Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch’s membrane opening-minimum rim width and RNFL

Autor: Sat Byul Seo, Hyun-Kyung Cho
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0301 basic medicine
Adult
Male
medicine.medical_specialty
Nerve fiber layer
Glaucoma
lcsh:Medicine
Diagnostic Techniques
Ophthalmological
Bruch's membrane
Retina
Article
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Deep Learning
Ophthalmology
Normal tension glaucoma
Diagnosis
medicine
Image Processing
Computer-Assisted
Humans
Low Tension Glaucoma
lcsh:Science
Intraocular Pressure
Multidisciplinary
Receiver operating characteristic
business.industry
Deep learning
lcsh:R
Area under the curve
Retinal
Middle Aged
medicine.disease
030104 developmental biology
medicine.anatomical_structure
chemistry
ROC Curve
Outcomes research
Area Under Curve
030221 ophthalmology & optometry
Optic nerve diseases
Female
lcsh:Q
Artificial intelligence
Bruch Membrane
business
Glaucoma
Open-Angle
Tomography
Optical Coherence
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Scientific Reports
ISSN: 2045-2322
Popis: We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch’s membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learning model. Discriminating early-stage glaucoma and GS is challenging and a deep-learning model may be helpful to clinicians. NTG accounts for an average 77% of open-angle glaucoma in Asians. BMO-MRW is a new structural parameter that has advantages in assessing neuroretinal rim tissue more accurately than conventional parameters. A dataset consisted of 229 eyes out of 277 GS and 168 eyes of 285 patients with early NTG. A deep-learning algorithm was developed to discriminate between GS and early NTG using a training set, and its accuracy was validated in the testing dataset using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The deep neural network model (DNN) achieved highest diagnostic performance, with an AUC of 0.966 (95%confidence interval 0.929–1.000) in classifying either GS or early NTG, while AUCs of 0.927–0.947 were obtained by other machine-learning models. The performance of the DNN model considering all three OCT-based parameters was the highest (AUC 0.966) compared to the combinations of just two parameters. As a single parameter, BMO-MRW (0.959) performed better than RNFL alone (0.914).
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje