BACKGROUND: Instruments designed to provide computer program-driven diagnosis of dermoscopic images of lesions are now commercially available. Multiple publications tout the improved diagnostic accuracy of these instruments compared with that of clinicians. OBJECTIVES: Our aim was to evaluate the actual usefulness of these instruments for dermatologists practising in a pigmented lesion clinic. METHODS: Over a 4-month period we subjected lesions, which were being evaluated in one of our clinics, to automated computer diagnosis performed by three commercially available instruments. We intentionally included three groups of lesions: group 1 lesions were suspicious melanocytic lesions that were scheduled to be excised; group 2 lesions were nonmelanocytic lesions; group 3 lesions were clinically obvious melanomas. The automated diagnoses provided by the instruments were compared with the dermoscopy diagnosis of experienced physicians and with histopathology. RESULTS: We included a total of 107 lesions. One imaging system's computer algorithm was unable to analyse one third of the lesions. All three instruments' computer algorithms were able to identify the clinically obvious melanomas (group 3) correctly. However, all three systems tended to overdiagnose by incorrectly classifying most seborrhoeic keratoses (group 2) as potential malignant lesions. Concerning the suspect melanocytic lesions (group 1), which are precisely the lesions for which a dermatologist would welcome a second opinion, we found significant variability in the diagnostic accuracy of the instruments tested. However, all three systems providing computer-assisted diagnosis had a tendency to overdiagnose benign melanocytic lesions as potential melanomas. CONCLUSIONS: Although the image analysis systems tested by us correctly identified the clinically obvious melanomas, they were not able to discriminate between most dysplastic naevi and early malignant melanoma. Thus, for the moment these computer-assisted diagnostic imaging machines provide little to no added benefit for the experienced dermatologist/dermoscopist.
BACKGROUND: Instruments designed to provide computer program-driven diagnosis of dermoscopic images of lesions are now commercially available. Multiple publications tout the improved diagnostic accuracy of these instruments compared with that of clinicians. OBJECTIVES: Our aim was to evaluate the actual usefulness of these instruments for dermatologists practising in a pigmented lesion clinic. METHODS: Over a 4-month period we subjected lesions, which were being evaluated in one of our clinics, to automated computer diagnosis performed by three commercially available instruments. We intentionally included three groups of lesions: group 1 lesions were suspicious melanocytic lesions that were scheduled to be excised; group 2 lesions were nonmelanocytic lesions; group 3 lesions were clinically obvious melanomas. The automated diagnoses provided by the instruments were compared with the dermoscopy diagnosis of experienced physicians and with histopathology. RESULTS: We included a total of 107 lesions. One imaging system's computer algorithm was unable to analyse one third of the lesions. All three instruments' computer algorithms were able to identify the clinically obvious melanomas (group 3) correctly. However, all three systems tended to overdiagnose by incorrectly classifying most seborrhoeic keratoses (group 2) as potential malignant lesions. Concerning the suspect melanocytic lesions (group 1), which are precisely the lesions for which a dermatologist would welcome a second opinion, we found significant variability in the diagnostic accuracy of the instruments tested. However, all three systems providing computer-assisted diagnosis had a tendency to overdiagnose benign melanocytic lesions as potential melanomas. CONCLUSIONS: Although the image analysis systems tested by us correctly identified the clinically obvious melanomas, they were not able to discriminate between most dysplastic naevi and early malignant melanoma. Thus, for the moment these computer-assisted diagnostic imaging machines provide little to no added benefit for the experienced dermatologist/dermoscopist.
Authors: Nabin K Mishra; Ravneet Kaur; Reda Kasmi; Jason R Hagerty; Robert LeAnder; Ronald J Stanley; Randy H Moss; William V Stoecker Journal: Skin Res Technol Date: 2019-03-14 Impact factor: 2.365
Authors: Jacqueline Dinnes; Jonathan J Deeks; Naomi Chuchu; Rubeta N Matin; Kai Yuen Wong; Roger Benjamin Aldridge; Alana Durack; Abha Gulati; Sue Ann Chan; Louise Johnston; Susan E Bayliss; Jo Leonardi-Bee; Yemisi Takwoingi; Clare Davenport; Colette O'Sullivan; Hamid Tehrani; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04
Authors: Lavinia Ferrante di Ruffano; Yemisi Takwoingi; Jacqueline Dinnes; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Rubeta N Matin; Kathie Godfrey; Colette O'Sullivan; Abha Gulati; Sue Ann Chan; Alana Durack; Susan O'Connell; Matthew D Gardiner; Jeffrey Bamber; Jonathan J Deeks; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04
Authors: Jacqueline Dinnes; Jonathan J Deeks; Matthew J Grainge; Naomi Chuchu; Lavinia Ferrante di Ruffano; Rubeta N Matin; David R Thomson; Kai Yuen Wong; Roger Benjamin Aldridge; Rachel Abbott; Monica Fawzy; Susan E Bayliss; Yemisi Takwoingi; Clare Davenport; Kathie Godfrey; Fiona M Walter; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04
Authors: Kajsa Møllersen; Herbert Kirchesch; Maciel Zortea; Thomas R Schopf; Kristian Hindberg; Fred Godtliebsen Journal: Biomed Res Int Date: 2015-11-26 Impact factor: 3.411