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Importance: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic categories might be helpful., Methods: A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Dermoscopic images were classified by a binary CNN trained to differentiate melanocytic from non-melanocytic lesions (FotoFinder Systems, Bad Birnbach, Germany). Primary endpoint was the accuracy of the CNN's classification in comparison with dermatologists reviewing level-II information. Secondary endpoints included dermatologists' accuracies according to their level of experience and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC)., Results: The CNN revealed an accuracy and ROC AUC with corresponding 95 % confidence intervals (CI) of 91.0 % (83.8 % to 95.2 %) and 0.981 (0.962 to 1). In level I, dermatologists showed a mean accuracy of 83.7 % (82.5 % to 84.8 %). With level II information, the accuracy improved to 87.8 % (86.7 % to 88.9 %; p < 0.001). When comparing accuracies of CNN and dermatologists in level II, the CNN's accuracy was higher (91.0 % versus 87.8 %, p < 0.001). For experts with level II information results were on par with the CNN (91.0 % versus 90.4 %, p = 0.368)., Conclusions: The tested CNN accurately differentiated melanocytic from non-melanocytic skin lesions and outperformed dermatologists. The CNN may support clinicians and could be used in an ensemble approach combined with other CNN models., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. HA Haenssle received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening: Scibase AB, FotoFinder Systems GmbH, Heine Optotechnik GmbH, Magnosco GmbH. JK Winkler and W Stolz also received honoraria from Fotofinder Systems GmBH. The other authors state no conflict of interest related to the study., (Copyright © 2024. Published by Elsevier Ltd.)

Background: The detection of cutaneous metastases (CMs) from various primary tumours represents a diagnostic challenge., Objectives: Our aim was to evaluate the general characteristics and dermatoscopic features of CMs from different primary tumours., Methods: Retrospective, multicentre, descriptive, cross-sectional study of biopsy-proven CMs., Results: We included 583 patients (247 females, median age: 64 years, 25%-75% percentiles: 54-74 years) with 632 CMs, of which 52.2% (n = 330) were local, and 26.7% (n = 169) were distant. The most common primary tumours were melanomas (n = 474) and breast cancer (n = 59). Most non-melanoma CMs were non-pigmented (n = 151, 95.6%). Of 169 distant metastases, 54 (32.0%) appeared on the head and neck region. On dermatoscopy, pigmented melanoma metastases were frequently structureless blue (63.6%, n = 201), while amelanotic metastases were typified by linear serpentine vessels and a white structureless pattern. No significant difference was found between amelanotic melanoma metastases and CMs of other primary tumours., Conclusions: The head and neck area is a common site for distant CMs. Our study confirms that most pigmented melanoma metastasis are structureless blue on dermatoscopy and may mimic blue nevi. Amelanotic metastases are typified by linear serpentine vessels and a white structureless pattern, regardless of the primary tumour., (© 2024 European Academy of Dermatology and Venereology.)

Importance: Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis., Design and Patients: In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations., Results: At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites)., Conclusions: ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: HA Haenssle received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening: Scibase AB, FotoFinder Systems GmbH, Heine Optotechnik GmbH, Magnosco GmbH. JK Winkler received honoraria and/or travel expenses from BMS, Fotofinder Systems GmBH, LaRoche Posay, Almirall, Biotest, Amgen, BMS, Leo Pharma, MSD, Philochem and Roche. LV Maul has served as advisor and/or received speaking fees and/or travel expenses and/or participated in clinical trials sponsored by Almirall, Amgen, BMS, Celgene, Eli Lilly, Kyowa Kirin, Incyte, L’Oreal, MSD, Novartis, Pierre Fabre, Roche, and Sanofi. The other authors state no conflict of interest related to the study., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer., Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection., Methods: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance., Results: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users., Conclusions: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice., (© 2023 The Authors. Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.)

