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. Design, Setting, and Participants: In this diagnostic study, a total of 5619 (CAN5600 data set) and 2006 (CAN2000 data set; a manually revised subset of CAN5600) cropped lesion images of either melanoma or nevus were semiautomatically annotated from approximately 500 000 photographs on the internet using convolutional neural networks (CNNs), region-based CNNs, and large mask inpainting. For unsupervised pretraining, 132 673 possible lesions (LESION130k data set) were also created with diversity by collecting images from 18 482 websites in approximately 80 countries. A total of 5000 synthetic images (GAN5000 data set) were generated using the generative adversarial network (StyleGAN2-ADA; training, CAN2000 data set; pretraining, LESION130k data set). Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUROC) for determining malignant neoplasms was analyzed. In each test, 1 of the 7 preexisting public data sets (total of 2312 images; including Edinburgh, an SNU subset, Asan test, Waterloo, 7-point criteria evaluation, PAD-UFES-20, and MED-NODE) was used as the test data set. Subsequently, a comparative study was conducted between the performance of the EfficientNet Lite0 CNN on the proposed data set and that trained on the remaining 6 preexisting data sets. Results: The EfficientNet Lite0 CNN trained on the annotated or synthetic images achieved higher or equivalent mean (SD) AUROCs to the EfficientNet Lite0 trained using the pathologically confirmed public data sets, including CAN5600 (0.874 [0.042]; P = .02), CAN2000 (0.848 [0.027]; P = .08), and GAN5000 (0.838 [0.040]; P = .31 [Wilcoxon signed rank test]) and the preexisting data sets combined (0.809 [0.063]) by the benefits of increased size of the training data set. Conclusions and Relevance: The synthetic data set in this diagnostic study was created using various AI technologies from internet images. A neural network trained on the created data set (CAN5600) performed better than the same network trained on preexisting data sets combined. Both the annotated (CAN5600 and LESION130k) and synthetic (GAN5000) data sets could be shared for AI training and consensus between physicians.

Needleless laser injector versus needle injection for skin enhancement and rejuvenation effect of dermal filler.

BACKGROUND AND OBJECTIVES: A needleless laser-induced microjet injector is a novel transdermal drug delivery system that can rapidly inject a very small and precise drug dose into the skin with minimal pain and downtime. In this study, we aimed to compare the laser-induced microjet injection versus needle injection of polylactic acid/hyaluronic acid filler for skin enhancement and rejuvenation. PATIENTS AND METHODS: A 24-week prospective, single-center, assessor-blinded, randomized, split-face study was conducted. The enrolled patients underwent one treatment session of dermal filler injection using a laser-induced microjet injector on one half of the face or a traditional needle injection on the other half of the face. Evaluation was conducted at baseline before treatment and at 4, 12, and 24 weeks after treatment. RESULTS: A single treatment of filler injection with a laser-induced microjet injector resulted in similar improvements in skin hydration and elasticity as a single treatment of filler injection by using manual needle injection, with reduced pain, side effects, and decreased treatment time. CONCLUSIONS: Laser-induced microjet injector enabled not only the application of a controlled dose and filler depth but also even distribution, improved clinical efficacy, reduced pain and side effects, and sufficient time for clinicians to perform treatment.

A Randomized Split-Face Study of Photodynamic Therapy With St. John's Wort and Indole-3-Acetic Acid for the Treatment of Acne.

BACKGROUND: St. John's wort (SJW) contains hypericin, a powerful photosensitizer with antimicrobial and anti-inflammatory activities. OBJECTIVE: To compare the efficacy and safety of SJW-photodynamic therapy (PDT) with that of indole-3-acetic acid (IAA)-PDT for the treatment of acne and investigate the skin rejuvenating effects of SJW-PDT. MATERIALS AND METHODS: In vitro experiments were conducted to examine the generation of reactive oxygen species and the antimicrobial effects of SJW-PDT. In the prospective, double-blind, split-face, randomized study, 31 patients with facial acne were treated with SJW or IAA with simultaneous illumination of red light and green light. RESULTS: SJW produces free radicals with visible light irradiation, and the growth of Cutibacterium acnes and Staphylococcus aureus is significantly suppressed. One week after the last treatment, the acne lesion counts were significantly decreased in both groups (56.5% reduction in SJW, p < .001 vs 57.0% in IAA, p < .001). Significant reductions in sebum secretion, erythema index, roughness, and wrinkles were observed in both groups after the treatment. No side effects were observed. CONCLUSION: SJW-PDT is a simple, safe, and effective treatment option for acne that is also beneficial for skin rejuvenation.

