Automated Machine Learning (AutoML) for the Diagnosis of Melanoma Skin Lesions From Consumer-Grade Camera Photos.

Autor:	Potluru A; Dermatology, National Health Service (NHS) Greater Glasgow and Clyde, Edinburgh, GBR., Arora A; Clinical Medicine, University of Cambridge, Cambridge, GBR., Arora A; Clinical Medicine, University of Cambridge, Cambridge, GBR., Aslam Joiya S; Trauma and Orthopaedics, Yeovil District Hospital, Yeovil, GBR.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2024 Aug 23; Vol. 16 (8), pp. e67559. Date of Electronic Publication: 2024 Aug 23 (Print Publication: 2024).
DOI:	10.7759/cureus.67559
Abstrakt:	Background: In recent years, there has been much speculation about the role of artificial intelligence (AI) and machine learning in dermatology. Advances in computer vision have increased the potential for automated diagnosis of images. However, there remains a gap between the technological development of the algorithms and their real-world implementation. This study aims to develop and test an automated machine learning (AutoML) algorithm for the diagnosis of melanoma, with no technical or coding skills required by the operator. Methods: The Skin Cancer Detection Dataset from the University of Waterloo Vision and Image Processing Lab contains 206 images sourced from the public databases DermIS and DermQuest. The dataset was split into two groups: training data (n=174) and testing data (n=32). A machine learning algorithm was created using 'Teachable Machine', trained on the training data, to differentiate between melanoma and non-melanoma skin lesions. Results: The AutoML algorithm identified 12/14 non-melanoma images and 15/18 melanoma images in the testing dataset. The overall accuracy was 84.4%, with a sensitivity of 83.3% and a specificity of 85.7%. Conclusions: Existing literature has tested a range of different machine learning algorithms on the same dataset. These have often required expertise in machine learning and the ability to code. The results of this study, using a no-code tool, perform comparably to existing efforts and suggest that there is potential for future clinical AI algorithms to be developed by doctors even without any technical expertise as long as they have access to relevant local data. Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Potluru et al.)
Databáze:	MEDLINE
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