title: A perimetric test procedure that uses structural information.
abstract: OBJECTIVE
  To develop a perimetric test strategy, Structure Estimation of Minimum Uncertainty (SEMU), that uses structural information to drive stimulus choices.
  
  
  METHODS
  Structure Estimation of Minimum Uncertainty uses retinal nerve fiber layer (RNFL) thickness data as measured by optical coherence tomography to predict perimetric sensitivity. This prediction is used to set suprathreshold levels that then alter a prior probability distribution of the final test output. Using computer simulation, we studied SEMU's performance under three different patient error response conditions: No Error, Typical False Positive errors, and Extremely Unreliable patients. In experiment 1, SEMU was compared with an existing suprathreshold cum thresholding combination test procedure, Estimation of Minimum Uncertainty (EMU), on single visual field locations. We used these results to finalize SEMU parameters. In experiment 2, SEMU was compared with full threshold (FT) on 163 glaucomatous visual fields.
  
  
  RESULTS
  On individual locations, SEMU has similar accuracy to EMU, but is, on average, one presentation faster than EMU. For the typical false-positive error condition, SEMU has significantly lower error compared with FT (SEMU average 0.33 dB lower; p < 0.001) and the 90% measured sensitivity range for SEMU is also smaller than that for FT. For unreliable patients, however, FT has lower mean and SD of error. Structure Estimation of Minimum Uncertainty makes significantly fewer presentations than FT (1.08 presentation on average fewer in a typical false-positive condition; p < 0.001). Assuming that a location in the field is marked abnormal if it falls below the 5th percentile of normal, SEMU has a false-positive rate of less than 10% for all error conditions compared with FT's rate of 20% or more.
  
  
  CONCLUSIONS
  On average, simulations show that using RNFL information to guide stimulus placement in a perimetric test procedure maintains accuracy, improves precision, and decreases test duration for patients with less than 15% false-positive rates.
authors.affiliation: *MPhil †PhD Computing and Information Systems (SBG, JD, AT), and Optometry and Vision Sciences (AMM, JD), the University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: Shonraj Ballae
authors.initials: SB
authors.lastname: Ganeshrao
authors.forename: Allison M
authors.initials: AM
authors.lastname: McKendrick
authors.forename: Jonathan
authors.initials: J
authors.lastname: Denniss
authors.forename: Andrew
authors.initials: A
authors.lastname: Turpin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005189:False Positive Reactions; D005901:Glaucoma / Q000503:physiopathology*; D006801:Humans; D009412:Nerve Fibers / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000503:physiopathology*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012165:Retinal Ganglion Cells / Q000473:pathology*; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence; D058609:Visual Field Tests / Q000379:methods*; D014794:Visual Fields / Q000502:physiology*