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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 (CO2) laser to treat facial telangiectasias. This study included 155 facial telangiectasia lesions in 72 patients who visited the Kangnam Sacred Heart Hospital, Hallym University. Treatment efficacy and improvement were evaluated by quantitative measurements performed by two trained evaluators who assessed the percentage of residual lesion length using the same tape measure. Lesions were evaluated before laser therapy and 1, 3, and 6 months after the first treatment. Based on the initial lesion length (100%), the average percentages of the residual length at 1, 3, and 6 months were 48.26% (p < 0.01), 4.25% (p < 0.01), and 1.41% (p < 0.01), respectively. Complications were evaluated using the Patient and Observer Scar Assessment Scale (POSAS). The average POSAS scores improved from 46.09 at the first visit to 23.42 (p < 0.01), and 15.24 (p < 0.01) at the 3- and 6-month follow-up. No recurrence was noted at the 6-month follow-up. CO2 laser treatment using the pinhole method to treat facial telangiectasias is a safe, inexpensive, and effective treatment that provides patients with excellent aesthetic satisfaction.

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. Consecutive images from an Internet skin cancer community (‘RD’ dataset, 1,282 images posted between 25 January 2020 to 30 July 2021; https://reddit.com/r/melanoma) were analyzed retrospectively, along with hospital datasets (Edinburgh dataset, 1,300 images; SNU dataset, 2,101 images; TeleDerm dataset, 340 consecutive images). The algorithm’s performance was equivalent to that of dermatologists in the curated clinical datasets (Edinburgh and SNU datasets). However, its performance deteriorated in the RD and TeleDerm datasets because of insufficient image quality and the presence of out-of-distribution disorders, respectively. For the RD dataset, the algorithm’s Top-1/3 accuracy (39.2%/67.2%) and AUC (0.800) were equivalent to that of general physicians (36.8%/52.9%). It was more accurate than that of the laypersons using random Internet searches (19.2%/24.4%). The Top-1/3 accuracy was affected by inadequate image quality (adequate = 43.2%/71.3% versus inadequate = 32.9%/60.8%), whereas participant performance did not deteriorate (adequate = 35.8%/52.7% vs. inadequate = 38.4%/53.3%). In this report, the algorithm performance was significantly affected by the change of the intended settings, which implies that AI algorithms at dermatologist-level, in-distribution setting, may not be able to show the same level of performance in with out-of-distribution settings.

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. The area under the curves for malignancy detection were 0.928 ± 0.002 (Edinburgh) and 0.937 ± 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 ± 1.6% and 92.0 ± 1.1% (Edinburgh) and 44.8 ± 1.2% and 78.1 ± 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P0.0001) and 1.1% (P0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P = 0.045) and 10.1% (P = 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

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.com) improves the accuracy of nondermatologists in diagnosing skin neoplasms. Methods A total of 285 cases (random series) with skin neoplasms suspected of malignancy by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean [SD] age, 57.0 [17.7] years; 62 [43.1%] men) was diagnosed via routine examination with photographic review and assistance by the algorithm, whereas the control group (141 cases, mean [SD] age, 61.0 [15.3] years; 52 [36.9%] men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared. Results Among the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance of AI was higher than that before the assistance (46.5%, P = .008). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P < .001). In the control group, the difference in the Top-1 accuracy between before and after reviewing photographs was not significant (before, 46.1%; after, 51.8%; P = .19), and the number of differential diagnoses did not significantly increase (before, 2.0 ± 0.4; after, 2.1 ± 0.5; P = .57). Conclusions In real-world settings, AI augmented the diagnostic accuracy of trainee doctors. The limitation of this study is that the algorithm was tested only for Asians recruited from a single region. Additional international randomized controlled trials involving various ethnicities are required.

