On validating YOLOv8 as a bioinformatics tool capable of identifying tension rings in human irises

Iridology is an alternative diagnostic method that assesses overall health by examining the iris. Proponents claim that each organ in the body is linked to the iris via nerve connections, with dysfunctions manifesting as distinctive markings on the iris. While iridology has been relegated to pseudoscience in Western medicine, it has been of particular interest in Eastern health practices for over a millennia. With advancements in deep learning, there is renewed interest in iridology as a health assessment tool. Deep learning algorithms can analyze iris images objectively, potentially validating iridology's claims. This study explores using YOLOv8, a deep learning model, to detect tension rings in iris images as an indicator of anxiety. By training the model on a labeled dataset of iris images, we aim to develop a non-invasive, cost-effective tool for anxiety assessment. Results show that YOLOv8 can reliably detect tension rings, suggesting its potential as a diagnostic aid.