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Building Artificial Intelligence for Dermatological PracticeDOI: 10.4236/oalib.1104541, PP. 1-6 Subject Areas: Dermatology Keywords: Aesthetics, Telemedicine, Teledermatology Abstract
Purpose: Wrinkles
are the most visually obvious features of aging and are the prime target of a
vast number of products (both medical and cosmetic). It is important for
clinicians to be able to grade wrinkles objectively. Although wrinkles are
easily recognisable for humans, it remains a very challenging task for computer
vision systems to detect them automatically. In our center, we developed a
wrinkle detection algorithm based on a technique called “reversible jump Markov
chain Monte Carlo framework with delayed rejection”. This system is able to
accurately and rapidly detect wrinkles. Methods: 300
images were submitted to the analyser for reading. Each image was analysed with
a million iterations in ten minutes. The same 300 images were sent to a
dermatologist for post-analyser evaluation. The system was trained to detect
major and minor wrinkles. The results were benchmarked against the reviewing
dermatologist. Results: Out of 300 patients, the
pickup rate for major wrinkles was 100%. However, on average it would be able
to trace out only approximately 56.5% of the entire length of the wrinkle. The
analyser was also able to detect minor wrinkles. However, the detection rate
was only 13.4%. Conclusion: Our system is able to
accurately detect all major wrinkles. This enables physicians to track progress
of antiwrinkling techniques such as Botox or surgical facelifts. Our system is
also low cost as the wrinkle detection can be simply based off simple
photographs.
Tian, B. (2018). Building Artificial Intelligence for Dermatological Practice. Open Access Library Journal, 5, e4541. doi: http://dx.doi.org/10.4236/oalib.1104541. References
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