This was a single-center, unmasked, paralleled, randomized controlled trial.A randomized trial was conducted in a tertiary care institute in South Korea to validate whether artificial intelligence (AI) could augment the accuracy of nonexpert physicians in the real-world settings, which included diverse out-of-distribution conditions. Consecutive patients aged19 years, having one or more skin lesions suspicious for skin cancer detected by either the patient or physician, were randomly allocated to four nondermatology trainees and four dermatology residents. The attending dermatologists examined the randomly allocated patients with (AI-assisted group) or without (unaided group) the real-time assistance of AI algorithm (https://b2020.modelderm.com#world; convolutional neural networks; unmasked design) after simple randomization of the patients.Using 576 consecutive cases (Fitzpatrick skin phototypes III or IV) with suspicious lesions out of the initial 603 recruitments, the accuracy of the AI-assisted group (n = 295, 53.9%) was found to be significantly higher than those of the unaided group (n = 281, 43.8%; P = 0.019). Whereas the augmentation was more significant from 54.7% (n = 150) to 30.7% (n = 138; P0.0001) in the nondermatology trainees who had the least experience in dermatology, it was not significant in the dermatology residents. The algorithm could help trainees in the AI-assisted group include more differential diagnoses than the unaided group (2.09 vs. 1.95 diagnoses; P = 0.0005). However, a 12.2% drop in Top-1 accuracy of the trainees was observed in cases in which all Top-3 predictions given by the algorithm were incorrect.The multiclass AI algorithm augmented the diagnostic accuracy of nonexpert physicians in dermatology.

BackgroundOnychomycosis 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.ObjectivesThis study evaluated the diagnostic abilities of a deep neural network (http://nail.modelderm.com) and dermoscopic examination in patients with onychomycosis.MethodsA prospective observational study was performed in patients presenting with dystrophic features in the toenails. Clinical photographs were taken by research assistants, and the ground truth was determined either by direct microscopy using the potassium hydroxide test or by fungal culture. Five board-certified dermatologists determined a diagnosis of onychomycosis using the clinical photographs. The diagnosis was also made using the algorithm and dermoscopic examination.ResultsA total of 90 patients (mean age, 55.3; male, 43.3%) assessed between September 2018 and July 2019 were included in the analysis. The detection of onychomycosis using the algorithm (AUC, 0.751; 95% CI, 0.646-0.856) and that by dermoscopy (AUC, 0.755; 95% CI, 0.654-0.855) were seen to be comparable (Delong's test; P = 0.952). The sensitivity and specificity of the algorithm at the operating point were 70.2% and 72.7%, respectively. The sensitivity and specificity of diagnosis by the five dermatologists were 73.0% and 49.7%, respectively. The Youden index of the algorithm (0.429) was also comparable to that of the dermatologists' diagnosis (0.230±0.176; Wilcoxon rank-sum test; P = 0.667).ConclusionsAs a standalone method, the algorithm analyzed photographs taken by non-physician and showed comparable accuracy for the diagnosis of onychomycosis to that made by experienced dermatologists and by dermoscopic examination. Large sample size and world-wide, multicentered studies should be investigated to prove the performance of the algorithm.

BACKGROUND Although deep neural networks have shown promising results in diagnosing skin cancer, a prospective evaluation in a real-world setting could confirm these results. OBJECTIVE The aim of this study was to evaluate whether an algorithm (http://b2019.modelderm.com) could improve the accuracy of nondermatologists in diagnosing skin neoplasms. METHODS A total of 285 cases (random series) with skin neoplasms suspected by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean [SD] age, 57.0 [17.7] years; 62 [43.1%] men) was diagnosed via routine examination with capturing photographs and assisted by the algorithm, whereas the control group (141 cases, mean [SD] age, 61.0 [15.3] years; 52 [36.9%] men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared. RESULTS Among the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance was higher than that before the assistance (46.5%, P = 0.0081). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P < 0.0001). In the control group, the difference in the Top-1 accuracy between before and after reviewing photographs was not significant (before, 46.1%; after, 51.8%; P = 0.1867) and the number of differential diagnoses was not also significantly increased (before, 2.0 ± 0.4; after, 2.1 ± 0.5; P = 0.5653). CONCLUSIONS In real-world settings, artificial intelligence augmented the diagnostic accuracy of trainee doctors. The limitation of this study is that the algorithm was tested only for Asians recruited from a single region. Additional international randomized controlled trials involving various ethnicities are required.

