Iridology Research

Evaluation of autonomic involvement in Parkinson’s disease using pupillometry

Tue, 27 Jan 2026 00:49:10 +0000

Abstract

Introduction

The pupillary light reflex (PLR) indicates the function of the autonomic nervous system, which causes the pupil to constrict and dilate. Evaluation of the PLR determines the parasympathetic and sympathetic balance. We aimed to demonstrate autonomic changes by pupillometry in Parkinson’d disease (PD) and to investigate the relationship between the changes and motor stage and levodopa equivalent dose (LEDD).

Method

Static pupillometry measurement was performed at scotopic, mesopic and photopic settings. Dynamic pupillometry measurement was performed at 500 lux illumination and pupil diameter was recorded. Static and dynamic pupillometry parameters of Parkinson’s disease patients and healthy control group of similar age and gender were compared. The relationship between Unified Parkinson’s Disease Rating Scale (UPDRS)-motor, modified Hoehn and Yahr (mHYE) and levodopa equivalent daily dose (LEDD) and pupillometry parameters were analysed.

Results

In static pupillometry, mesopic pupil diameter was significantly lower in PD patients (P = 0.04). In dynamic pupillometry, pupil diameter was lower and mean pupil dilation rate was lower in the PD group at 18 seconds. There was a significant negative correlation between mean pupil dilatation velocity and mHYE, UPDRS-motor and LEDD. The mean pupil dilatation velocity was statistically significantly lower in patients not receiving dopaminergic treatment.

Conclusion

Changes in pupillometry values in PD have emphasized that the autonomic nervous system is affected and the parasympathetic nervous system was found to be correlated with the motor involvement of the disease.

Full Abstract: https://link.springer.com/article/10.1007/s10072-025-08182-y?

Authors:

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Pupillometric evaluation and analysis of light reflex in healthy subjects as a tool to study autonomic nervous system changes with aging

Tue, 27 Jan 2026 00:20:56 +0000

Abstract

To assess the relationships between aging and autonomic control of pupillary functions, TV-pupillometry and light reflex evaluation were performed in 52 healthy volunteers in the age range 15-75 years, grouped into four age classes (group 1: 15-29 years, Group 2: 30-44 years, Group 3: 45-59 years, Group 4: 60-75 years). Baseline light pupil diameter was found to be age-dependent, together with light reflex contraction velocity, which presented a linear correlation with age. Light reflex amplitude and half-redilatation velocity were reduced in older subjects, but presented only a weak linear correlation with age, while latency, contraction time and half-redilatation time percent of secondary dilatation and redilatation at 5 seconds did not show significant changes with age. These results confirm that there are important age-dependent changes in the mechanisms involved in pupillary autonomic functions, regarding both sympathetic and parasympathetic components. These changes appear to be easily detectable by making use of a sensitive and non-invasive technique such as TV-pupillometry.

G Pozzessere¹, E Valle, P Rossi, B Petrucci, A Ambrosini, M D’Alessio, F Pierelli, P Giacomini

Affiliations

PMID: 8695677
DOI: 10.1007/BF03340116

Reference: https://pubmed.ncbi.nlm.nih.gov/8695677/

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Diagnostic utility of pupillometry in detecting autonomic dysfunction in migraine disorders

Tue, 27 Jan 2026 00:02:16 +0000

Abstract

Purpose: This study aims to comprehensively investigate autonomic nervous system dysfunction in migraine patients using automated pupillometry, a non-invasive and objective assessment method.
Materials and Methods: This study was designed as a prospective, non-randomized clinical trial involving a total of 128 participants aged 18–59 years (64 migraine patients and 64 healthy controls). All participants underwent a comprehensive ophthalmological examination followed by automated pupillometry testing. Pupillometry measurements were performed under scotopic (dark), mesopic (dim light), and photopic (bright light) conditions, with subsequent recording of dynamic pupil dilation measurements.

Results: The mean pupil dilation velocity in migraine patients (0.11 ± 0.45 mm/s) was found to be significantly slower compared to the healthy control group (0.13 ± 0.46 mm/s). Additionally, a significant difference was observed between the two groups in pupil diameter under mesopic conditions. Analysis of dynamic pupil dilation revealed a significant difference at the 10th second of measurement between Group I (migraine patients; 5.63 ± 0.87 mm) and Group II (healthy controls; 6.05 ± 0.95 mm).
Conclusion: This study demonstrates that migraine patients exhibit slower pupil dilation velocity compared to healthy individuals, indicating autonomic nervous system dysfunction. These findings suggest that automated pupillometry may serve as a valuable, non-invasive, and objective tool for evaluating autonomic functions in migraine patients.

