Thickness Speed Progression Index: Machine Learning Approach for Keratoconus Detection.

Abstract

Authors

• Shady T Awwad
  Department of Ophthalmology, American University of Beirut Medical Center (P.I., S.T.A.), Beirut, Lebanon. Electronic address: sawwad@gmail.com.
• Bassel Hammoud
  1Biomedical Engineering Program and.
• Jad F Assaf
  Faculty of Medicine, American University of Beirut (J.F.A., C.Z., P.B., M.C.), Beirut, Lebanon.
• Lara Asroui
  From the Department of Ophthalmology (S.T.A., B.H., L.A., and C.J.M.), American University of Beirut Medical Center, Beirut, Lebanon.
• James Bradley Randleman
  Cole Eye Institute (B.H. and J.B.R.), Cleveland Clinic, Cleveland, Ohio, USA; Cleveland Clinic Lerner College of Medicine of Case Western Reserve University (J.B.R.), Cleveland, Ohio, USA.
• Cynthia J Roberts
  Departments of Ophthalmology & Visual Sciences and Biomedical Engineering (C.J.R.), The Ohio State University, Columbus, Ohio, USA.
• Douglas D Koch
  Cullen Eye Institute (D.D.K.), Baylor College of Medicine, Houston, Texas, USA.
• Jawad Kaisania
  Department of Computer Science (J.K. and S.E.), American University of Beirut, Beirut, Lebanon.
• Carl-Joe Mehanna
  Department of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, Creteil, France.
• Shadi Elbassuoni
  Department of Computer Science (J.K. and S.E.), American University of Beirut, Beirut, Lebanon.

Keywords

External Resources