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. Methods and findings To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation [SD], 52.1 [18.3]; 4,701 men [45.1%]) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. In addition, the results obtained by the algorithm for the data of randomly selected batches of 30 patients were compared with those obtained by 44 dermatologists in experimental settings; the dermatologists were only provided with multiple images of each lesion, without clinical information. With regard to the determination of malignancy, the area under the curve (AUC) achieved by the algorithm was 0.863 (95% confidence interval [CI] 0.852–0.875), when unprocessed clinical photographs were used. The sensitivity and specificity of the algorithm at the predefined high-specificity threshold were 62.7% (95% CI 59.9–65.1) and 90.0% (95% CI 89.4–90.6), respectively. Furthermore, the sensitivity and specificity of the first clinical impression of 65 attending physicians were 70.2% and 95.6%, respectively, which were superior to those of the algorithm (McNemar test; p < 0.0001). The positive and negative predictive values of the algorithm were 45.4% (CI 43.7–47.3) and 94.8% (CI 94.4–95.2), respectively, whereas those of the first clinical impression were 68.1% and 96.0%, respectively. In the reader test conducted using images corresponding to batches of 30 patients, the sensitivity and specificity of the algorithm at the predefined threshold were 66.9% (95% CI 57.7–76.0) and 87.4% (95% CI 82.5–92.2), respectively. Furthermore, the sensitivity and specificity derived from the first impression of 44 of the participants were 65.8% (95% CI 55.7–75.9) and 85.7% (95% CI 82.4–88.9), respectively, which are values comparable with those of the algorithm (Wilcoxon signed-rank test; p = 0.607 and 0.097). Limitations of this study include the exclusive use of high-quality clinical photographs taken in hospitals and the lack of ethnic diversity in the study population. Conclusions Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information., Seung Seog Han and colleagues compare the performance of a neural network algorithm with that of dermatologists in diagnosis of skin neoplasms., Author summary Why was this study done? The diagnostic performance of artificial intelligence based on deep learning algorithms has been demonstrated to be superior to or at least comparable with that of dermatologists. However, the difference in diagnostic efficiency between algorithms and dermatologists was determined using experimental reader tests with limited clinical information related to the photographed skin abnormalities. Most studies performed internal validation, which indicates that both the training and validation images were selected from the same source. In addition, only a small number of disorders have been validated in the previous studies. Thus, practical limitations and biases have complicated translation to actual practices. What did the researchers do and find? The performance of the neural network algorithm was compared with that of standard dermatologic practice for diagnosing almost all types of skin neoplasms on a large scale. The algorithm could successfully screen malignancy, without lesion preselection by a dermatologist. Under experimental settings, in which only images were provided for diagnosis, the performance of the algorithm was comparable with that of the 44 dermatologists who performed the reader test. However, the performance of the algorithm was inferior to that of the attending physicians who actually consulted with patients. This highlights the value of clinical data, in addition to visual findings, for accurate diagnosis of cutaneous neoplasms. What do these findings mean? Given photographs of abnormal skin findings, the algorithm can work ceaselessly to determine the need for dermatologic consultation at a performance level comparable with that of dermatologists. To further improve the algorithm’s performance, metadata, such as past medical history, should be integrated with the clinical images.

