Design of a null-screen for a compact corneal topographer

Campos‐García, Manuel; Aguirre-Aguirre, Daniel; Lechuga-Núñez, José Antonio; Peña-Conzuelo, Andrés

doi:10.1117/12.2526241

Cited by 6 publications

(9 citation statements)

References 7 publications

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“…Recently, low-cost, portable, smartphone-based keratoconus screening and diagnostic solutions have been proposed [1,2,11,15,19,38,45]. Most such solutions [15,19,38] rely on a 3D-printed placido disc attachment, attached to the smartphone's camera to project concentric rings over the surface of the examined cornea. The smartphone camera is used to capture an image of the cornea with the placido ring reflection.…”

Section: Smartphone-based Keratoconus Diagnosticsmentioning

confidence: 99%

“…This setup was evaluated with 11 participants (8 normal and 3 keratoconus), and obtained high false positive rate-3 out of 8 normal were classified as having keratoconus. To increase data points per image, Garcia et al [15] use an array of circular holes, instead of the placido-based design, such that each hole resulted in a circular spot of size 0.006mm radius on the image plane. However, the segmentation and detection of these circular spots in the reflected image is error-prone [5].…”

Section: Smartphone-based Keratoconus Diagnosticsmentioning

confidence: 99%

“…However, the segmentation and detection of these circular spots in the reflected image is error-prone [5]. In spite of the detailed system description, most of these works [15,19,38,45] were not evaluated with any keratoconus patient. In contrast, SmartKC was evaluated with 101 distinct eyes (67 non-keratoconus and 34 keratoconus) in a clinical setting.…”

Section: Smartphone-based Keratoconus Diagnosticsmentioning

confidence: 99%

“…Prior work has proposed low-cost, smartphone-based keratoconus diagnostic solutions [1,15,38]. Most of these solutions leverage a combination of 3D-printed black-and-white concentric rings (called placido rings), a macro lens, and an LED circuitry over a smartphone's camera to capture the reflection of the placido rings (called mires) on the cornea.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of their innovative design solutions, none of them have been satisfactorily evaluated, with the maximum sample size being 11 subjects [1,2,11]. Many require additional hardware [1,2,15,38], such as chin-and head-rest, tripod and external power source, making them less portable. Moroever, these methods are based on machine learning classifiers, and output only 2-class/4-class keratoconus classification without any explanation or interpretability.…”

Section: Introductionmentioning

confidence: 99%

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SmartKC: Smartphone-based Corneal Topographer for Keratoconus Detection

Gairola¹,

Bohra²,

Shaheer³

et al. 2021

Preprint

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Keratoconus is a severe eye disease affecting the cornea (the clear, dome-shaped outer surface of the eye), causing it to become thin and develop a conical bulge. The diagnosis of keratoconus requires sophisticated ophthalmic devices which are non-portable and very expensive. This makes early detection of keratoconus inaccessible to large populations in lowand middle-income countries, making it a leading cause for partial/complete blindness among such populations. We propose SmartKC, a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3D-printed placido's disc attachment, an LED light strip, and an intelligent smartphone app to capture the reflection of the placido rings on the cornea. An image processing pipeline analyzes the corneal image and uses the smartphone's camera parameters, the placido rings' 3D location, the pixel location of the reflected placido rings and the setup's working distance to construct the corneal surface, via the Arc-Step method and Zernike polynomials based surface fitting. In a clinical study with 101 distinct eyes, we found that SmartKC achieves a sensitivity of 87.8% and a specificity of 80.4%. Moreover, the quantitative curvature estimates (sim-K) strongly correlate with a gold-standard medical device (Pearson correlation coefficient = 0.77). Our results indicate that SmartKC has the potential to be used as a keratoconus screening tool under real-world medical settings.CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Consumer health.

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Section: Smartphone-based Keratoconus Diagnosticsmentioning

confidence: 99%

Section: Smartphone-based Keratoconus Diagnosticsmentioning

confidence: 99%

Section: Smartphone-based Keratoconus Diagnosticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SmartKC: Smartphone-based Corneal Topographer for Keratoconus Detection

Gairola¹,

Bohra²,

Shaheer³

et al. 2021

Preprint

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Keratoconus Classifier for Smartphone-based Corneal Topographer

Gairola

Joshi

Balasubramaniam

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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SmartKC

Gairola

Bohra

Shaheer

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

Keratoconus is a severe eye disease affecting the cornea (the clear, dome-shaped outer surface of the eye), causing it to become thin and develop a conical bulge. The diagnosis of keratoconus requires sophisticated ophthalmic devices which are non-portable and very expensive. This makes early detection of keratoconus inaccessible to large populations in low-and middle-income countries, making it a leading cause for partial/complete blindness among such populations. We propose SmartKC, a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3D-printed placido's disc attachment, an LED light strip, and an intelligent smartphone app to capture the reflection of the placido rings on the cornea. An image processing pipeline analyzes the corneal image and uses the smartphone's camera parameters, the placido rings' 3D location, the pixel location of the reflected placido rings and the setup's working distance to construct the corneal surface, via the Arc-Step method and Zernike polynomials based surface fitting. In a clinical study with 101 distinct eyes, we found that SmartKC achieves a sensitivity of 87.8% and a specificity of 80.4%. Moreover, the quantitative curvature estimates (sim-K) strongly correlate with a gold-standard medical device (Pearson correlation coefficient = 0.77). Our results indicate that SmartKC has the potential to be used as a keratoconus screening tool under real-world medical settings.

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Design of a null-screen for a compact corneal topographer

Cited by 6 publications

References 7 publications

SmartKC: Smartphone-based Corneal Topographer for Keratoconus Detection

SmartKC: Smartphone-based Corneal Topographer for Keratoconus Detection

Keratoconus Classifier for Smartphone-based Corneal Topographer

SmartKC

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