Efficacy and Safety of the Micro-insulated Needle Radiofrequency Device for Reduction of Submental Fat.

BACKGROUND: Recently, it has been reported that a micro-insulated needle radiofrequency (RF) system is effective at achieving subcutaneous fat reduction; however, no study has yet applied this technique to reduce submental fat. OBJECTIVE: To evaluate the efficacy and safety of a fractional RF device with a micro-insulated needle to reduce submental fat. MATERIALS AND METHODS: In this prospective, single-blinded, pre-post comparative study, 24 adults with excess submental fat were treated once using a micro-insulated needle RF device. Outcomes included efficacy (submental fat rating by an independent investigator, fat volume quantified with a 3-dimensional camera, and patient satisfaction), assessed 1 and 2 months after the procedure, and safety (adverse events), assessed throughout the study. RESULTS: The patients' Physician-Assisted Submental Fat Rating Scale score significantly decreased after 1 month and further decreased after 2 months. The average volume of submental fat was significantly decreased after 2 months (20.44 ± 5.53 cc to 16.41 ± 4.58 cc, p < .001). Patient satisfaction was high. Transient and mild local skin reactions without long-term sequelae were observed in 4 patients. CONCLUSION: The micro-insulated needle RF device is beneficial for the reduction of submental fat and has tolerable safety profiles. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05517824.

Early Postoperative Injections of Polydeoxyribonucleotide Prevent Hypertrophic Scarring After Thyroidectomy: A Randomized Controlled Trial.

Objective: Polydeoxyribonucleotide (PDRN) is known to enhance wound healing, but there has been no clinical trial investigating the effect of PDRN on scar prevention in surgical wounds. This study aimed to evaluate the efficacy of PDRN administration in preventing postoperative scars. Approach: In this randomized controlled trial (NCT05149118), 44 patients who underwent open thyroidectomy were randomly assigned to the PDRN treatment or untreated control group. Only patients in the treatment group received two consecutive injections of PDRN 1 and 2 days after surgery. The modified Vancouver Scar Scale (mVSS), patients' subjective symptoms, erythema index (EI), melanin index (MI), and scar height were assessed 3 months after surgery. Results: Patients in the treatment group had lower mVSS scores (1.619 ± 1.244 vs. 2.500 ± 1.540, respectively; p = 0.059) and a significantly lower vascularity subscore (0.476 ± 0.512 vs. 0.900 ± 0.447, respectively; p = 0.010) than those in the control group at the 3-month follow-up. Compared with the control group, the level of subjective symptoms, EI, and scar height were all significantly lowered in the PDRN injection group. No specific side effects related to PDRN injection were observed. Innovation: This is the first clinical study that demonstrated that PDRN injections rapidly decreased postsurgical wound erythema and as a result, significantly reduced both excessive scar formation and accompanying symptoms. Conclusion: Early postoperative injection of PDRN is an effective and safe treatment to prevent hypertrophic scars and improve scar outcomes.

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. 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.

Fibroblasts Close a Void in Free Space by a Purse-String Mechanism.

The mechanism by which stromal cells fill voids in injured tissue remains a fundamental question in regenerative medicine. While it is well-established that fibroblasts fill voids by depositing extracellular matrix (ECM) proteins as they migrate toward the wound site, little is known about their ability to adopt an epithelial-like purse-string behavior. To investigate fibroblast behavior during gap closure, we created an artificial wound with a large void space. We discovered that fibroblasts could form a free-standing bridge over deep microvoids, closing the void via purse-string contraction, a mechanism previously thought to be unique to epithelial wound closure. The findings also revealed that myosin II mediated contractility and intercellular adherent junctions were required for the closure of the fibroblast gap in our fabricated three-dimensional artificial wound. To fulfill their repair function under the specific microenvironmental conditions of wounds, fibroblasts appeared to acquire the structural features of epithelial cells, namely, contractile actin bundles that span over multiple cells along the boundary. These findings shed light on a novel mechanism by which stromal cells bridge the 3D gap during physiological processes such as morphogenesis and wound healing.

