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...
Autores principales: | , , , , , |
---|---|
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 |