Physician Level Assessment of Hirsute Women and of Their Eligibility for Laser Treatment With Deep Learning.

OBJECTIVES: Hirsutism is a widespread condition affecting 5%-15% of females. Laser treatment of hirsutism has the best long-term effect. Patients with nonpigmented or nonterminal hairs are not eligible for laser treatment, and the current patient journey needed to establish eligibility for laser hair removal is problematic in many health-care systems. METHODS: In this study, we compared the ability to assess eligibility for laser hair removal of health-care professionals and convolutional neural network (CNN)-based models. RESULTS: The CNN ensemble model, synthesized from the outputs of five individual CNN models, reached an eligibility assessment accuracy of 0.52 (95% CI: 0.42-0.60) and a κ of 0.20 (95% CI: 0.13-0.27), taking a consensus expert label as reference. For comparison, board-certified dermatologists achieved a mean accuracy of 0.48 (95% CI: 0.44-0.52) and a mean κ of 0.26 (95% CI: 0.22-0.31). Intra-rater analysis of board-certified dermatologists yielded κ in the 0.32 (95% CI: 0.24-0.40) and 0.65 (95% CI: 0.56-0.74) range. CONCLUSION: Current assessment of eligibility for laser hair removal is challenging. Developing a laser hair removal eligibility assessment tool based on deep learning that performs on a par with trained dermatologists is feasible. Such a model may potentially reduce workload, increase quality and effectiveness, and facilitate equal health-care access. However, to achieve true clinical generalizability, prospective randomized clinical intervention studies are needed.

[Artificial intelligence in diagnosing skin diseases].

Dermatology is a visual speciality suited for implementation of computer-aided diagnostic (CAD) systems as summarised in this review. There has been great progress in CAD melanoma detection, whereas the detection of multiple lesion skin diseases has proved more difficult. We need data on clinical implementation of CAD systems in order to know, how data from studies can be extrapolated to real-world clinical settings. Good clinical test designs and common standards for reporting and monitoring efficacy are needed. Implementation of CAD in the best possible way will be a challenge for health systems and clinicians in the coming years.

Systematic review of machine learning for diagnosis and prognosis in dermatology.

Background: Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential for using such systems in dermatology.Objective: To evaluate the ways in which machine learning has been utilized in dermatology to date and provide an overview of the findings in current literature on the subject.Methods: We conducted a systematic review of existing literature, identifying the literature through a systematic search of the PubMed database. Two doctors assessed screening and eligibility with respect to pre-determined inclusion and exclusion criteria.Results: A total of 2175 publications were identified, and 64 publications were included. We identified eight major categories where machine learning tools were tested in dermatology. Most systems involved image recognition tools that were primarily aimed at binary classification of malignant melanoma (MM). Short system descriptions and results of all included systems are presented in tables.Conclusions: We present a complete overview of artificial intelligence implemented in dermatology. Impressive outcomes were reported in all of the identified eight categories, but head-to-head comparison proved difficult. The many areas of dermatology where we identified machine learning tools indicate the diversity of machine learning.

Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases.

Background: Diagnosis of skin diseases is often challenging and computer-aided diagnostic tools are urgently needed to underpin decision making. Objective: To develop a convolutional neural network model to classify clinically relevant selected multiple-lesion skin diseases, this in accordance to the STARD guidelines. Methods: This was an image-based retrospective study using multi-task learning for binary classification. A VGG-16 model was trained on 16,543 non-standardized images. Image data was distributed in training set (80%), validation set (10%), and test set (10%). All images were collected from a clinical database of a Danish population attending one dermatological department. Included was patients categorized with ICD-10 codes related to acne, rosacea, psoriasis, eczema, and cutaneous t-cell lymphoma. Results: Acne was distinguished from rosacea with a sensitivity of 85.42% CI 72.24-93.93% and a specificity of 89.53% CI 83.97-93.68%, cutaneous t-cell lymphoma was distinguished from eczema with a sensitivity of 74.29% CI 67.82-80.05% and a specificity of 84.09% CI 80.83-86.99%, and psoriasis from eczema with a sensitivity of 81.79% CI 78.51-84.76% and a specificity of 73.57% CI 69.76-77.13%. All results were based on the test set. Conclusion: The performance rates reported were equal or superior to those reported for general practitioners with dermatological training, indicating that computer-aided diagnostic models based on convolutional neural network may potentially be employed for diagnosing multiple-lesion skin diseases.

Severe Infantile Bullous Pemphigoid Treated with Dapsone after Bridging with Systemic Glucocorticoid.

We present a case of severe and treatment-refractory bullous pemphigoid in a 3-month-old child. After topical and systemic corticoid treatment proved inefficient, dapsone 0.75 mg/kg was added initially without success. Disease control was reached with dapsone 1.5 mg/kg in addition to both topical and systemic glucocorticoid treatment, leaving the child with several side effects of the glucocorticoid treatment.

Bullous Pemphigoid as an Adverse Reaction to Pembrolizumab: Two Case Reports.

Checkpoint inhibitors are novel and promising treatment options for different types of cancer. Programmed cell death 1 (PD-1) inhibitors, such as pembrolizumab, have been shown to significantly raise the survival rates of disseminated malignant melanoma (MM). Autoimmune adverse reactions are very common in checkpoint inhibitors. We present 2 cases of bullous pemphigoid, as adverse reactions to pembrolizumab-treated MM.

Exploring Valrubicin's Effect on Propionibacterium Acnes-Induced Skin Inflammation in Vitro and in Vivo.

Acne is a common skin disease involving colonization with Propionibacterium acnes (P. acnes), hyperproliferation of the follicular epithelium and inflammatory events. Valrubicin is a second-generation anthracycline, non-toxic upon contact, and available in a topical formulation. Valrubicin's predecessor doxorubicin possesses antibacterial effects and previously we demonstrated that valrubicin inhibits keratinocyte proliferation and skin inflammation suggesting beneficial topical treatment of acne with valrubicin. This study aims to investigate valrubicin's possible use in acne treatment by testing valrubicin's antibacterial effects against P. acnes and P. acnes-induced skin inflammation in vitro and in vivo. Valrubicin was demonstrated not to possess antibacterial effects against P. acnes. Additionally, valrubicin was demonstrated not to reduce mRNA and protein expression levels of the inflammatory markers interleukin (IL)-1ß, IL-8, and tumor necrosis factor (TNF)-α in vitro in human keratinocytes co-cultured with P. acnes. Moreover, in vivo, valrubicin, applied both topically and intra-dermally, was not able to reduce signs of inflammation in mouse ears intra-dermally injected with P. acnes. Taken together, this study does not support beneficial antibacterial and anti inflammatory effects of topical valrubicin treatment of acne.