Basal cell carcinoma of the scalp shows distinct features from the face in Asians.

Basal cell carcinoma (BCC) affecting different sites has been reported to have different clinicopathological features. In previous studies, the scalp was commonly classified to the head and neck region. However, the scalp has distinct characteristics from those of other parts of the skin. We retrospectively reviewed the medical records of patients who underwent surgical treatment for BCC. A total of 734 lesions were examined, and 13.2% originated from the scalp. The nodular type was the most common histologic subtype; however, the proportion of the superficial type was significantly higher than that of facial BCC (p < 0.001). Compared with facial BCC, younger age (p = 0.046) and larger tumor size (p < 0.001) were observed in scalp BCC. These characteristics were similar to those of truncal BCC in that they demonstrated a higher proportion of the superficial type (p < 0.001), younger age (p = 0.001), and larger tumor diameter (p < 0.001) compared with BCC in the head and neck region. Scalp BCC and truncal BCC were not significantly different in terms of age (p = 0.052) and tumor size (p = 0.230). In conclusion, despite the anatomical proximity, features of scalp BCC were similar to those of truncal lesions compared with facial lesions. Scalp BCC might be a separate entity from facial BCC.

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.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.

Clinical Implication of the Regional Thickness of the Lower Facial Skin, Superficial Fat, and Superficial Musculoaponeurotic System on High-Intensity Focused Ultrasound Treatment.

BACKGROUND: Knowledge of the topographic thickness of the skin and soft tissues is necessary when performing a high-intensity focused ultrasound (HIFU) procedure. Thermal tissue injury to the superficial musculoaponeurotic system (SMAS) or deeper can injure the facial nerve and its branches. OBJECTIVE: To demonstrate the topographic thickness of the lower facial skin, superficial fat, and SMAS. MATERIALS AND METHODS: The ultrasound data of 200 healthy patients who underwent lower facial rejuvenation were retrospectively reviewed. RESULTS: The mean age was 41.1 ± 13.7 years (range, 19-76 years). The jowl had thinner skin, thicker superficial fat, and deeper superficial and deep margins of the SMAS than the preauricle or lower cheek. The thickness of the superficial fat decreased with age, especially on the preauricle, lower cheek, and jowl. Women had thicker superficial fat than men on the preauricle and lower cheek. The superficial and deep margins of the SMAS were located more superficially in old and male patients with a slim facial figure than in young and female patients with a chubby facial figure. CONCLUSION: The present findings provide anatomical information regarding the superficial fat and SMAS, which is useful in determining the focal penetration depth of HIFU treatment for lower face rejuvenation.

Clinical Characteristics of 9 Adult Patients with Granulomatous Periorificial Dermatitis and Comparison with Childhood Granulomatous Periorificial Dermatitis.

To our knowledge, there have been no previous reports of granulomatous periorificial dermatitis (GPD) in adult patients in contrast to childhood GPD (CGPD). We report cases of 9 adult patients with GPD with regards to the clinical and histopathological characteristics and their management. GPD in adults may be an entity that is actually underdiagnosed, especially in middle-aged females. It is a benign disorder, albeit requiring a relatively long-term treatment. Unlike CGPD, GPD in adults is frequently accompanied by pruritus with predilection for the eyelid and should be treated initially with oral medication.

A Case of Paraneoplastic Pemphigus as a Preceding Manifestation of Underlying Follicular Lymphoma Treated with R-CHOP.

Paraneoplastic pemphigus is a rare, life-threatening disorder associated with an underlying neoplasm, which presents with painful stomatitis and polymorphous skin lesions. Successful diagnosis of paraneoplastic pemphigus can lead to the diagnosis and treatment of the underlying malignancy. However, involvement of the respiratory system is typically unresponsive to treatment. Herein, we report the case of a 44-year-old female diagnosed with paraneoplastic pemphigus with underlying follicular lymphoma treated with a chemotherapy regimen including rituximab. Her skin lesions and underlying lymphoma responded to treatment, but bronchiolitis obliterans continued to progress and resulted in fatal respiratory failure.

