Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review

Cui, Xiaoyu; Wei, Ran; Gong, Lixin; Qi, Ruiqun; Zhao, Zeyin; Chen, Hong‐Duo; Song, Kexing; Abdulrahman, Amer A.; Wang, Yining; Chen, John Z.S.; Chen, Shuo; Zhao, Yue; Gao, Xiao-Zhi

doi:10.1016/j.jaad.2019.06.042

Cited by 39 publications

(18 citation statements)

References 16 publications

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“…The skin tumor classification system using deep learning showed better results in both six-and two-class classification accuracy than BCDs and TRN dermatologists. Many similar tests have been reported in previous research [3,36,37], and it is considered that the machine learning algorithm has reached dermatologist-level accuracy in skin lesion classification [4,5,36]. In the present study, although the FRCNN and the dermatologists had similar results in terms of sensitivity, false positive rates were BCDs: 13.4%, TRNs: 14.1%, and FRCNN: 5.5%.…”

Section: Discussionsupporting

confidence: 88%

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai¹,

Yamazaki²,

Hirano

et al. 2020

Biomolecules

176

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Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

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Section: Discussionsupporting

confidence: 88%

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai¹,

Yamazaki²,

Hirano

et al. 2020

Biomolecules

176

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“…CSID provides a large amount of skin imaging data for AI training and has facilitated the development of a series of clinical AI projects. AI research has also become more popular in dermatology in China; for example, AI methods were used to classify melanoma using 2,200 dermoscopy images in the Chinese population (19). However, there is currently no research or summary review of the attitudes that Chinese dermatologists' hold towards AI.…”

Section: Introductionmentioning

confidence: 99%

Web-based study on Chinese dermatologists’ attitudes towards artificial intelligence

Shen¹,

Li²,

Wang³

et al. 2020

Ann Transl Med

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Background: Artificial intelligence (AI) has become a powerful tool and is attracting more attention in the field of medicine. There are a number of AI studies focusing on skin diseases, and there are many AI products that have been applied in dermatology. However, the attitudes of dermatologists, specifically those from China, towards AI, is not clear as few, if any studies have focused on this issue.Methods: A web-based questionnaire was designed by experts from the Chinese Skin Image Database (CSID) and published on the UMER Doctor platform (an online learning platform for dermatologists developed by the Shanghai Wheat Color Intelligent Technology Company, China). A total of 1,228 Chinese dermatologists were recruited and provided answers to the questionnaire online. The differences of dermatologists' attitudes towards AI among the different groups (stratified by age, gender, hospital level, education degree, professional title, and hospital ownership) were compared by using the Mann-Whitney U test and the Kruskal-Wallis H test. The correlations between stratified factors and dermatologists' attitudes towards AI were calculated by using the Spearman's rank correlation test. SPSS (version 22.0) was utilized for all analyses. A two-sided P value <0.05 was considered statistically significant in all analyses.Results: A total of 1,228 Chinese dermatologists from 30 provinces, autonomous regions, municipalities, and other regions (including Hong Kong, Macau, and Taiwan) participated in this survey. The dermatologists who participated acquired AI-related information mainly through the Internet, meetings or forums, and 70.51% of participated dermatologists acquired AI-related information by two or more approaches. In total, 99.51% of participated dermatologists pay attention (general, passive-active, and active attention) to information pertaining to AI. Stratified analyses revealed statistically significant differences in their attention levels (unconcerned, general, passive-active, and active attention) to AI-related information by gender, hospital level, education degree, and professional title (P values ≤1.79E−02). In total, 95.36% of the participated dermatologists thought the role of AI to be in "assisting the daily diagnosis and treatment activities for dermatologists". Stratified analyses about the thought of AI roles (unconcerned, useless, assist, and replace) showed that there was no statistically significant difference except for the hospital level (P value =4.09E−03). The correlations between stratified factors with attention levels and the opinions of AI roles showed extremely weak correlations. Furthermore, 64.17% of participated dermatologists thought secondary Original Article on Medical Artificial Intelligent Research Shen et al. Chinese dermatologists' attitudes towards AI

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“…The CNN achieved an overall AUC of [ 91%, which was similar to the average output predications of 21 dermatologists. Many studies since then have leveraged transfer learning to classify lesions into a number of skin cancer classes and determine the probability of malignancy; these studies showed comparable accuracy, AUROC, sensitivity, and/or specificity to board-certified dermatologists or dermatologists in training [19,[31][32][33][34][35][36][37][38][39]. It is also important to note the average dermatologist's diagnostic accuracy (e.g., sensitivity and specificity) when evaluating ML models for general screening.…”

Section: Melanomamentioning

confidence: 99%

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

185

108

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Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

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Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review

Cited by 39 publications

References 16 publications

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Web-based study on Chinese dermatologists’ attitudes towards artificial intelligence

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

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