An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology
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-constrain...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1316 |
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| author | Behnam Askarian Amin Askarian Fatemehsadat Tabei Jo Woon Chong |
| author_facet | Behnam Askarian Amin Askarian Fatemehsadat Tabei Jo Woon Chong |
| author_sort | Behnam Askarian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a3e07802ad3148deb716ef3396dfbb7c |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a3e07802ad3148deb716ef3396dfbb7c2025-08-20T02:52:45ZengMDPI AGSensors1424-82202025-02-01255131610.3390/s25051316An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone TechnologyBehnam Askarian0Amin Askarian1Fatemehsadat Tabei2Jo Woon Chong3College of Engineering, West Texas A&M University, Canyon, TX 79016, USAAskarian Clinic, Shiraz 71877-75778, IranCollege of Engineering, West Texas A&M University, Canyon, TX 79016, USADepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USAKeratoconus (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.https://www.mdpi.com/1424-8220/25/5/1316keratoconus (KC)corneal topographysmartphonePlacido discsupport vector machine (SVM) |
| spellingShingle | Behnam Askarian Amin Askarian Fatemehsadat Tabei Jo Woon Chong An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology Sensors keratoconus (KC) corneal topography smartphone Placido disc support vector machine (SVM) |
| title | An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology |
| title_full | An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology |
| title_fullStr | An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology |
| title_full_unstemmed | An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology |
| title_short | An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology |
| title_sort | iot enabled mhealth sensing approach for remote detection of keratoconus using smartphone technology |
| topic | keratoconus (KC) corneal topography smartphone Placido disc support vector machine (SVM) |
| url | https://www.mdpi.com/1424-8220/25/5/1316 |
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