Automated Dermatological Diagnosis: Hype or Reality?

Navarrete‐Dechent, Cristián; Dusza, Stephen W.; Liopyris, Konstantinos; Marghoob, Ashfaq A.; Halpern, Allan C.; Marchetti, Michael A.

doi:10.1016/j.jid.2018.04.040

Cited by 102 publications

(83 citation statements)

References 8 publications

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“…They found that the top-1 error rate was 71% and the top-5 error rate was 42%. Although the sample size of the images tested by Navarrete-Dechent et al [47] was small, these findings suggest that the sensitivity of the algorithm was significantly lower than had been presented in the original publication [31]. It is known that published algorithms may underperform in less than ideal conditions or when validated through external testing.…”

Section: Melanomamentioning

confidence: 88%

“…The algorithm published by Han et al [31] is one of the few that are publicly available and, consequently, Navarrete-Dechent et al were able to evaluate its generalizability [47]. These authors tested the algorithm with 100 biopsyproven, high-quality images from the International Skin Imaging Collaboration Archive; all 100 images were lesions from Caucasians in the USA.…”

Section: Melanomamentioning

confidence: 99%

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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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Section: Melanomamentioning

confidence: 88%

Section: Melanomamentioning

confidence: 99%

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

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

185

108

View full text Add to dashboard Cite

show abstract

“…For example, Navarrete-Dechent et al took Han et al's established an AI algorithm that was trained with a relatively diverse set of data and tested it in a unique database of Caucasian Americans from the southern United States. They found that the performance was suboptimal compared to how it was reported originally (88). The issue of generalizability is thus not simple to solve and may require either unique or extended data depending on the composition of the population being tested.…”

Section: Generalizabilitymentioning

confidence: 94%

“…Dermatologist collaboration has also been highlighted as essential (6). Systems need to be trained with the full spectrum of human populations and clinical presentations that challenge dermatologists in clinical practice (88). Systems can also benefit from receiving inputs on other metrics available to physicians such as anatomic location, duration of the lesion and images of unaffected skin (88).…”

Section: Next Stepsmentioning

confidence: 99%

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

et al. 2020

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“…The inclusion of metadata containing sociodemographic information about the patient (sex, skin type, race, and age) is thus necessary to verify the presence of biases related to imbalance or underrepresentation (Navarrete-Dechent et al, 2018). When possible, the obvious solution to this problem is to broaden the dataset by including images and data of patients from less represented groups.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images

Cullell-Dalmau

Otero-Viñas

Manzo

2020

Journal of Investigative Dermatology

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Deep learning is a branch of artificial intelligence that uses computational networks inspired by the human brain to extract patterns from raw data. Development and application of deep learning methods for image analysis, including classification, segmentation, and restoration, have accelerated in the last decade. These tools have been progressively incorporated into several research fields, opening new avenues in the analysis of biomedical imaging. Recently, the application of deep learning to dermatological images has shown great potential. Deep learning algorithms have shown performance comparable with humans in classifying skin lesion images into different skin cancer categories. The potential relevance of deep learning to the clinical realm created the need for researchers in disciplines other than computer science to understand its fundamentals. In this paper, we introduce the basics of a deep learning architecture for image classification, the convolutional neural network, in a manner accessible to nonexperts. We explain its fundamental operation, the convolution, and describe the metrics for the evaluation of its performance. These concepts are important to interpret and evaluate scientific publications involving these tools. We also present examples of recent applications for dermatology. We further discuss the capabilities and limitations of these artificial intelligencebased methods.

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Automated Dermatological Diagnosis: Hype or Reality?

Abstract: CONFLICT OF INTEREST WL is employed by SK Telecom. But the company did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Cited by 102 publications

References 8 publications

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

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

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images

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