Details

Autor(en) / Beteiligte

• Hoffmann, K.
• Gambichler, T.
• Rick, A.
• Kreutz, M.
• Anschuetz, M.
• Grünendick, T.
• Orlikov, A.
• Gehlen, S.
• Perotti, R.
• Andreassi, L.
• Newton Bishop, J.
• Césarini, J-P.
• Fischer, T.
• Frosch, P.J.
• Lindskov, R.
• Mackie, R.
• Nashan, D.
• Sommer, A.
• Neumann, M.
• Ortonne, J.P.
• Bahadoran, P.
• Penas, P.F.
• Zoras, U.
• Altmeyer, P.

Titel

Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy

Ist Teil von

• British journal of dermatology (1951), 2003-10, Vol.149 (4), p.801-809

Ort / Verlag

Oxford, UK: Blackwell Science Ltd

Erscheinungsjahr

2003

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Summary Background  Early detection of melanomas by means of diverse screening campaigns is an important step towards a reduction in mortality. Computer‐aided analysis of digital images obtained by dermoscopy has been reported to be an accurate, practical and time‐saving tool for the evaluation of pigmented skin lesions (PSLs). A prototype for the computer‐aided diagnosis of PSLs using artificial neural networks (NNs) has recently been developed: diagnostic and neural analysis of skin cancer (DANAOS). Objectives  To demonstrate the accuracy of PSL diagnosis by the DANAOS expert system, a multicentre study on a diverse multinational population was conducted. Methods  A calibrated camera system was developed and used to collect images of PSLs in a multicentre study in 13 dermatology centres in nine European countries. The dataset was used to train an NN expert system for the computer‐aided diagnosis of melanoma. We analysed different aspects of the data collection and its influence on the performance of the expert system. The NN expert system was trained with a dataset of 2218 dermoscopic images of PSLs. Results  The resulting expert system showed a performance similar to that of dermatologists as published in the literature. The performance depended on the size and quality of the database and its selection. Conclusions  The need for a large database, the usefulness of multicentre data collection, as well as the benefit of a representative collection of cases from clinical practice, were demonstrated in this trial. Images that were difficult to classify using the NN expert system were not identical to those found difficult to classify by clinicians. We suggest therefore that the combination of clinician and computer may potentially increase the accuracy of PSL diagnosis. This may result in improved detection of melanoma and a reduction in unnecessary excisions.