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quantusRT

Retinal fundus image analysis and classification for diabetic retinopathy risk assessment

Non-invasive: quantusRT is a non-invasive test to predict the risk of malignancy of diabetic retinopathy from a retinography image.

Fast: quantusRT generates accurate results in just a few minutes.

QuantusRT reliability table

  Sensitivity Specificity
Clinical observation 80.0% 92.0%
quantusRT 74.3% 97.8%
* PPV and NPV (Positive Predictive Value and Negative Predictive Value)

WHY DOES quantusRT WORK?

An automated support tool is defined as one that requires minimal or no physician intervention to obtain a result. In recent years, research has focused on automatic algorithms to improve current clinical diagnosis based on images. The rise of Artificial Intelligence techniques, and especially Deep Learning techniques, has increased the number of studies using these types of algorithms in diagnostic ophthalmology.

Several recently published studies provide evidence that detection of diabetic retinopathy using trained Deep Learning models can achieve high accuracy in diverse populations and provide quantitative comparisons of how model performance may vary across datasets consisting of retinographies of different disease severity and ethnicity.

quantusRT is presented as a new Artificial Intelligence method based on last generation Deep Learning. Different studies carried out have proven the existing correlation between the quantitative analysis method proposed by quantusRT. The technology is based on performing a quantitative analysis of the texture of the retinal fundus image obtained using an ocular retinograph. This analysis makes it possible to identify patterns associated with specific pathologies and determine the risk of the presence of diabetic retinopathy.

WHEN TO USE quantusRT?

quantusRT has been designed with a clear focus on the diabetic population, and is intended to be a tool for the detection of diabetic retinopathy, being of great help in the screening of patients with risk factors and prioritization of waiting lists.

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