BACKGROUND: The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not been yet tested in real-life conditions. OBJECTIVE: To assess the diagnostic performance and potential clinical utility of a 174-multiclass AI algorithm in a real-life telemedicine setting. METHODS: Prospective, diagnostic accuracy study including consecutive patients who submitted images for teledermatology evaluation. The treating dermatologist chose a single image to upload to a web application during teleconsultation. A follow-up reader study including nine healthcare providers (3 dermatologists, 3 dermatology residents and 3 general practitioners) was performed. RESULTS: A total of 340 cases from 281 patients met study inclusion criteria. The mean (SD) age of patients was 33.7 (17.5) years; 63% (n = 177) were female. Exposure to the AI algorithm results was considered useful in 11.8% of visits (n = 40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n = 2) of cases. The overall top-1 accuracy of the algorithm (41.2%) was lower than that of the dermatologists (60.1%), residents (57.8%) and general practitioners (49.3%) (all comparisons P < 0.05, in the reader study). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained, the balanced top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P = 0.049). Algorithm performance was associated with patient skin type and image quality. CONCLUSIONS: A 174-disease class AI algorithm appears to be a promising tool in the triage and evaluation of lesions with patient-taken photographs via telemedicine.

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. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.

BACKGROUNDThe aim of this study was to validate the performance of algorithm (http://rcnn.modelderm.com) for the diagnosis of benign and malignant skin tumors.METHODSWith external validation dataset (43 disorders; 40,331 clinical images from 10,426 patients; January 1, 2008 – March 31, 2019), we compared the prediction of algorithm with the clinical diagnosis of 65 attending physicians at the time of biopsy request.RESULTSFor binary-task classification of determining malignancy, the AUC of the algorithm was 0.863(95% CI 0.852-0.875) with unprocessed clinical photographs. The sensitivity/specificity of the algorithm at the predefined high-sensitivity and high-specificity threshold were 79.1%(76.9-81.4)/76.9%(76.1-77.8) and 62.7%(59.9- 65.5)/90.0%(89.4-90.6), respectively. The sensitivity/specificity calculated by the clinical diagnosis of attending physicians were 88.1%/83.8%(Top-3) and 70.2%/95.6%(Top-1), which were superior to those of algorithm.For multi-task classification, the mean Top-1,2,3 accuracies of the algorithm were 42.6±20.7%, 56.1±22.8%, 61.9±22.9%, and those of clinical diagnosis were 65.4±17.7%, 73.9±16.6%, 74.7±16.6%, respectively.In the reader test with images from 30-patients batches, the sensitivity / specificity of the algorithm at the predefined threshold were 66.9%±30.2% / 87.4%±16.5%. The sensitivity / specificity derived from the first diagnosis of 44 the participants were 65.8%±33.3% / 85.7%±11.0%, which were comparable with those of the algorithm (Wilcoxon signed-rank test; P=0.61 / 0.097).CONCLUSIONSOur algorithm could diagnose skin tumors at dermatologist-level when diagnosis was made solely with photographs, demonstrating its potential as a mass screening tool in telemedicine setting. However, due to limited data relevancy, the performance was inferior to that of actual medical examination. Clinical information should be integrated with imaging information to achieve more accurate predictions.

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.To evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant.Region-based convolutional neural network technology was used to create 924 538 possible lesions by extracting nodular benign lesions from 182 348 clinical photographs. After manually or automatically annotating these possible lesions based on image findings, convolutional neural networks were trained with 1 106 886 image crops to locate and diagnose cancer. Validation data sets (2844 images from 673 patients; mean [SD] age, 58.2 [19.9] years; 308 men [45.8%]; 185 patients with malignant tumors, 305 with benign tumors, and 183 free of tumor) were obtained from 3 hospitals between January 1, 2010, and September 30, 2018.The area under the receiver operating characteristic curve, F1 score (mean of precision and recall; range, 0.000-1.000), and Youden index score (sensitivity + specificity -1; 0%-100%) were used to compare the performance of the algorithm with that of the participants.The algorithm analyzed a mean (SD) of 4.2 (2.4) photographs per patient and reported the malignancy score according to the highest malignancy output. The area under the receiver operating characteristic curve for the validation data set (673 patients) was 0.910. At a high-sensitivity cutoff threshold, the sensitivity and specificity of the model with the 673 patients were 76.8% and 90.6%, respectively. With the test partition (325 images; 80 patients), the performance of the algorithm was compared with the performance of 13 board-certified dermatologists, 34 dermatology residents, 20 nondermatologic physicians, and 52 members of the general public with no medical background. When the disease screening performance was evaluated at high sensitivity areas using the F1 score and Youden index score, the algorithm showed a higher F1 score (0.831 vs 0.653 [0.126], P .001) and Youden index score (0.675 vs 0.417 [0.124], P .001) than that of nondermatologic physicians. The accuracy of the algorithm was comparable with that of dermatologists (F1 score, 0.831 vs 0.835 [0.040]; Youden index score, 0.675 vs 0.671 [0.100]).The results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.

