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Ros-NET Diagnosis Tool


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Diagnosis of rosacea has traditionally been done with a physical examination, history and 'qualitative human assessment [which] is often subjective and suffers from a relatively high intra- and inter-observer variability in evaluating patient outcomes,' [1] sometimes resulting in misdiagnosis. [2] A team of seven researchers at Ohio State University’s Department of Dermatology and the Wake Forest School of Medicine have developed an algorithm using AI, dubbed “Ros-NET,” that relies on digital images of rosacea patients to diagnose patients which according to the team works 88% to 90% correctly. The team used the same algorithm similar to one they had previously developed for acne. We may be hearing more about Ros-NET. [1]

End Notes

[1] Skin Res Technol 2020 May;26(3):413-421. doi:10.1111/srt.12817
Ros-NET: A deep convolutional neural network for automatic identification of rosacea lesions
Binol H, Plotner A, et al.

[2] Misdiagnosed Rosacea 

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