An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone

Behnam Askarian; Fatemehsadat Tabei; Amin Askarian; Jo Woon Chong

doi:10.1117/12.2293765

An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.

Original language	English
Title of host publication	Medical Imaging 2018
Subtitle of host publication	Computer-Aided Diagnosis
Editors	Kensaku Mori, Nicholas Petrick
Publisher	SPIE
ISBN (Electronic)	9781510616394
DOIs	https://doi.org/10.1117/12.2293765
State	Published - 2018
Event	Medical Imaging 2018: Computer-Aided Diagnosis - Houston, United States Duration: Feb 12 2018 → Feb 15 2018

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	10575
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2018: Computer-Aided Diagnosis
Country	United States
City	Houston
Period	02/12/18 → 02/15/18

Keywords

Cornea
Corneal topography
Image processing
Keratoconus
Smartphone

Access to Document

10.1117/12.2293765

Link to publication in Scopus

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Cite this

@inproceedings{f06eb642cfcd476681a2a74872e8f21e,

title = "An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone",

abstract = "Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.",

keywords = "Cornea, Corneal topography, Image processing, Keratoconus, Smartphone",

author = "Behnam Askarian and Fatemehsadat Tabei and Amin Askarian and Chong, {Jo Woon}",

year = "2018",

doi = "10.1117/12.2293765",

language = "English",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Kensaku Mori and Nicholas Petrick",

booktitle = "Medical Imaging 2018",

}

Askarian, B, Tabei, F, Askarian, A & Chong, JW 2018, An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone. in K Mori & N Petrick (eds), Medical Imaging 2018: Computer-Aided Diagnosis., 1057512, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10575, SPIE, Medical Imaging 2018: Computer-Aided Diagnosis, Houston, United States, 02/12/18. https://doi.org/10.1117/12.2293765

An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone. / Askarian, Behnam; Tabei, Fatemehsadat; Askarian, Amin; Chong, Jo Woon.

Medical Imaging 2018: Computer-Aided Diagnosis. ed. / Kensaku Mori; Nicholas Petrick. SPIE, 2018. 1057512 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10575).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone

AU - Askarian, Behnam

AU - Tabei, Fatemehsadat

AU - Askarian, Amin

AU - Chong, Jo Woon

PY - 2018

Y1 - 2018

N2 - Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.

AB - Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.

KW - Cornea

KW - Corneal topography

KW - Image processing

KW - Keratoconus

KW - Smartphone

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AN - SCOPUS:85046274777

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2018

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