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. 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.

Resveratrol Inhibits Particulate Matter-Induced Inflammatory Responses in Human Keratinocytes.

Particulate matter (PM), a major air pollutant, is a complex mixture of solid and liquid particles of various sizes. PM has been demonstrated to cause intracellular inflammation in human keratinocytes, and is associated with various skin disorders, including atopic dermatitis, eczema, and skin aging. Resveratrol is a natural polyphenol with strong antioxidant properties, and its beneficial effects against skin changes due to PM remain elusive. Therefore, in the present study, we investigated the effect of resveratrol on PM-induced skin inflammation and attempted to deduce the molecular mechanisms underlying resveratrol's effects. We found that resveratrol inhibited PM-induced aryl hydrocarbon receptor activation and reactive oxygen species formation in keratinocytes. It also suppressed the subsequent cellular inflammatory response by inhibiting mitogen-activated protein kinase activation. Consequentially, resveratrol reduced PM-induced cyclooxygenase-2/prostaglandin E2 and proinflammatory cytokine expression, including that of matrix metalloproteinase (MMP)-1, MMP-9, and interleukin-8, all of which are known to be central mediators of various inflammatory conditions and aging. In conclusion, resveratrol inhibits the PM-induced inflammatory response in human keratinocytes, and we suggest that resveratrol may have potential for preventing air pollution-related skin problems.

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. 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% (P < 0.0001) and 1.1% (P < 0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P < 0.0001). 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.

Intractable Chronic Granulomatous Perioral Dermatitis in Patients Receiving Growth Hormone Therapy: A New Association between CGPD and GH.

Childhood granulomatous periorificial dermatitis (CGPD) is a self-limiting skin condition characterized by papular eruptions around the mouth, nose and eyes of preadolescent children. We report two cases of intractable CGPD in which patients receiving growth hormone (GH) therapy showed persistent symptoms despite multiple treatment modalities. This association may suggest the role of GH in the pathogenesis of CGPD.

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. 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. Main Outcomes and Measures: 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. Results: 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]). Conclusions and Relevance: The results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.

Nonsurgical correction of drooping mouth corners using monophasic hyaluronic acid and incobotulinumtoxinA.

BACKGROUND: Nonsurgical correction of drooping of the corners of the mouth requires a multimodal approach combining botulinum toxin and soft tissue filler injections. OBJECTIVE: To validate a nonsurgical therapeutic approach for correcting a "saddened" appearance characterized by drooping mouth corners, oral commissures, and/or marionette lines. METHODS: In this prospective, evaluator-blinded study, monophasic hyaluronic acid (MHA) was infiltrated to correct drooping mouth corners in four steps, deep oral commissures in two steps, and/or deep marionette lines in two steps, in that order. Then, incobotulinumtoxinA was injected along the depressor anguli oris and mentalis muscles. RESULTS: The total volumes of MHA used in steps 1-4 were 0.2 mL (interquartile range [IQR]: 0.19-0.3) for the right side of the face and 0.25 mL (IQR: 0.2-0.3) for the left side; total volumes in steps 5-8 were 0.18 mL (IQR: 0-0.4) for the right side and 0.15 mL (IQR: 0-0.33) for the left side. The total mean dose of incobotulinumtoxinA was 26.5 units (IQR: 24-28). The median degrees of drooping of the mouth corners were -4° (IQR: -7° to -2°) at baseline, -1° (IQR: -3° to 1°) at post-treatment 2 weeks, and -1° (IQR: -3° to 0°) at post-treatment 3 months. Median Global Aesthetic Improvement Scale scores were 3 (IQR: 2-3) at post-treatment 2 weeks and 3 (IQR: 2-3.75) at 3 months. CONCLUSION: Our results demonstrated that nonsurgical treatment with MHA and incobotulinumtoxinA provides satisfactory therapeutic outcomes in patients with a saddened appearance by correcting drooping of the mouth corners, deep oral commissures, and/or deep marionette lines.