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Abstract 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.

BackgroundThe 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 findingsTo 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.ConclusionsOur 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.

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.

Patients with chronic itch describe their pruritus in a wide variety of ways. However, these subjective descriptions are often not taken into consideration by physicians. This study aimed to validate patients’ descriptions of pruritus, and to investigate the relationship between various descriptions of pruritus and the patient burden of chronic pruritus by examining the mediating effects of sleep disturbance and sexual dysfunction on patient’s quality of life, as predicted by various descriptions of pruritus. Exploratory and confirmatory factor analyses were performed to identify the factor structure measured by 11 descriptions of pruritus. The study then analysed differences in the degree of sleep disturbance, sexual dysfunction, and quality of life deterioration factors using a structural equation modelling method. Using data from 419 patients with chronic pruritus, 11 descriptions of pruritus were classified into 2 groups: (i) sensory pruritus (i.e. stinging, stabbing, burning, painful, formication, throbbing, and cold) that are linked with descriptions of pruritus patterns; and (ii) affective pruritus (i.e. annoying, unbearable, worrisome, and warm) from patient reports of psychological or emotional distress. The study found that affective pruritus decreases patient’s quality of life either directly or indirectly through sleep disturbance. In conclusion, clues about a patients’ sleep disturbance or poor quality of life can be obtained through their descriptions of pruritus.

A variety of applications of human adipose tissue stem cell-derived exosomes have been suggested as novel cell-free therapeutic strategies in the regenerative and aesthetic medical fields. This study evaluated the clinical efficacy and safety of adipose tissue stem cell-derived exosomes as an adjuvant therapy after application of fractional CO2 laser for acne scars. A 12-week prospective, double-blind, randomized, split-face trial was performed. A total of 25 patients received 3 consecutive treatment sessions of fractional CO2 laser to the whole face, with a follow-up evaluation. Post-laser treatment regimens were applied; for each patient, one side of the face was treated with adipose tissue stem cell-derived exosomes gel and the other side was treated with control gel. Adipose tissue stem cell-derived exosomes-treated sides had achieved a significantly greater improvement than the control sides at the final follow-up visit (percentage reduction in échelle d’évaluation clinique des cicatrices d’acné scores: 32.5 vs 19.9%, p

An optimized therapeutic regimen involving a non-ablative fractionated laser or radiofrequency therapy for acne scars has not yet been established. To evaluate whether the combination of a non-ablative fractional laser (NAF) and fractional micro-needling radiofrequency (FMR) has clinical advantages for the treatment of atrophic acne scars compared with NAF alone, a 16-week prospective, randomized split-face study was performed. Each facial side of a patient was treated with 3 sessions of either NAF with FMR or NAF alone, with a 4-week interval between each session. Although both sides demonstrated significant decreases in the échelle d’évaluation clinique des cicatrices d’acné (ECCA) score, the facial side treated using the combination regimen demonstrated greater improvement in ECCA score regarding degree and onset time than the NAF-treated side. Histopathological and immunohistochemical results confirmed the clinical findings. This study demonstrated that a combination regimen involving NAF and FMR could be a viable option with satisfactory efficacy.

Studies have shown that stem cells and their derivatives, including conditioned media (CM), have inhibitory effects on skin pigmentation. However, evidence supporting the skin brightening effect of exosomes derived from stem cells is lacking. We studied the antipigmentation effect in vitro and skin brightening efficacy in vivo of exosomes derived from human adipose tissue-derived mesenchymal stem/stromal cells (ASC-exosomes). Exosomes were isolated from the CM of ASCs using the tangential flow filtration method. ASC-exosomes reduced intracellular melanin levels in B16F10 melanoma cells regardless of the presence of the α-melanocyte-stimulating hormone (α-MSH). The skin brightening efficacy of a cosmetic formulation containing ASC-exosomes was assessed in human volunteers with hyperpigmentation in a prospective, split-face, double-blind, randomized placebo-controlled study. The ASC-exosome-containing formulation statistically decreased the melanin contents compared to the placebo control. However, the melanin-reduction activity was limited and diminished along with time. A further improvement in efficient transdermal delivery of ASC-exosomes will be helpful for more profound efficacy. In summary, these results suggest that ASC-exosomes can be used as a cosmeceutical for skin brightening.