Year 2025, Volume: 50 Issue: 2, 299 – 307, 30.06.2025

Ceren Türkoğlu , Dilek İşcan , Elif Arslan

https://doi.org/10.17826/cumj.1658478

PDF: https://iridology-research.com/pdf/

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Enhanced Diabetes Detection Through CNN-SVM Hybrid Architecture

Mon, 26 Jan 2026 22:25:29 +0000

Abstract:

Corresponding and unconventional medicine approaches have shown capacity for the treatment and diagnosis of chronic illnesses such as diabetes and arthritis. Concurrently, computer image treating approaches for illness detection have emerged as a reliable and quickly expanding sector in biomedical research. The purpose of this suggested model is to use soft computing approaches to assess the investigative validity of iridology, an ancient complementary and substitute medicine practice, for the diagnosis of type II diabetes. Methods: The study included a mixed population of diabetic and non-diabetic participants. Infrared pictures of both eyes were taken. In the iris picture the area of interest (ROI), which matches to the location of the pancreatic organ on the iridology chart, was clipped. CNN and CNN were used to extract features such as accuracy, sensitivity, specificity, difference variance, contrast, homogeneity, correlation, dissimilarity, and entropy. The collection included both a test iris database and real-time iris photographs. The CNN model obtained an extraordinary validation accuracy of 98.7 %, while the SVM model achieved 68.39 %. The CNN model’s performance measures were 9 8. 7 % accuracy, 1 0 0 % recall (sensitivity), and 1 0 0 % F1-score. The false negative and false positive ratios were both 0.0, demonstrating that the CNN model is very reliable and precise.

Authors: Rashmi Badave; Ankita Avthankar; Poonam Sadafal; Rajashree Devidas Thosar; Pravin Rupnar; Vishal Borate

Reference: https://ieeexplore.ieee.org/abstract/document/11211463

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A Case-Control Study to Determine the Correlation Between Obesity and Iris Markers

Mon, 26 Jan 2026 22:22:36 +0000

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 increased sympathetic nervous system activity). These markers correlated with clinical measurements of obesity such as waist and hip circumference, visceral fat area, body fat mass, and percent body fat. Each iris marker tended to correlate with different test items. The results suggest that iridology could be utilized to identify obesity-related detailed pathophysiological factors. Iridology combined with
advanced technologies such as deep learning and artificial intelligence may further improve early detection and personalized treatment approaches for obesity-related health issues, potentially making iridology a useful component in comprehensive obesity management programs.

References:

Ha-Kwon Kim1, Jungeun Choi1, Horyong Yoo1, Seong-Hwan Choi2, Seong-Il Park3,
Hyun-Jung Park3, Miso S. Park1,4,*
1 : Department of Cardiology and Neurology of Korean Medicine, College of Korean Medicine, Daejeon University,
2 : Gao Korean Medicine Clinic, 3 : Dr. Park Seong-Il Clinic,
4 : Clinical Trial Center, Daejeon Korean Medicine Hospital of Daejeon University

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Pupil Flattening as a Diagnostic Marker for Intervertebral Disc Bulge: An Iridology-Based Approach

Mon, 26 Jan 2026 22:02:12 +0000

Abstract

Intervertebral disc bulge (IVDB) is a prevalent spinal condition that significantly affects quality of life, yet current diagnostic methods can be expensive and not always easily accessible. This study explores the potential of pupil flattening as an iridological marker for IVDB. A case-control study was conducted with 60 IVDB patients and 60 healthy controls. High-resolution iris images were analyzed for pupil flattening, revealing a markedly higher prevalence in IVDB patients (88.3 % in the right iris, 81.7 % in the left iris) compared to controls (15 % in the right iris, 10 % in the left iris). ROC analysis demonstrated strong diagnostic accuracy, with AUC values of 0.79 for males (95 % CI: 0.60–0.98) and 0.88 (95 % CI: 0.78–0.99) for females. Sensitivity and specificity were particularly high in females, at 80 % and 97.62 %, respectively. These findings indicate that pupil flattening could serve as a non-invasive, cost-effective diagnostic tool for IVDB, offering a potential complement to existing diagnostic approaches.

Results

The demographic characteristics of the patients are summarized in Table 1. Predominantly, the study population consisted of females (n = 77, 64.2 %). The mean age of the patients was 45 years, with ages ranging from approximately 18–75 years. Iris flattening emerged as a highly prevalent lesion in patients with IVDB, affecting 73.3 % of cases (44 out of 60 patients), as detailed in Table 1. The clinical manifestations showed marked differences between IVDB and control groups.

Authors: Y. Rosy Ayda a, Abinaya Suresh b, M. Alkousar Taz b, MaheshKumar Kuppysamy c.

