Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT

Autor:	Hsin-Yi Chen, Hsiang-Li Shen, Chao-Wei Wu, Chi-Jie Lu, Ssu-Han Chen
Rok vydání:	2021
Předmět:	Medicine (General) optical coherence tomography (OCT) machine learning glaucoma genetic structures Clinical Biochemistry Posterior pole Decision tree Nerve fiber layer Glaucoma Machine learning computer.software_genre Article Logistic model tree R5-920 Optical coherence tomography medicine Medical diagnosis medicine.diagnostic_test business.industry medicine.disease eye diseases Random forest medicine.anatomical_structure sense organs Artificial intelligence business computer
Zdroj:	Diagnostics Diagnostics, Vol 11, Iss 1718, p 1718 (2021) Diagnostics; Volume 11; Issue 9; Pages: 1718
ISSN:	2075-4418
DOI:	10.3390/diagnostics11091718
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c13e2da54e759fd8b99f521bd601f413 https://doi.org/10.3390/diagnostics11091718 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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