Systematic review of machine learning for diagnosis and prognosis in dermatology

Thomsen, Kenneth; Iversen, Lars; Titlestad, Therese Louise; Winther, Ole

doi:10.1080/09546634.2019.1682500

Cited by 75 publications

(67 citation statements)

References 67 publications

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“…Artificial intelligence, also known as machine learning, is a nonlinear mathematical modeling technology that is used extensively in modern day living, such as email communications, social media, web searching, stores and services, banking and finance, aviation and prediction of machinery failure, maps and directions, criminology, and war. 15,31 Recently, artificial intelligence is being increasingly used in clinical medicine including gastroenterology 32,33 endoscopy, 34 and hepatology, 15 radiology, 35 pathology, 36 dentistry, 37 oncology, 38 cardiology, 39 dermatology, 40 neurosurgery, 41 gynecology, 42 and in medical research, particularly big data analysis. Whereas convolutional neural network is the usual network used for image analysis, 43 feed-forward multilayer perceptron networks are the modeling technique for clinical prediction and have been used in the current study as well.…”

Section: Discussionmentioning

confidence: 99%

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

et al. 2020

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Background and Aim: About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options. Methods: Data of 263 patients (mean age 37.0 AE 14.0-years, 176, 77% male) with acute severe UC admitted during a 12-year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN). Results: Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C-reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower. Conclusion: It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.

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Section: Discussionmentioning

confidence: 99%

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

et al. 2020

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“…(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.…”

Section: Key Summary Pointsmentioning

confidence: 99%

“…Translational research in dermatology is already abundant, with data from the genome, epigenome, transcriptome, proteome, and microbiome, areas of research that are often referred to by the shortened term ''omics'' [2]. Recent advancements in faster processing and cheaper storage have allowed for the development of machine learning (ML) algorithms with human-like intelligence that have numerous applications in dermatology [3][4][5]. To assess the effectiveness of these emerging technologies, it is imperative that dermatologists have a basic understanding of artificial intelligence and ML.…”

Section: Introductionmentioning

confidence: 99%

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

Chan

Reddy

Myers

et al. 2020

Dermatol Ther (Heidelb)

166

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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“…For example, studies have reviewed the specificities and sensitivities of AI tools for melanoma screening (9). To the best of our knowledge, only one systematic review has been published on dermatological applications of AI in general, not limited to neoplastic lesions (10).…”

Section: Reviewsmentioning

confidence: 99%

“…Beyond psoriasis, applications have been described classifying acne, lichen planus, pityriasis lichenoides and dermatomyositis (10,(54)(55)(56)(57)(58). Seite et al developed a smartphone AI tool that grades and classifies types of acne lesions (e.g., comedonal, inflammatory, post inflammatory hyperpigmentation, etc.)…”

Section: Psoriasis and Other Inflammatory Skin Diseasesmentioning

confidence: 99%

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

et al. 2020

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Systematic review of machine learning for diagnosis and prognosis in dermatology

Cited by 75 publications

References 67 publications

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

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

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

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