Building Artificial Intelligence for Dermatological Practice - Open Access Library Journal

OALibJ > Vol.5 No.4, April 2018

Building Artificial Intelligence for Dermatological Practice ()

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DOI: 10.4236/oalib.1104541 589 Downloads 1,394 Views Citations

Author(s)

Brian Tian

Affiliation(s)

IDOC Medical Holdings, Singapore.

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.

KEYWORDS

Aesthetics, Telemedicine, Teledermatology

Share and Cite:

Tian, B. (2018) Building Artificial Intelligence for Dermatological Practice. Open Access Library Journal, 5, 1-6. doi: 10.4236/oalib.1104541.

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