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. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated. Results — The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65–86% top-1 accuracy, 0.90–0.98 AUC, 0.88/0.83–0.97/0.94 sensitivity/specificity, and 0.71–0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures. Interpretation — The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments.

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. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

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. Eighteen Korean women (Fitzpatrick skin type III or IV) with moderate-to-severe bilateral melasma were randomly treated with a single session of 1550-nm FP on one cheek, and with a 15% TCA peel on the other cheek. Outcome measures included an objective melasma area severity index and subjective patient-rated overall improvement at 4 and 12 weeks after treatment. Melasma lesions were significantly improved 4 weeks after either treatment, but melasma recurred at 12 weeks. Post-inflammatory hyperpigmentation developed in 28% of patients at 4 weeks but resolved in all but one patient by 12 weeks. There was no difference between FP treatment and TCA peeling with respect to any outcome measure. FP laser and TCA peel treatments were equally effective and safe when used to treat moderate-to-severe melasma, but neither treatment was long-lasting. We suggest that multiple or periodic maintenance treatments and/or supplemental procedures may be required for the successful treatment of melasma in Asian women.

Background Epidermal growth factor receptor (EGFR) critically regulates tumour cell division, survival and metastasis. Agents that inhibit EGFR have been used in the treatment of advanced-stage malignancies, but cause variable cutaneous side-effects, most often papulopustular eruptions and xerosis. Objectives We assayed expression of inflammatory cytokines [interleukin (IL)-1alpha, tumour necrosis factor (TNF)-alpha, interferon (IFN)-gamma, human leucocyte antigen (HLA)-DR and intercellular adhesion molecule (ICAM)-1], differentiation markers (filaggrin, involucrin and loricrin) and phosphorylated EGFRs (pEGFRs) in papulopustular eruptions to determine the association between these markers and the eruptions caused by cetuximab. Patients/methods Twelve papulopustular lesion biopsies were selected from patients with colon cancer who had received cetuximab treatment. Immunohistochemistry and immunofluorescence with a confocal laser scanning microscopy were performed. Results Filaggrin expression decreased and expression of involucrin, various inflammatory markers (IL-1alpha, TNF-alpha, ICAM-1 and HLA-DR) increased and the expression of pEGFR was markedly downregulated in papulopustular eruptions. In perilesions, decreased pEGFR expression was noted in hair follicles compared with interfollicular epidermis. The increase of IL-1alpha and TNF-alpha was observed in perilesions as in the lesions. Conclusions The early inflammatory events (IL-1alpha and TNF-alpha expression) seen, and the lack of pEGFR in perilesional follicles, indicate that inflammatory events induced by EGFR inhibition may initiate papulopustular eruptions along with the altered differentiations. The decrease of filaggrin may contribute to the pathogenesis of the xerosis caused by cetuximab.

Summary Background Epithelioid sarcoma is a rare mesenchymal neoplasm of unknown histogenesis. Data on genome-wide surveys for chromosomal aberrations in epithelioid sarcoma are limited. Objectives To investigate genetic aberrations in epithelioid sarcoma. Methods We analysed seven cases of epithelioid sarcoma (classic type, three cases and proximal type, four cases) by comparative genomic hybridization (CGH), and correlated findings with the results of additional immunohistochemical study. Results and conclusions CGH analysis showed DNA copy number changes at one to five different genomic sites in six of seven cases (86%). The majority of the changes were gains. The most frequent gain was at 22q (six cases). Other recurrent changes include gains of 12q24-qter (four cases), 17 (four cases), and 5q32-qter (three cases). High-level homology was seen in chromosomal aberration in both types. In addition, expression of interleukin-2 receptorβ, located in 22q, was revealed by immunohistochemical method in six cases with gain of 22q, suggesting it may play a role in epithelioid sarcoma tumorigenesis.

