The University of Tokyo · Medicine
Professor Ryo Asaoka's research lab specializes in glaucoma progression prediction and visual field assessment, focusing on developing advanced statistical and machine learning models to improve clinical decision-making. The lab investigates the relationship between clinical measurements—such as visual acuity, visual field defects, intraocular pressure, and corneal parameters—and patient outcomes, with an emphasis on early detection and personalized monitoring. Key research directions include the application of regression models (e.g., Lasso, M-estimator, VBLR) and Random Forest algorithms to predict visual field progression using standard perimetry data, as well as evaluating the impact of binocular versus monocular visual field assessment on clinical interpretation. The lab also explores ocular biometrics and tonometry parameters to enhance the understanding of glaucoma pathophysiology and improve diagnostic accuracy.
Figures are computed from collected data and may differ slightly.
The VRQoL prediction model with the Random Forest method enables clinicians to better understand patients' VRQoL based on standard clinical measurements of VA and VF.
Monocular measures, such as better eye MD, can give the impression that a patient's VF loss is more degraded than it might be under binocular viewing. This effect is more pronounced in patients with advanced VF defects. The IVF offers a rapid assessment of a patient's binocular VF severity without extra testing.
The purpose of the study was to investigate the correlation between Corneal Visualization Scheimpflug Technology (Corvis ST tonometry: CST) parameters and various other ocular parameters, including intraocular pressure (IOP) with Goldmann applanation tonometry. IOP with Goldmann applanation tonometry (IOP-G), central corneal thickness (CCT), axial length (AL), corneal curvature, and CST parameters were measured in 94 eyes of 94 normal subjects. The relationship between ten CST parameters against
Age and the degree of VF damage were related to future progression. Average IOP was not related to the progression rate; however, fluctuation of IOP was associated with faster progression, although this was not the case when average IOP was below 15 mm Hg.
Purpose.: We evaluated the usefulness of various regression models, including least absolute shrinkage and selection operator (Lasso) regression, to predict future visual field (VF) progression in glaucoma patients. Methods.: Series of 10 VFs (Humphrey Field Analyzer 24-2 SITA-standard) from each of 513 eyes in 324 open-angle glaucoma patients, obtained in 4.9 ± 1.3 years (mean ± SD), were investigated. For each patient, the mean of all total deviation values (mTD) in the 10th VF was predicted u
The VBLR more accurately predicts future VF progression in glaucoma patients compared to conventional OLSLR, especially in short VF series.
Approximately 10 VFs, are needed to achieve an accurate prediction of PW VF sensitivity and mean sensitivity. Prediction error of PW VF sensitivity can be significantly minimized using the M-estimator robust regression model compared with conventional OLSLR.
In both POAG and PACG eyes, VF damage was more pronounced in superior hemifield than inferior hemifield; however, this tendency was more obvious in POAG eyes than in PACG eyes.
The bIOP measurement from CST is independent from CCT, but dependent on CH and CRF.
The MP-3 microperimeter appears to be useful to evaluate central visual function in RP eyes, exhibiting test-retest reproducibility that is equal to, or better than, that observed in HFA 10-2 VFs.
It is important to compare the results of Corneal Visualization Scheimpflug Technology instrument (CST) measurements and Reichert Ocular Response Analyzer (ORA) parameters. The purpose of the study was to investigate the association between CST measurements and ORA parameters in ninety-five patients with primary open-angle glaucoma. Measurements of CST, ORA, axial length (AL), average corneal curvature (CC), central corneal thickness (CCT) and intraocular pressure (IOP) with Goldmann applanation
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