TY - JOUR
T1 - Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study
AU - Yousefi, Siamak
AU - Pasquale, Louis R.
AU - Boland, Michael V.
AU - Johnson, Chris A.
N1 - Funding Information:
This work was supported by NIH Grants EY033005 (S.Y.), R21EY031725 (S.Y.), Challenge Grant from Research to Prevent Blindness (S.Y.), and R01EY015473 (L.R.P.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. L.R.P. is also supported by Research to Prevent Blindness (NYC) and The Glaucoma Foundation (NYC).
Funding Information:
This work was supported by NIH Grants EY033005 (S.Y.), R21EY031725 (S.Y.), Challenge Grant from Research to Prevent Blindness (S.Y.), and R01EY015473 (L.R.P.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. L.R.P. is also supported by Research to Prevent Blindness (NYC) and The Glaucoma Foundation (NYC). Obtained funding: Siamak Yousefi
Publisher Copyright:
© 2022 American Academy of Ophthalmology
PY - 2022/12
Y1 - 2022/12
N2 - Purpose: To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. Design: Cross-sectional and longitudinal study. Participants: A total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years. Methods: Visual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified. Main Outcome Measures: Machine-expert correspondence and type of patterns of VF loss associated with rapid progression. Results: The average VF mean deviation (MD) at conversion to glaucoma was −2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was −5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of −1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD. Conclusions: An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.
AB - Purpose: To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. Design: Cross-sectional and longitudinal study. Participants: A total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years. Methods: Visual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified. Main Outcome Measures: Machine-expert correspondence and type of patterns of VF loss associated with rapid progression. Results: The average VF mean deviation (MD) at conversion to glaucoma was −2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was −5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of −1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD. Conclusions: An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.
KW - Artificial intelligence
KW - Deep archetypal analysis
KW - Patterns of visual field loss
KW - Rapid glaucoma progression
KW - Unsupervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85138575342&partnerID=8YFLogxK
U2 - 10.1016/j.ophtha.2022.07.001
DO - 10.1016/j.ophtha.2022.07.001
M3 - Article
C2 - 35817199
AN - SCOPUS:85138575342
VL - 129
SP - 1402
EP - 1411
JO - Ophthalmology
JF - Ophthalmology
SN - 0161-6420
IS - 12
ER -