Epidermal stem cells (SC) are believed to be resistant to environmental damage for the purpose of self renewal. Most promising SC markers include integrin a6 and p63. The aim of our study was to determine whether the integrin a6+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. We irradiated early passage normal human keratinocytes (NHK) with 0, 25, 50, and 100 mJ/cm2 UVB. The percentage of cell death was calculated. Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) and western blotting analyses were performed to identify integrin a6 and p63, and flow cytometry analysis with integrin a6 and p63 antibodies was done. After 50 and 100 mJ/cm2 UVB, integrin a6+p63+cells were found to be much increased by fluorescence-activated cell sorting. Expression of integrin a6 and p63 was increased in NHK after UVB irradiation, which was shown with real-time RT-PCR and western blotting analyses. We concluded that an increase of integrin a6+p63+ cells after high-dose UVB may suggest that the putative progenitor or SC are resistant to UVB irradiation.

Summary Background Pruritus in patients with psoriasis has been reported to be more common than previously thought. Objectives To determine the actual prevalence of pruritus in psoriasis according to severity of psoriasis and to verify the hypothesis of involvement of neuropeptides and their receptors in psoriatic pruritus. Methods We analysed questionnaire replies from 152 patients with chronic plaque-type psoriasis and we assayed the expression of neuropeptides and their receptors in lesional skin biopsies obtained from psoriatic patients with pruritus compared with those from psoriatic patients without pruritus, nonlesional skin of patients with pruritic psoriasis and normal controls by confocal laser scanning microscopy. Results Of the 152 patients with psoriasis, 112 (73·7%) had pruritus, and these patients had a higher mean Psoriasis Area and Severity Index (PASI) score than psoriatic patients without pruritus. There was positive correlation between the PASI score and the intensity of pruritus. Keratinocytes in the psoriatic plaques of patients with pruritus showed consistently increased expression of substance P receptor (SPR), high-affinity nerve growth factor receptor (TrkA) and calcitonin gene-related peptide receptor (CGRPR). Conclusions Pruritus is a common feature in psoriasis. Considering the well-known roles of neuropeptides in pathogenesis of both psoriasis and pruritus, increased SPR, TrkA and CGRPR may be involved in the pathogenesis of pruritus in psoriasis and in the severity of psoriasis.

Eccrine angiomatous hamartoma (EAH) is a rare, benign condition characterized histologically by increased numbers of eccrine elements, as well as numerous capillary channels. In most cases, EAH arises as a single lesion; however, multiple variants have been reported. We report a 35-year-old female patient with multiple, sudoriparous, subcutaneous nodules on the right foot, which showed typical histopathological findings of EAH, and vascular components of the tumor consisted of thin-walled dilated vascular spaces intermixed with spindle cells and some histiocytoid endothelial cells representing spindle cell hemangioma (SCH). To our knowledge, the co-existence of EAH with SCH is a novel finding and not yet described.

A 26-year-old white woman presented with a 15-month history of a slowly enlarging papule involving the proximal nail fold of her left thumb. She gave no history of trauma to the region and denied any ulceration or bleeding. Although the lesion had caused disfigurement of her nail, she sought medical evaluation only because the lesion had recently become slightly painful on palpation. Physical examination revealed a 4-mm, smooth, hyperpigmented papule overlying the medial proximal nail fold of the left thumb (Figure 1). The lesion was soft, with a gray-pink hue, and slightly tender to palpation. Longitudinal grooving of the nail plate was observed distal to the papule (Figure 2). A skin biopsy specimen was obtained (Figure 3 and Figure 4). What is your diagnosis?

