A Methodical Review of Iridology-Based Computer-Aided Organ Status Assessment Techniques

Abstract

The pseudoscience known as iridology makes the unsubstantiated claim that it can identify medical disorders by examining the iris, the colored portion of the eye. Iridology does not provide a reliable means of diagnosis, and there is no scientific proof to back up its claims. To find patterns that are connected to particular medical conditions, computerized iris analysis software may need to examine thousands of iris images. A method of iridology known as Computer-Aided Iridology (CAI) uses software to study the iris. CAI still is not a medically accepted diagnostic technique and is not any more trustworthy than conventional iridology. Applying technology in medical science had a great impact on diagnosing diseases. Decision making is the most critical task in computer-aided applications. Computer vision and deep learning make this task more accurate and are widely used in many applications, mainly in diagnosing diseases. The methodologies, data acquisition source, and volume of data used for both training and testing in the pre-diagnosis of human organs utilizing iris patterns are thoroughly studied. Understanding its limitations allows researchers to concentrate on creating and evaluating improvements in technology that could boost its accuracy and usefulness. Iridology has been considered as having no use for years and becomes effective when combined with technology. This study includes various technical factors used in iridology for the pre-diagnosing of diseases. Recognizing the limitations of iridology allows healthcare providers to avoid errors in diagnosis and prevent individuals from undergoing redundant procedures or therapies based solely on iridology assessments.

Keywords

• iridology
• organs
• human body
• diagnosis
• normalization
• segmentation