Neural network approach to classify infective keratitis

Saini, Jagjit S.; Jain, Arun Kumar; Kumar, Sanjay; Vikal, Siddharth; Pankaj, Sidharath; Singh, Sukhwinder

doi:10.1076/ceyr.27.2.111.15949

Cited by 42 publications

(34 citation statements)

References 15 publications

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“…and slit lamp examination findings (including the elevation and texture of corneal infiltrates). [39][40][41][42] In much the same way that an expert clinician typically gathers information from multiple sources before formulating a diagnosis, predictive models for identifying the underlying cause of infection will likely benefit from accessing this additional contextual information in addition to imaging data. These models may also benefit from adopting a Bayesian decision framework accounting for the pre-test probability of different infectious agents based on local epidemiology, which demonstrates marked geographic variability.…”

Section: Discussionmentioning

confidence: 99%

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks

Redd

Prajna

Srinivasan

et al. 2022

Ophthalmology Science

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Discussionmentioning

confidence: 99%

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks

Redd

Prajna

Srinivasan

et al. 2022

Ophthalmology Science

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“…This is the first study to diagnose FK with corneal photographs using DL-based machine learning techniques. Saini et al used the basic neural network structure to classify infective keratitis 26 . Their input consists of 40 variables on patients’ history and lab data for training and testing.…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach in diagnosing fungal keratitis based on corneal photographs

Kuo

Hsu

Yin

et al. 2020

Sci Rep

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Fungal keratitis (FK) is the most devastating and vision-threatening microbial keratitis, but clinical diagnosis a great challenge. This study aimed to develop and verify a deep learning (DL)-based corneal photograph model for diagnosing FK. Corneal photos of laboratory-confirmed microbial keratitis were consecutively collected from a single referral center. A DL framework with DenseNet architecture was used to automatically recognize FK from the photo. The diagnoses of FK via corneal photograph for comparing DL-based models were made in the Expert and NCS-Oph group through a majority decision of three non-corneal specialty ophthalmologist and three corneal specialists, respectively. The average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was approximately 71, 68, 60, and 78. The sensitivity was higher than that of the NCS-Oph (52%, P < .01), whereas the specificity was lower than that of the NCS-Oph (83%, P < .01). The average accuracy of around 70% was comparable with that of the NCS-Oph. Therefore, the sensitive DL-based diagnostic model is a promising tool for improving first-line medical care at rural area in early identification of FK.

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“…In order to decrease the need of diagnostic test and to simplify the diagnosis of patients with PIK the use of artificial neural networks (ANN) may have a role in the future. As described by Saini et al, 27 the ANN are associative self-learning techniques with the ability to identify multidimensional relationships and perform pattern recognition in nonlineal domains. Saini et al demonstrated that with the use of ANN they were able to correctly identify the category (fungal vs bacterial) in 39 of 44 cases of infectious keratitis.…”

Section: Case Nomentioning

confidence: 99%

What is causing the corneal ulcer? Management strategies for unresponsive corneal ulceration

Amescua

Miller

Alfonso

2011

Eye

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Infectious keratitis represents a significant cause of ocular morbidity in the United States. The work-up and treatment of presumed infectious keratitis (PIK) has changed in the past two decades. The development of newer topical antibiotics has enabled broad-spectrum antibiotic coverage with good tissue penetration. The majority of PIK cases respond well to this strategy. The small numbers of cases that do not respond to the treatment are the cases that offer a diagnostic and therapeutic challenge. This review will describe different algorithms that can be followed for the successful management of patients with difficult or progressive PIK. These algorithms are based on scientific work and on our empirical clinical experience. The review will also present three different clinical cases of PIK that were managed according to the algorithms presented in this review.

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Neural network approach to classify infective keratitis

Abstract: ANN has the potential to help clinicians classify corneal ulcers more accurately.

Cited by 42 publications

References 15 publications

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks

A deep learning approach in diagnosing fungal keratitis based on corneal photographs

What is causing the corneal ulcer? Management strategies for unresponsive corneal ulceration

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