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Tytuł pozycji:

Automated Keratoconus Detection by 3D Corneal Images Reconstruction.

Tytuł :
Automated Keratoconus Detection by 3D Corneal Images Reconstruction.
Autorzy :
Mahmoud HAH; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11351, Saudi Arabia.
Mengash HA; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11351, Saudi Arabia.
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Źródło :
Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Mar 26; Vol. 21 (7). Date of Electronic Publication: 2021 Mar 26.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Original Publication: Basel, Switzerland : MDPI, c2000-
MeSH Terms :
Keratoconus*/diagnostic imaging
Cornea/diagnostic imaging ; Corneal Topography ; Humans ; Machine Learning
References :
Am J Ophthalmol. 2014 Jul;158(1):32-40.e2. (PMID: 24709808)
Invest Ophthalmol Vis Sci. 1997 Oct;38(11):2290-9. (PMID: 9344352)
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5302-5. (PMID: 23367126)
Clin Optom (Auckl). 2016 Feb 24;8:13-21. (PMID: 30214345)
Eye (Lond). 2015 Jul;29(7):843-59. (PMID: 25931166)
J Biomed Inform. 2002 Jun;35(3):151-9. (PMID: 12669978)
Image Vis Comput. 2017 Feb;58:13-24. (PMID: 29731533)
Comput Methods Programs Biomed. 2014 Aug;116(1):39-47. (PMID: 24857632)
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:747-50. (PMID: 18002064)
Grant Information :
through the Fast-track Research Funding Program. This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University
Contributed Indexing :
Keywords: 3D eye construction; cornea; depth calculation; keratoconus detection; machine learning
Entry Date(s) :
Date Created: 20210403 Date Completed: 20210427 Latest Revision: 20210427
Update Code :
20210428
PubMed Central ID :
PMC8036293
DOI :
10.3390/s21072326
PMID :
33810578
Czasopismo naukowe
This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts.

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