Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting.

Journal: Ophthalmology. Glaucoma
PMID: 39277171

Abstract

PURPOSE: This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence results were compared with tele-ophthalmologists' diagnoses and with a glaucoma specialist's assessment for those participants referred to a tertiary eye care hospital.

Authors

  • Swati Upadhyaya
    Department of Glaucoma, Aravind Eye Hospital, Pondicherry, India. Electronic address: docswatinirmala@gmail.com.
  • Divya Parthasarathy Rao
    Remidio Innovative Solutions, Inc, Glen Allen, Virginia.
  • Srinivasan Kavitha
    Department of Glaucoma, Aravind Eye Hospital, Pondicherry, India.
  • Shonraj Ballae Ganeshrao
    Remidio Innovative Solutions Private Limited, Bengaluru, India.
  • Kalpa Negiloni
    Remidio Innovative Solutions Private Limited, Bengaluru, India.
  • Shreya Bhandary
    Remidio Innovative Solutions Private Limited, Bengaluru, India.
  • Florian M Savoy
    AI&ML, Medios Technologies Pte Ltd, Remidio Innovative Solutions, Singapore.
  • Rengaraj Venkatesh
    Glaucoma, Aravind Eye Hospital, Pondicherry, India.

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