Objectives To investigate longitudinal changes in behavior and quality-of-life (QoL) in children with sleep disordered breathing (SDB) after adenotonsillectomy (AT). Methods This study prospectively enrolled 148 children who underwent AT for SDB. Caregivers filled out Korean attention deficit hyperactivity disorder (ADHD) rating scale (K-ARS) and Korean version of the obstructive sleep apnea-18 (KOSA-18) at preoperative 1 day, postoperative 1 month, and postoperative 6 months. Longitudinal changes in K-ARS and KOSA-18 were evaluated and compared among subgroups. Results Both K-ARS and KOSA-18 scores improved immediately at postoperative 1 month and were maintained at postoperative 6 months. The mean preoperative K-ARS scores of the non-ADHD, ADHD-trait, and overt-ADHD groups showed uniformly immediate and sustained improvements. Also, the mean preoperative KOSA-18 scores of the small, moderate, and large impact groups were significantly improved both at postoperative 1 month and 6 months. The changes in behavior and QoL showed no significant differences with regard to the aforementioned factors. Conclusion After AT, we may expect immediate and sustained improvements in behavior and QoL in children with SDB regardless of preoperative symptom severity. Moreover, by performing AT, improvements in behavior and QoL are expected regardless of sex, age, allergic trait, or concurrent coblation-assisted turbinoplasty. These findings may help our colleague physicians counselling pediatric SDB patients and their caregivers.

Exosomes are nano-sized vesicles that serve as mediators for cell-to-cell communication. With their unique nucleic acids, proteins, and lipids cargo compositions that reflect the characteristics of producer cells, exosomes can be utilized as cell-free therapeutics. Among exosomes derived from various cellular origins, mesenchymal stem cell-derived exosomes (MSC-exosomes) have gained great attention due to their immunomodulatory and regenerative functions. Indeed, many studies have shown anti-inflammatory, anti-aging and wound healing effects of MSC-exosomes in various in vitro and in vivo models. In addition, recent advances in the field of exosome biology have enabled development of specific guidelines and quality control methods, which will ultimately lead to clinical application of exosomes. This review highlights recent studies that investigate therapeutic potential of MSC-exosomes and relevant mode of actions for skin diseases, as well as quality control measures required for development of exosome-derived therapeutics.

Atopic dermatitis (AD) is a multifactorial, heterogeneous disease associated with epidermal barrier disruption and intense systemic inflammation. Previously, we showed that exosomes derived from human adipose tissue-derived mesenchymal stem cells (ASC-exosomes) attenuate AD-like symptoms by reducing multiple inflammatory cytokine levels. Here, we investigated ASC-exosomes’ effects on skin barrier restoration by analyzing protein and lipid contents. We found that subcutaneous injection of ASC-exosomes in an oxazolone-induced dermatitis model remarkably reduced trans-epidermal water loss, while enhancing stratum corneum (SC) hydration and markedly decreasing the levels of inflammatory cytokines such as IL-4, IL-5, IL-13, TNF-α, IFN-γ, IL-17, and TSLP, all in a dose-dependent manner. Interestingly, ASC-exosomes induced the production of ceramides and dihydroceramides. Electron microscopic analysis revealed enhanced epidermal lamellar bodies and formation of lamellar layer at the interface of the SC and stratum granulosum with ASC-exosomes treatment. Deep RNA sequencing analysis of skin lesions demonstrated that ASC-exosomes restores the expression of genes involved in skin barrier, lipid metabolism, cell cycle, and inflammatory response in the diseased area. Collectively, our results suggest that ASC-exosomes effectively restore epidermal barrier functions in AD by facilitating the de novo synthesis of ceramides, resulting in a promising cell-free therapeutic option for treating AD.

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.