum up, the use of random forest and K-nearest neighbors (KNN) alongside the iris dataset offers valuable insights into predicting cardiac disease. Both models demonstrate strengths in [...]

Abstract Our primary research objective was to determine if the irises reliably show signs indicating the presence of Diabetes Mellitus. We examined symptomatic patients who had already been diagnosed with the disease by common laboratory and clinical methods. In our study, we employed slitlamp-enhanced Iridology and slitlamp photography, as used successfully for many years in Russia as an adjunct to other means of diagnostic and pathology evaluation. We were able to discern certain iris patterns indicating the pre-diagnosed disease with a high degree of certainty.

• January 2002

• Thesis for: D. Sc.
• Advisor: S. B. Isabekova

• S. B. Isabekova
• B. S. Kuralbaev
• A. G. Kezdikbaeva
• Leonard Mehlmauer

Researchgate URL: https://www.researchgate.net/publication/230882307_Diabetes_Prevention_and_Iris_Data

Download Abstract as PDF: https://iridology-research.com/pdf/DiabetesPreventionandIrisData.pdf