title: Validation of a Skin-Lesion Image-Matching Algorithm Based on Computer Vision Technology.
abstract: BACKGROUND
  Melanoma incidence is increasing globally, but consistently accurate skin-lesion classification methods remain elusive. We developed a simple software system to classify potentially all types of skin lesions. In the current study, we evaluated the system's ability to identify melanomas with a diameter of 10 mm or larger.
  
  
  METHODS
  The skin-lesion classification system is composed of a proprietary database of nearly 12,000 diagnosed skin-lesion images and a computer algorithm based on the principles of content-based image retrieval. The algorithm compares characteristics of new skin-lesion images with images in the database to identify the nearest-match diagnosis.
  
  
  RESULTS
  Nearly all classification accuracy measures for this new system exceeded 90%, with results for sensitivity of 90.4% (95% confidence interval, 85.6-93.7%), specificity of 91.5% (85.4-95.2%), positive predictive value of 94.5% (90.4-96.9%), negative predictive value of 85.5% (78.7-90.4%), and overall classification accuracy of 90.8% (87.2-93.4%).
  
  
  CONCLUSIONS
  The image-matching algorithm performed with high accuracy for the classification of larger melanomas. Furthermore, the system does not require a dermoscope or any other specialized hardware; any close-focusing camera will do. This system has the potential to be an inexpensive and accurate tool for the evaluation of skin lesions in ethnically and geographically diverse populations.
authors.affiliation: 1 Lūbax, Inc., Los Angeles, California.
authors.forename: Raymond H
authors.initials: RH
authors.lastname: Chen
authors.affiliation: 1 Lūbax, Inc., Los Angeles, California.
authors.forename: Magnus
authors.initials: M
authors.lastname: Snorrason
authors.affiliation: 1 Lūbax, Inc., Los Angeles, California.
authors.forename: Shelley M
authors.initials: SM
authors.lastname: Enger
authors.affiliation: 1 Lūbax, Inc., Los Angeles, California.
authors.forename: Eslam
authors.initials: E
authors.lastname: Mostafa
authors.affiliation: 2 Department of Dermatology, Stanford Medical School , Redwood City, California.
authors.forename: Justin M
authors.initials: JM
authors.lastname: Ko
authors.affiliation: 3 Department of Dermatology, University of Sao Paulo Medical School , Sao Paulo, Brazil .
authors.forename: Valeria
authors.initials: V
authors.lastname: Aoki
authors.affiliation: 4 The Manor Hospital , Oxford, United Kingdom .
authors.forename: Jonathan
authors.initials: J
authors.lastname: Bowling
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D003880:Dermatology / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008545:Melanoma / Q000145:classification* / Q000175:diagnosis* / Q000000981:diagnostic imaging; D008875:Middle Aged; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000145:classification* / Q000175:diagnosis* / Q000000981:diagnostic imaging; D012984:Software; D017216:Telemedicine / Q000379:methods*; D055815:Young Adult
keywords: computer-assisted image processing; databases; melanoma; skin neoplasms; store and forward; teledermatology