Work

An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology

Year: 2025

Type: article

Abstract: Keratoconus (KC) is a progressive eye disease and a major cause of vision impairment and blindness worldwide. Early diagnosis is crucial for effective management, yet conventional diagnostic methods rely on expensive and bulky imaging devices, limiting accessibility, especially in resource-constrained settings. This paper introduces a novel smartphone-based approach for the early detection of KC, leveraging screen-projected Placido disc patterns and an advanced image processing framework. Unlike traditional corneal topographers, our method utilizes a unique Placido disc projection technique and a machine learning-based classification model to analyze corneal irregularities with high precision. With a sensitivity of 96.08%, specificity of 97.96%, and overall accuracy of 97% on our dataset, the proposed system demonstrates exceptional diagnostic reliability. By transforming a standard smartphone into an effective screening tool, this innovation provides an affordable, portable, and user-friendly solution for early KC detection, bridging the gap in eye care accessibility and reducing the global burden of undiagnosed keratoconus.

Source: Sensors

DOI: 10.3390/s25051316

Authors: Behnam Askarian, Amin Askarian, Fatemehsadat Tabei, Jo Woon Chong

Institutions: West Texas A&M University, Texas Tech University

Language: English

Topic: Corneal surgery and disorders

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137