Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy

Background: Malignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history. However, there is controversy over screening programs and many advocate screening only for high-risk individuals. Objectives: This study aimed to evaluate the accuracy of an art...

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Autores principales:	Michael Phillips, Jack Greenhalgh, Helen Marsden, Ioulios Palamaras
Formato:	article
Lenguaje:	EN
Publicado:	Mattioli1885 2019
Materias:	melanoma artificial intelligence primary care detection identification Dermatology RL1-803
Acceso en línea:	https://doaj.org/article/e54e9fb8eaed4e668190606c93d5df7e
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spelling	oai:doaj.org-article:e54e9fb8eaed4e668190606c93d5df7e2021-11-17T08:29:04ZDetection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy10.5826/dpc.1001a112160-9381https://doaj.org/article/e54e9fb8eaed4e668190606c93d5df7e2019-12-01T00:00:00Zhttp://dpcj.org/index.php/dpc/article/view/735https://doaj.org/toc/2160-9381 Background: Malignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history. However, there is controversy over screening programs and many advocate screening only for high-risk individuals. Objectives: This study aimed to evaluate the accuracy of an artificial intelligence neural network (Deep Ensemble for Recognition of Melanoma [DERM]) to identify malignant melanoma from dermoscopic images of pigmented skin lesions and to show how this compared to doctors’ performance assessed by meta-analysis. Methods: DERM was trained and tested using 7,102 dermoscopic images of both histologically confirmed melanoma (24%) and benign pigmented lesions (76%). A meta-analysis was conducted of studies examining the accuracy of naked-eye examination, with or without dermoscopy, by specialist and general physicians whose clinical diagnosis was compared to histopathology. The meta-analysis was based on evaluation of 32,226pigmented lesions including 3,277 histopathology-confirmed malignant melanoma cases. The receiver operating characteristic (ROC) curve was used to examine and compare the diagnostic accuracy. Results: DERM achieved a ROC area under the curve (AUC) of 0.93 (95% confidence interval: 0.92-0.94), and sensitivity and specificity of 85.0% and 85.3%, respectively. Avoidance of false-negative results is essential, so different decision thresholds were examined. At 95% sensitivity DERM achieved a specificity of 64.1% and at 95% specificity the sensitivity was 67%. The meta-analysis showed primary care physicians (10 studies) achieve an AUC of 0.83 (95% confidence interval: 0.79-0.86), with sensitivity and specificity of 79.9% and 70.9%; and dermatologists (92 studies) 0.91 (0.88-0.93), 87.5%, and 81.4%, respectively. Conclusions: DERM has the potential to be used as a decision support tool in primary care, by providing dermatologist-grade recommendation on the likelihood of malignant melanoma. Michael PhillipsJack GreenhalghHelen MarsdenIoulios PalamarasMattioli1885articlemelanomaartificial intelligenceprimary caredetectionidentificationDermatologyRL1-803ENDermatology Practical & Conceptual (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	melanoma artificial intelligence primary care detection identification Dermatology RL1-803
spellingShingle	melanoma artificial intelligence primary care detection identification Dermatology RL1-803 Michael Phillips Jack Greenhalgh Helen Marsden Ioulios Palamaras Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy
description	Background: Malignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history. However, there is controversy over screening programs and many advocate screening only for high-risk individuals. Objectives: This study aimed to evaluate the accuracy of an artificial intelligence neural network (Deep Ensemble for Recognition of Melanoma [DERM]) to identify malignant melanoma from dermoscopic images of pigmented skin lesions and to show how this compared to doctors’ performance assessed by meta-analysis. Methods: DERM was trained and tested using 7,102 dermoscopic images of both histologically confirmed melanoma (24%) and benign pigmented lesions (76%). A meta-analysis was conducted of studies examining the accuracy of naked-eye examination, with or without dermoscopy, by specialist and general physicians whose clinical diagnosis was compared to histopathology. The meta-analysis was based on evaluation of 32,226pigmented lesions including 3,277 histopathology-confirmed malignant melanoma cases. The receiver operating characteristic (ROC) curve was used to examine and compare the diagnostic accuracy. Results: DERM achieved a ROC area under the curve (AUC) of 0.93 (95% confidence interval: 0.92-0.94), and sensitivity and specificity of 85.0% and 85.3%, respectively. Avoidance of false-negative results is essential, so different decision thresholds were examined. At 95% sensitivity DERM achieved a specificity of 64.1% and at 95% specificity the sensitivity was 67%. The meta-analysis showed primary care physicians (10 studies) achieve an AUC of 0.83 (95% confidence interval: 0.79-0.86), with sensitivity and specificity of 79.9% and 70.9%; and dermatologists (92 studies) 0.91 (0.88-0.93), 87.5%, and 81.4%, respectively. Conclusions: DERM has the potential to be used as a decision support tool in primary care, by providing dermatologist-grade recommendation on the likelihood of malignant melanoma.
format	article
author	Michael Phillips Jack Greenhalgh Helen Marsden Ioulios Palamaras
author_facet	Michael Phillips Jack Greenhalgh Helen Marsden Ioulios Palamaras
author_sort	Michael Phillips
title	Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy
title_short	Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy
title_full	Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy
title_fullStr	Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy
title_full_unstemmed	Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy
title_sort	detection of malignant melanoma using artificial intelligence: an observational study of diagnostic accuracy
publisher	Mattioli1885
publishDate	2019
url	https://doaj.org/article/e54e9fb8eaed4e668190606c93d5df7e
work_keys_str_mv	AT michaelphillips detectionofmalignantmelanomausingartificialintelligenceanobservationalstudyofdiagnosticaccuracy AT jackgreenhalgh detectionofmalignantmelanomausingartificialintelligenceanobservationalstudyofdiagnosticaccuracy AT helenmarsden detectionofmalignantmelanomausingartificialintelligenceanobservationalstudyofdiagnosticaccuracy AT iouliospalamaras detectionofmalignantmelanomausingartificialintelligenceanobservationalstudyofdiagnosticaccuracy
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Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy

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