Dear Editor: Congenital melanocytic nevus (CMN) is a melanocytic nevus that is either present at birth or appears during the latter stages of infancy1. Nevus sebaceous has been described as the hamartomatous locus of an embryologically defective pilosebaceous unit2. Here, we describe how we used the pinhole technique with an erbium-doped yttrium aluminium garnet (erbium : YAG) laser to treat nevi lesions in different patients. A 25-year-old male was diagnosed with CMN on his nose at birth (Fig. 1A). Emla cream (AstraZeneca AB, Sodertalje, Sweden) was applied to the treatment area prior to starting the procedure. The erbium : YAG laser (Action Er : YAG; Lutronic Corporation, Goyang, Korea) was used in continuous-wave mode, with 1-mm spot size and output power of 0.2 mJ/cm2. Multiple small holes measuring 1 mm in diameter were made on the periphery of the CMN lesion. A hydrocolloid dressing (DuoDERM Extra Thin CGF dressing; ConvaTec, ER Squibb & Sons, Princeton, NJ, USA) was applied to the lesions for ≥2 weeks after treatment. After performing 5 laser sessions at 4-week intervals, the cutaneous lesions resolved. There was no evidence of recurrence at the 1-year follow-up examination (Fig. 1B). Fig. 1 Congenital melanocytic nevus (CMN) in a 25-year-old man. (A) Before treatment: a solitary, 1-cm brown patch is visible on the right alar area of the nose. (B) One year after the final treatment session, the skin lesions resolved with minimal scarring. ... A 5-month-old infant was referred to our outpatient unit for CMN on the forearm (Fig. 1C). We performed the pinhole method of treatment using the erbium : YAG laser (Lutronic) in continuous-wave mode, with 1-mm spot size and output power of 0.2 mJ/cm2 (Fig. 1D). After receiving 6 treatment sessions at 2-month intervals, slight superficial pigmentation persisted (Fig. 1E). A 40-year-old woman presented with nevus sebaceous lesions on the right side of her forehead (Fig. 2A). We performed the pinhole method of treatment using the erbium : YAG laser (Lutronic) in continuous-wave mode, with 1-mm spot size and output power of 0.2 mJ/cm2. After receiving 5 treatment sessions at 4-week intervals, the patient showed marked improvement with only mild hypopigmentation (Fig. 2B). No recurrence was noted at the 6-month follow-up examination (Fig. 2C). Fig. 2 Nevus sebaceous lesions in a 40-year-old woman. (A) Before treatment, two 2.5-cm, well-demarcated, yellow, verrucous plaques were visible on the right side of the forehead. (B) Deep line- and doughnut-shaped holes were created to remove the nevus sebaceus ... Herein, we described how nevi lesions were effectively treated using the pinhole method. This simple method involves making multiple small holes at 2~5-mm intervals using a CO2 laser that penetrates the epidermis and deeper dermis3. The holes produced using the pinhole method are surrounded by untreated areas that promote rapid epidermal repair, thereby reducing the downtime and likelihood of side effects4. The other advantages of performing the pinhole method with ablative lasers are that this laser is easy to use and inexpensive, as ablative lasers are usually available in most dermatology clinics. To the best of our knowledge, this is the first report on the application of the pinhole method for the treatment of CMN and nevus sebaceous, so it is difficult to compare the advantages and disadvantages of the pinhole method with those of conventional treatment methods. Therefore, it is necessary to conduct further studies with a large sample to confirm the effectiveness and safety of the pinhole method. In conclusion, we suggest using the pinhole method with erbium : YAG laser as an alternative therapy for treating nevi lesions.

