High-Tech Devices in the Diagnosis of Eye Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3155

Special Issue Editor


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Guest Editor
Department of Health Sciences, Università degli Studi di Milano, Unit of Ophthalmology, ASST Santi Paolo e Carlo, Milan, Italy
Interests: cornea; ocular surface; glaucoma

Special Issue Information

Dear Colleagues,

In recent decades, ophthalmology has been the object of relevant innovations in diagnostics. In former times, diagnosis of eye diseases was based on photography, microscopy, automated perimetry, and standard ultrasound techniques. Today, several new technologies are clinically available, including, amongst others, optical coherence tomography (OCT) and angioOCT, several instruments to classify dry eye disease, different types of topographers, aberrometers and analyzers of the anterior chamber, confocal microscopy, ultrasound biomicroscopy, and technologies that are starting to integrate morphology and function in glaucoma. The large amount of data collected by these instruments frequently open the access to analyses performed by artificial intelligence. The use of these innovative devices has enlarged our diagnostic abilities, allowing an earlier detection of eye diseases and the possibility to diagnose the earliest progressions. Yet some clinical characteristics (including long-term usefulness, i.e., the ability of these instruments to improve the vision-related quality of life of our patients) still need to be fully elucidated.

In this Special Issue, we are looking for research papers and review articles, including new findings in the fields of diagnosis of eye diseases using high-tech devices. Our goal is to address, amongst others, diagnostic imaging (optical diagnosis), molecular pathology diagnosis (biomarkers), and artificial intelligence diagnosis.

Dr. Paolo Fogagnolo
Guest Editor

Manuscript Submission Information

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Keywords

  • optical coherence tomography (OCT)
  • angioOCT
  • dry eye disease
  • topography
  • aberrometry
  • confocal microscopy
  • morphology and function
  • glaucoma

Published Papers (1 paper)

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Research

14 pages, 900 KiB  
Article
Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT
by Chao-Wei Wu, Hsiang-Li Shen, Chi-Jie Lu, Ssu-Han Chen and Hsin-Yi Chen
Diagnostics 2021, 11(9), 1718; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11091718 - 19 Sep 2021
Cited by 20 | Viewed by 2656
Abstract
Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) [...] Read more.
Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma. Full article
(This article belongs to the Special Issue High-Tech Devices in the Diagnosis of Eye Diseases)
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