Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet.

Importance: Artificial intelligence (AI) training for diagnosing dermatologic images requires large amounts of clean data. Dermatologic images have different compositions, and many are inaccessible due to privacy concerns, which hinder the development of AI.

Objective: To build a training data set for discriminative and generative AI from unstandardized internet images of melanoma and nevus.

Evaluation of the Pinhole Method Using Carbon Dioxide Laser on Facial Telangiectasia.

Facial telangiectasias are small, dilated blood vessels frequently located on the face. They are cosmetically disfiguring and require an effective solution. We aimed to investigate the effect of the pinhole method using a carbon dioxide (CO) laser to treat facial telangiectasias.

The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search.

Model Dermatology ( https://modelderm.com ; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets.

Augmenting the accuracy of trainee doctors in diagnosing skin lesions suspected of skin neoplasms in a real-world setting: A prospective controlled before-and-after study.

Background: Although deep neural networks have shown promising results in the diagnosis of skin cancer, a prospective evaluation in a real-world setting could confirm these results. This study aimed to evaluate whether an algorithm (http://b2019.modelderm.

Correction: Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis.

[This corrects the article DOI: 10.1371/journal.pone.

Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study.

Background: The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings.

Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis.

Background: Onychomycosis is the most common nail disorder and is associated with diagnostic challenges. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of this condition. However, comparative studies of the two tools in the diagnosis of onychomycosis have not previously been conducted.

Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders.

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals.

Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network.

Importance: Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results.

Objective: To evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant.

Design, Setting, And Participants: Region-based convolutional neural network technology was used to create 924 538 possible lesions by extracting nodular benign lesions from 182 348 clinical photographs.

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods - 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset.

Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.

We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets.

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models.

Split-face comparative study of 1550 nm fractional photothermolysis and trichloroacetic acid 15% chemical peeling for facial melasma in Asian skin.

Fractional photothermolysis (FP) therapy and chemical peels have been reported to be effective in patients with recalcitrant melasma. However, there is little information to compare the efficacy of single treatment session in Asian women. The aim of this study was to examine the efficacy, long-lasting outcomes and safety of a single session of 1550-nm erbium-doped FP in Asian patients, compared with trichloroacetic acid (TCA) peel with a medium depth.

Q-Switched Nd:YAG Laser Therapy of Acquired Bilateral Nevus of Ota-like Macules.

Background: Acquired bilateral nevus of Ota-like macules (ABNOM) is a dermal pigmented lesion common in individuals of Oriental origin. The Q-switched Nd:YAG laser (QSNYL) has been used successfully to treat a variety of benign, dermal, pigmented lesions, including nevus of Ota lesions. The similarity between ABNOM and nevus of Ota suggested that QSNYL may also be effective in the former.

Increase of integrin α6+p63+ cells after ultraviolet B irradiation in normal human keratinocytes.

Epidermal stem cells (SC) are believed to be resistant to environmental damage for the purpose of self renewal. Most promising SC markers include integrin α6 and p63. The aim of our study was to determine whether the integrin α6+p63+ cell fraction representative of the epidermal progenitor or SC is increased after ultraviolet B (UVB) irradiation and to clarify the hypothesis that epidermal SC are resistant to high-dose UVB damage.