Cargando…

Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?

OBJECTIVES: To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm. DESIGN: Case–control study. PARTICIPANTS: 293 eyes of...

Descripción completa

Detalles Bibliográficos
Autores principales: Sugimoto, Koichiro, Murata, Hiroshi, Hirasawa, Hiroyo, Aihara, Makoto, Mayama, Chihiro, Asaoka, Ryo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796272/
https://www.ncbi.nlm.nih.gov/pubmed/24103806
http://dx.doi.org/10.1136/bmjopen-2013-003114
_version_ 1782287451388116992
author Sugimoto, Koichiro
Murata, Hiroshi
Hirasawa, Hiroyo
Aihara, Makoto
Mayama, Chihiro
Asaoka, Ryo
author_facet Sugimoto, Koichiro
Murata, Hiroshi
Hirasawa, Hiroyo
Aihara, Makoto
Mayama, Chihiro
Asaoka, Ryo
author_sort Sugimoto, Koichiro
collection PubMed
description OBJECTIVES: To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm. DESIGN: Case–control study. PARTICIPANTS: 293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG. INTERVENTIONS: Spectral domain OCT (Topcon 3D OCT-2000) and perimetry (Humphrey Field Analyser, 24-2 or 30-2 SITA standard) measurements were conducted in all of the participants. VF damage (Ocular Hypertension Treatment Study criteria (2002)) was used as a ‘gold-standard’ to classify glaucomatous eyes. The ‘Random Forest’ method was then used to analyse the relationship between the presence/absence of glaucomatous VF damage and the following variables: age, gender, right or left eye, axial length plus 237 different OCT measurements. MAIN OUTCOME MEASURES: The area under the receiver operating characteristic curve (AROC) was then derived using the probability of glaucoma as suggested by the proportion of votes in the Random Forest classifier. For comparison, five AROCs were derived based on: (1) macular retinal nerve fibre layer (m-RNFL) alone; (2) circumpapillary (cp-RNFL) alone; (3) ganglion cell layer and inner plexiform layer (GCL+IPL) alone; (4) rim area alone and (5) a decision tree method using the same variables as the Random Forest algorithm. RESULTS: The AROC from the combined Random Forest classifier (0.90) was significantly larger than the AROCs based on individual measurements of m-RNFL (0.86), cp-RNFL (0.77), GCL+IPL (0.80), rim area (0.78) and the decision tree method (0.75; p<0.05). CONCLUSIONS: Evaluating OCT measurements using the Random Forest method provides an accurate prediction of the presence of perimetric deterioration in glaucoma suspects.
format Online
Article
Text
id pubmed-3796272
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-37962722013-10-15 Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects? Sugimoto, Koichiro Murata, Hiroshi Hirasawa, Hiroyo Aihara, Makoto Mayama, Chihiro Asaoka, Ryo BMJ Open Ophthalmology OBJECTIVES: To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm. DESIGN: Case–control study. PARTICIPANTS: 293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG. INTERVENTIONS: Spectral domain OCT (Topcon 3D OCT-2000) and perimetry (Humphrey Field Analyser, 24-2 or 30-2 SITA standard) measurements were conducted in all of the participants. VF damage (Ocular Hypertension Treatment Study criteria (2002)) was used as a ‘gold-standard’ to classify glaucomatous eyes. The ‘Random Forest’ method was then used to analyse the relationship between the presence/absence of glaucomatous VF damage and the following variables: age, gender, right or left eye, axial length plus 237 different OCT measurements. MAIN OUTCOME MEASURES: The area under the receiver operating characteristic curve (AROC) was then derived using the probability of glaucoma as suggested by the proportion of votes in the Random Forest classifier. For comparison, five AROCs were derived based on: (1) macular retinal nerve fibre layer (m-RNFL) alone; (2) circumpapillary (cp-RNFL) alone; (3) ganglion cell layer and inner plexiform layer (GCL+IPL) alone; (4) rim area alone and (5) a decision tree method using the same variables as the Random Forest algorithm. RESULTS: The AROC from the combined Random Forest classifier (0.90) was significantly larger than the AROCs based on individual measurements of m-RNFL (0.86), cp-RNFL (0.77), GCL+IPL (0.80), rim area (0.78) and the decision tree method (0.75; p<0.05). CONCLUSIONS: Evaluating OCT measurements using the Random Forest method provides an accurate prediction of the presence of perimetric deterioration in glaucoma suspects. BMJ Publishing Group 2013-10-05 /pmc/articles/PMC3796272/ /pubmed/24103806 http://dx.doi.org/10.1136/bmjopen-2013-003114 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Ophthalmology
Sugimoto, Koichiro
Murata, Hiroshi
Hirasawa, Hiroyo
Aihara, Makoto
Mayama, Chihiro
Asaoka, Ryo
Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
title Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
title_full Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
title_fullStr Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
title_full_unstemmed Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
title_short Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
title_sort cross-sectional study: does combining optical coherence tomography measurements using the ‘random forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?
topic Ophthalmology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3796272/
https://www.ncbi.nlm.nih.gov/pubmed/24103806
http://dx.doi.org/10.1136/bmjopen-2013-003114
work_keys_str_mv AT sugimotokoichiro crosssectionalstudydoescombiningopticalcoherencetomographymeasurementsusingtherandomforestdecisiontreeclassifierimprovethepredictionofthepresenceofperimetricdeteriorationinglaucomasuspects
AT muratahiroshi crosssectionalstudydoescombiningopticalcoherencetomographymeasurementsusingtherandomforestdecisiontreeclassifierimprovethepredictionofthepresenceofperimetricdeteriorationinglaucomasuspects
AT hirasawahiroyo crosssectionalstudydoescombiningopticalcoherencetomographymeasurementsusingtherandomforestdecisiontreeclassifierimprovethepredictionofthepresenceofperimetricdeteriorationinglaucomasuspects
AT aiharamakoto crosssectionalstudydoescombiningopticalcoherencetomographymeasurementsusingtherandomforestdecisiontreeclassifierimprovethepredictionofthepresenceofperimetricdeteriorationinglaucomasuspects
AT mayamachihiro crosssectionalstudydoescombiningopticalcoherencetomographymeasurementsusingtherandomforestdecisiontreeclassifierimprovethepredictionofthepresenceofperimetricdeteriorationinglaucomasuspects
AT asaokaryo crosssectionalstudydoescombiningopticalcoherencetomographymeasurementsusingtherandomforestdecisiontreeclassifierimprovethepredictionofthepresenceofperimetricdeteriorationinglaucomasuspects