Reference: https://www.sciencedirect.com/science/article/abs/pii/S2212958825001636

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Iris Diagnosis Using Deep Learning: A Comprehensive Review

Tue, 19 Aug 2025 21:46:13 +0000

Abstract:

Iris diagnosis based on the sophisticated patterns and properties of the human iris has appeared
as a prospective method for use in medicine, particularly in ophthalmology and biometrics. Deep learning
methodologies have gained substantial attention over recent years due to their ability to automate and
enhance iris diagnosis at high accuracy and reliability. This critical analysis discusses the different deep
learning architectures and techniques employed in iris diagnosis, with primary emphasis on convolutional
neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models integrating traditional
image processing techniques with machine learning algorithms. We discuss key challenges encountered
in iris recognition, including changes in lighting, image quality, and occlusions, and how deep learning
technologies counter these challenges. The review also includes different deep learning data sets,
performance measures, and performance analysis of existing models. We also discuss the clinical
application of iris diagnosis, specifically its application in the detection of disease, e.g., glaucoma, diabetic
retinopathy, kidney disease and other eye diseases. We finally offer potential future directions within the
field, including the requirement for more robust models, real-time diagnosis, and compatibility with
wearable technology. This review is intended to offer a comprehensive summary of the present status of
deep learning technologies within iris diagnosis and their potential in future medical advancement.

Authors: Baba Fakruddin Ali B H1, Sai Kiran Sharma2, Shashi kanth Gupta
Assistant professor, Department of CSE, Nagarjuna College of Engineering Bangalore

Full Abstract: https://iridology-research.com/pdf/Mannuscript_Baba_F_Ali.pdf

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Non-Invasive Diagnosis of Type II Diabetes Using Iris- Based Machine Learning Models

Tue, 19 Aug 2025 21:38:31 +0000

Abstract – The increasing occurrence of Type II Diabetes Mellitus (T2DM) due to lifestyle changes has required the development
of non-invasive diagnostic methods. This study explores the potential of iridology, another diagnostic approach, joined with
machine learning (ML) algorithms to detect T2DM based on a precise Region of Interest (ROI) in the right iris. This work
introduces two ML-based classification methods. The first method employs multiple ML models with changing K-fold cross-
validation (ranging from 2 to 20 folds), achieving a maximum classification accuracy of 78.5% at 5-fold using Support Vector
Machine (SVM) and Binary Generalized Linear Model (GLM) Logistic Regression. The second method employs Principal
Component Analysis (PCA) to enhance feature selection, improving accuracy to 82.2% by training predictions from two initial
classifiers-Coarse Decision Tree (74.8% at 14-fold, PCA variance 97%) and Linear Discriminant Analysis (77.6% at 9-fold,
PCA variance 100%)-before refining classification with a Binary GLM Logistic Regression model. The proposed approach offers
a promising, non-invasive alternative for early diabetes detection using iris analysis and Artificial Intelligence (AI)

Full Abstract: https://iridology-research.com/pdf/Non-Invasive_Diagnosis_of_Type_II_Diabetes_Using_I.pdf

Authors: Alaa Abdulkareem Ahmed, Mohammad Tariq Yaseen
Department of Electrical Engineering, College of Engineering, University of Mosul, Iraq.

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Implementation of EfficientNet Architecture on Android Platform for Classification of Hypertension Disease Based on Iris Image

Tue, 19 Aug 2025 21:32:36 +0000

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 collected from Prof. Dr. Chairuddin P. Lubis Hospital, USU, and the general public in Padang Bulan Subdistrict, which were evenly divided into three categories comprising normal, prehypertension, and hypertension. Subsequently, the data were divided into training (70%), validation (20%), and testing (10%) subsets. The pre-processing steps included labeling, cropping, resizing, and augmentation. The EfficientNet architecture was chosen for its effectiveness in extracting visual features through compound scaling across depth, width, and resolution. The results confirmed the model’s ability to accurately categorize iris images into three classes, reaching a test accuracy of 93%. This performance was optimized by tuning the hyperparameters with 30 epochs and batch size 32. The developed system has the potential to serve as an alternative solution for hypertension classification and aids in facilitating early detection and intervention.

Author: Setiayu, Ade Bunga Dwi

Reference: https://repositori.usu.ac.id/handle/123456789/106185

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A Case-Control Study to Determine the Correlation Between Obesity and Iris Markers

Tue, 19 Aug 2025 21:20:48 +0000

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 increased sympathetic nervous system activity). These markers correlated with clinical measurements of obesity such as waist and hip circumference, visceral fat area, body fat mass, and percent body fat. Each iris marker tended to correlate with different test items. The results suggest that iridology could be utilized to identify obesity-related detailed pathophysiological factors. Iridology combined with advanced technologies such as deep learning and artificial intelligence may further improve early detection and personalized treatment approaches for obesity-related health issues, potentially making iridology a useful component in comprehensive obesity management programs.

Authors: Authors Kim Ha-kwon Choi Jeong-eun Yoo Ho-ryong Choi Seong-hwan Park Seong-il Park Hyeon-jeong Park Mi-so

Korean Society of Oriental Medical Pathology Journal of Oriental Physiology and Pathology

Reference: https://scholar.kyobobook.co.kr/article/detail/4040070853964

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