title: Total body photography for skin cancer screening.
abstract: BACKGROUND
  Total body photography may aid in melanoma screening but is not widely applied due to time and cost. We hypothesized that a near-simultaneous automated skin photo-acquisition system would be acceptable to patients and could rapidly obtain total body images that enable visualization of pigmented skin lesions.
  
  
  METHODS
  From February to May 2009, a study of 20 volunteers was performed at the University of Virginia to test a prototype 16-camera imaging booth built by the research team and to guide development of special purpose software. For each participant, images were obtained before and after marking 10 lesions (five "easy" and five "difficult"), and images were evaluated to estimate visualization rates. Imaging logistical challenges were scored by the operator, and participant opinion was assessed by questionnaire.
  
  
  RESULTS
  Average time for image capture was three minutes (range 2-5). All 55 "easy" lesions were visualized (sensitivity 100%, 90% CI 95-100%), and 54/55 "difficult" lesions were visualized (sensitivity 98%, 90% CI 92-100%). Operators and patients graded the imaging process favorably, with challenges identified regarding lighting and positioning.
  
  
  CONCLUSIONS
  Rapid-acquisition automated skin photography is feasible with a low-cost system, with excellent lesion visualization and participant acceptance. These data provide a basis for employing this method in clinical melanoma screening.
authors.affiliation: Department of Surgery, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: Lynn T
authors.initials: LT
authors.lastname: Dengel
authors.affiliation: Department of Public Health Sciences, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: Gina R
authors.initials: GR
authors.lastname: Petroni
authors.affiliation: Department of Surgery, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Judge
authors.affiliation: Department of Biomedical Engineering, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Chen
authors.affiliation: Department of Biomedical Engineering, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: Scott T
authors.initials: ST
authors.lastname: Acton
authors.affiliation: Department of Surgery, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: Anneke T
authors.initials: AT
authors.lastname: Schroen
authors.affiliation: Department of Surgery, University of Virginia Health Systems, Charlottesville, VA, USA.
authors.forename: Craig L
authors.initials: CL
authors.lastname: Slingluff
mesh_terms: D001291:Attitude of Health Personnel; D055088:Early Detection of Cancer / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008545:Melanoma / Q000175:diagnosis*; D017060:Patient Satisfaction; D010781:Photography / Q000295:instrumentation* / Q000379:methods; D010865:Pilot Projects; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis*; D012984:Software; D013997:Time Factors