1. Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms

    Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms

    Purpose To develop a deep learning method to predict visual field (VF) from wide-angle swept-source optical coherence tomography (SS-OCT) and compare the performance of three Google Inception architectures. Methods Three deep learning models (with Inception-ResNet-v2, Inception-v3, and Inception-v4) were trained to predict 24-2 VF from the macular ganglion cell-inner plexiform layer and the peripapillary retinal nerve fibre layer map obtained by SS-OCT. The prediction performance of the three models was evaluated by using the root mean square error (RMSE) between the actual and predicted VF. The performance was also compared among different glaucoma severities and Garway-Heath sectorizations. Results The training ...

    Read Full Article

    Login to comment.

  1. Categories

    1. Applications:

      Art, Cardiology, Dentistry, Dermatology, Developmental Biology, Gastroenterology, Gynecology, Microscopy, NDE/NDT, Neurology, Oncology, Ophthalmology, Other Non-Medical, Otolaryngology, Pulmonology, Urology
    2. Business News:

      Acquisition, Clinical Trials, Funding, Other Business News, Partnership, Patents
    3. Technology:

      Broadband Sources, Probes, Tunable Sources
    4. Miscellaneous:

      Jobs & Studentships, Student Theses, Textbooks
  2. Topics Mentioned

  3. Authors