D Gutkowicz-Krusin1, M Elbaum1, P Szwaykowski1, A W Kopf1. 1. Electro-Optical Sciences, Inc., Irvington, NYThe Ronald O. Perelman Department of Dermatology, New York University School of Medicine, Skin and Cancer Unit, New York, NY, USA.
Abstract
BACKGROUND/AIMS: Differentiation between early (Breslow thickness less than 1 mm) malignant melanoma (MM) and atypical melanocytic nevus (AMN) remains a challenge even to trained clinicians. The purpose of this study is to determine the feasibility of reliable discrimination between early MM and AMN with noninvasive, objective, automatic machine vision techniques. METHODS: A data base of 104 digitized dermoscopic color transparencies of melanocytic lesions was used to develop and test our computer-based algorithms for classification of such lesions as malignant (MM) or benign (AMN). Histopathologic diagnoses (30 MM and 74 AMN) were used as the "gold standard" for training and testing the algorithms. RESULTS: A fully automatic, objective technique for differentiating between early MM and AMN from their dermoscopic digital images was developed. The multiparameter linear classifier was trained to provide 100% sensitivity for MM. In the blind test, this technique did not miss a single MM and its specificity was comparable to that of skilled dermatologists. CONCLUSIONS: Reliable differentiation between early MM and AMN with high sensitivity is possible using machine vision techniques to analyze digitized dermoscopic lesion images.
BACKGROUND/AIMS: Differentiation between early (Breslow thickness less than 1 mm) malignant melanoma (MM) and atypical melanocytic nevus (AMN) remains a challenge even to trained clinicians. The purpose of this study is to determine the feasibility of reliable discrimination between early MM and AMN with noninvasive, objective, automatic machine vision techniques. METHODS: A data base of 104 digitized dermoscopic color transparencies of melanocytic lesions was used to develop and test our computer-based algorithms for classification of such lesions as malignant (MM) or benign (AMN). Histopathologic diagnoses (30 MM and 74 AMN) were used as the "gold standard" for training and testing the algorithms. RESULTS: A fully automatic, objective technique for differentiating between early MM and AMN from their dermoscopic digital images was developed. The multiparameter linear classifier was trained to provide 100% sensitivity for MM. In the blind test, this technique did not miss a single MM and its specificity was comparable to that of skilled dermatologists. CONCLUSIONS: Reliable differentiation between early MM and AMN with high sensitivity is possible using machine vision techniques to analyze digitized dermoscopic lesion images.
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