PT  - JOURNAL ARTICLE
AU  - Koichiro Sugimoto
AU  - Hiroshi Murata
AU  - Hiroyo Hirasawa
AU  - Makoto Aihara
AU  - Chihiro Mayama
AU  - Ryo Asaoka
TI  - 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?
AID  - 10.1136/bmjopen-2013-003114
DP  - 2013 Oct 01
TA  - BMJ Open
PG  - e003114
VI  - 3
IP  - 10
4099  - http://bmjopen.bmj.com/content/3/10/e003114.short
4100  - http://bmjopen.bmj.com/content/3/10/e003114.full
SO  - BMJ Open2013 Oct 01; 3
AB  - 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.