{
  "abstract": "Background/Introduction This study was developed to determine whether a machine learning model could be developed to assess blood pressure with accuracy comparable to arm cuff measurements.Methods A deep learning model was developed based on the UK Biobank dataset and was trained to detect both systolic and diastolic pressure. The hypothesis was formulated after data collection and before the development of the model. A comparison was conducted between arm cuff measurements, as ground truth and results from the model, using mean absolute error (MAE), mean squared error and coefficient of determination (R 2).Results Systolic pressure was measured with 9.81 MAE, 165.13 mean squared error and 0.36 R 2. Diastolic pressure was measured with 6.00 MAE, 58.21 and 0.30 R2.Conclusions This model improves on existing research and shows errors comparable to the variability of hand cuff measurements. The use of fundus images to assess blood pressure may be more indicative of long-term hypertension. Additional trials in clinical settings may be necessary, as well as additional prospective studies to validate results.",
  "authors": [
    {
      "affiliations": [
        "AEYE Health, New York, New York, USA"
      ],
      "name": "Idan Bressler"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Rabin Medical Center, Petah Tikva, Israel",
        "Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel"
      ],
      "name": "Dolev Dollberg"
    },
    {
      "affiliations": [
        "AEYE Health, New York, New York, USA"
      ],
      "name": "Rachelle Aviv"
    },
    {
      "affiliations": [
        "AEYE Health, New York, New York, USA"
      ],
      "name": "Danny Margalit"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, USA"
      ],
      "name": "Alon Harris"
    },
    {
      "affiliations": [
        "Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, USA"
      ],
      "name": "Brent Siesky"
    },
    {
      "affiliations": [
        "AEYE Health, New York, New York, USA",
        "New York Eye and Ear of Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA"
      ],
      "name": "Tsontcho Ianchulev"
    },
    {
      "affiliations": [
        "AEYE Health, New York, New York, USA"
      ],
      "name": "Zack Dvey-Aharon"
    }
  ],
  "title": "Non-contact optical blood pressure biometry using AI-based analysis of non-mydriatic fundus imaging",
  "uid": "ec42b67a-231e-5e1e-8c59-c8ef304f1b86"
}