- Title
- Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression
- Creators
- Osamah Saeedi - Visual SciencesMichael V Boland - Massachusetts Eye and Ear, Boston, MA, USALoris D'Acunto - San Francisco, CA, USARamya Swamy - Visual SciencesVikram Hegde - San Francisco, CA, USASurabhi Gupta - San Francisco, CA, USAAmin Venjara - Princeton, NJ, USAJoby Tsai - Visual SciencesJonathan S Myers - Wills Eye HospitalSarah R Wellik - University of MiamiGustavo DeMoraes - Columbia UniversityLouis R Pasquale - Icahn School of Medicine at Mount SinaiLucy Q Shen - Massachusetts Eye and Ear, Boston, MA, USAYangjiani Li - Harvard Medical SchoolTobias Elze - Harvard Medical School
- Publication Details
- Translational vision science & technology, Vol.10(7), pp.27-27
- Publisher
- The Association for Research in Vision and Ophthalmology
- Academic Unit
- UMMG Department of Ophthalmology; Miller School of Medicine
- Language
- English
- Resource Type
- Journal article
- PMID
- 34157101
- Record Identifier
- 991031660540202976
Journal article
Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression
Translational vision science & technology, Vol.10(7), pp.27-27
2021
PMID: 34157101
Metrics
5 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.36 Ophthalmology
- 1.36.226 Glaucoma
- Web Of Science research areas
- Ophthalmology
- ESI research areas
- Clinical Medicine
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Source: InCites