Dear Editor: Cafe au lait macules (CALMs) are benign epidermal pigmented lesions that can be idiopathic or associated with neurocutaneous syndromes, whereas facial telangiectasia is characterized by small dilated vessels that are visible on the surface of the skin. A 15-year-old boy presented with a CALM on his cheek (Fig. 1A). We performed 6 sessions of pinhole treatment every 4 weeks using erbium : YAG laser (Er : YAG Action; Lutronic, Goyang, Korea) set to a continuous wave mode with a spot size of 1 mm and a fluence of 0.2 mJ/cm2. The lesion showed marked improvement with mild erythema, and there was no recurrence at the 12-month follow-up (Fig. 1B). Fig. 1 A cafe au lait macule in a 15-year-old boy. (A) Before treatment, a 4×0.5-cm brown patch was evident on the patient's cheek. (B) This lesion showed marked improvement with only mild erythema at 1 year after final treatment (6 treatment ... A 55-year-old female presented with a 10-year history of telangiectasia on the right cheek (Fig. 2A). The telangiectasia was treated using the pinhole method using a CO2 laser (UM-L25; Union Medical Engineering, Uijeongbu, Korea) in continuous wave mode; the parameters were 1-mm spot size and 1.0-W output power. Multiple small holes, measuring 1 mm in diameter, were made down to the papillary dermis. These holes were made approximately 3 mm apart all over the telangiectasia area (Fig. 2B). The telangiectasia showed significant improvement after 1 treatment session (Fig. 2C). No recurrence was noted at the 3-month follow-up. Fig. 2 Facial telangiectasia in a 55-year-old woman. (A) Telangiectasia on the right cheek before treatment. (B) Multiple deep holes were made in line with the course of the vessels. (C) The telangiectasia showed significant improvement 1 month after 1 treatment ... We used 2 different ablative lasers, erbium : YAG and CO2 laser in the treatment of CALM and telangiectasia. The CO2 laser is used in cases where there is a concern about bleeding, whereas the erbium : YAG laser is used in cases where the accurate assessment of depth and width is important because the coagulation layer of the CO2 laser could interfere with this evaluation. The pinhole method, a manual type fractionated laser treatment, is a new way of using the conventional ablative laser. The pinhole method with ablative lasers involves making multiple small holes that penetrate from the epidermis to the deeper dermis at 2 to 5-mm intervals1,2. In cases of telangiectasia, treating only some points with the pinhole technique is sufficient for treating the entire affected surface. Blood vessels are like pipes and some coagulated, obstructed points in them can shut down the functioning of the entire structure; however, the treatment modality for CALM does not appear to correspond well with this suggested theory. Therefore, in our patients' cases, the CALM was successfully removed after multiple treatment sessions, whereas the telangiectasia was successfully treated after only 1 session. The holes produced through the pinhole method are surrounded by untreated areas that aid in rapid epidermal repair, thereby reducing the downtime and side effects3,4. The other advantages of using this pinhole technique with ablative lasers are that is easier to use and less expensive than the treatment modality involving pulsed dye laser, as ablative lasers are usually available in most dermatology clinics. In conclusion, the pinhole method using the erbium : YAG laser could be beneficial for treating CALMs, which have been proven difficult to treat with other methods. Furthermore, the pinhole method using the CO2 laser can be used an effective treatment alternative for telangiectasia.

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. Objective: To determine the efficacy and side-effect profiles of QSNYL treatment of ABNOM in Korean patients. Methods: Of 42 Korean patients with ABNOM, 29 were treated with QSNYL (1,064 nm, 3 mm spot size, fluence 8∼ 9.5 J/cm 2 ), for up to 10 sessions each. Clinical photographs were taken before and after treatment. Lesion clearance was graded and complications such as hyperpigmentation, scarring, hypopigmentation, and erythema were assessed. Results: Of the 29 treated patients, 19 (66%) showed excellent or good results. Of the patients who were treated more than 3 times, 76% showed good to excellent results. Two patients experienced post-laser hyperpigmentation (PLH), which persisted for more than one month, but no patient experienced persistent erythema or hypertrophic scarring. Conclusion: QSNYL is safe and effective in the treatment of ABNOM in Korean patients. Short-interval repetitive treatment is especially useful in improving therapeutic results and reducing PLH. (Ann Dermatol 21(3) 255∼260, 2009)