Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study

Br J Dermatol. 2002 Sep;147(3):481-6. doi: 10.1046/j.1365-2133.2002.04978.x.

Abstract

Background: In the last few years digital dermoscopy has been introduced as an additional tool to improve the clinical diagnosis of pigmented skin lesions.

Objective: To evaluate the validity of digital dermoscopy by comparing the diagnoses of a dermatologist experienced in dermoscopy (5 years of experience) with those of a clinician with minimal training in this field, and then comparing these results with those obtained using computer-aided diagnoses.

Methods: Three hundred and forty-one pigmented melanocytic and non-melanocytic skin lesions were included. All lesions were surgically excised and histopathologically examined. Digital dermoscopic images of all lesions were framed and analysed using software based on a trained artificial neural network. Cohen's kappa statistic was calculated to assess the validity with regard to the correct diagnoses of melanoma and non-melanoma.

Results: Sensitivity was high for the experienced dermatologist and the computer (92%) and lower for the inexperienced clinician (69%). Specificity of the diagnosis by the experienced dermatologist was higher (99%) than that of the inexperienced clinician (94%) and the computer assessment (74%). Notably, computer analysis gave a higher number of false positives (26%) compared with the experienced dermatologist (0.6%) and the inexperienced clinician (5.5%).

Conclusions: Our results indicate that analysis either by a trained dermatologist or an artificial neural network-trained computer can improve the diagnostic accuracy of melanoma compared with that of an inexperienced clinician and that the computer diagnosis might represent a useful tool for the screening of melanoma, particularly at centres not experienced in dermoscopy.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Child
  • Child, Preschool
  • Clinical Competence*
  • Dermatology / education
  • Diagnosis, Computer-Assisted
  • Diagnosis, Differential
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Male
  • Melanoma / diagnosis*
  • Microscopy, Video
  • Middle Aged
  • Neural Networks, Computer
  • Nevus, Pigmented / diagnosis
  • Predictive Value of Tests
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Skin Neoplasms / diagnosis*