Importance: Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking., Objective: To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions., Design, Setting, and Participants: In this prospective diagnostic 2-center study, dermatologists performed skin cancer screenings using naked-eye examination and dermoscopy. Dermatologists graded suspect melanocytic lesions by the probability of malignancy (range 0-1, threshold for malignancy ≥0.5) and indicated management decisions (no action, follow-up, excision). Next, dermoscopic images of suspect lesions were assessed by a market-approved CNN, Moleanalyzer Pro (FotoFinder Systems). The CNN malignancy scores (range 0-1, threshold for malignancy ≥0.5) were transferred to dermatologists with the request to re-evaluate lesions and revise initial decisions in consideration of CNN results. Reference diagnoses were based on histopathologic examination in 125 (54.8%) lesions or, in the case of nonexcised lesions, on clinical follow-up data and expert consensus. Data were collected from October 2020 to October 2021., Main Outcomes and Measures: Primary outcome measures were diagnostic sensitivity and specificity of dermatologists alone and dermatologists cooperating with the CNN. Accuracy and receiver operator characteristic area under the curve (ROC AUC) were considered as additional measures., Results: A total of 22 dermatologists detected 228 suspect melanocytic lesions (190 nevi, 38 melanomas) in 188 patients (mean [range] age, 53.4 [19-91] years; 97 [51.6%] male patients). Diagnostic sensitivity and specificity significantly improved when dermatologists additionally integrated CNN results into decision-making (mean sensitivity from 84.2% [95% CI, 69.6%-92.6%] to 100.0% [95% CI, 90.8%-100.0%]; P = .03; mean specificity from 72.1% [95% CI, 65.3%-78.0%] to 83.7% [95% CI, 77.8%-88.3%]; P < .001; mean accuracy from 74.1% [95% CI, 68.1%-79.4%] to 86.4% [95% CI, 81.3%-90.3%]; P < .001; and mean ROC AUC from 0.895 [95% CI, 0.836-0.954] to 0.968 [95% CI, 0.948-0.988]; P = .005). In addition, the CNN alone achieved a comparable sensitivity, higher specificity, and higher diagnostic accuracy compared with dermatologists alone in classifying melanocytic lesions. Moreover, unnecessary excisions of benign nevi were reduced by 19.2%, from 104 (54.7%) of 190 benign nevi to 84 nevi when dermatologists cooperated with the CNN (P < .001). Most lesions were examined by dermatologists with 2 to 5 years (96, 42.1%) or less than 2 years of experience (78, 34.2%); others (54, 23.7%) were evaluated by dermatologists with more than 5 years of experience. Dermatologists with less dermoscopy experience cooperating with the CNN had the most diagnostic improvement compared with more experienced dermatologists., Conclusions and Relevance: In this prospective diagnostic study, these findings suggest that dermatologists may improve their performance when they cooperate with the market-approved CNN and that a broader application of this human with machine approach could be beneficial for dermatologists and patients.

Phakomatosis pigmentovascularis is a diagnosis that denotes the coexistence of pigmentary and vascular birthmarks of specific types, accompanied by variable multisystem involvement, including CNS disease, asymmetrical growth, and a predisposition to malignancy. Using a tight phenotypic group and high-depth next-generation sequencing of affected tissues, we discover here clonal mosaic variants in gene PTPN11 encoding SHP2 phosphatase as a cause of phakomatosis pigmentovascularis type III or spilorosea. Within an individual, the same variant is found in distinct pigmentary and vascular birthmarks and is undetectable in blood. We go on to show that the same variants can cause either the pigmentary or vascular phenotypes alone, and drive melanoma development within pigmentary lesions. Protein structure modeling highlights that although variants lead to loss of function at the level of the phosphatase domain, resultant conformational changes promote longer ligand binding. In vitro modeling of the missense variants confirms downstream MAPK pathway overactivation and widespread disruption of human endothelial cell angiogenesis. Importantly, patients with PTPN11 mosaicism theoretically risk passing on the variant to their children as the germline RASopathy Noonan syndrome with lentigines. These findings improve our understanding of the pathogenesis and biology of nevus spilus and capillary malformation syndromes, paving the way for better clinical management., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically 'unclear' lesions may benefit from artificial intelligence support via convolutional neural networks (CNN)., Methods: In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as 'benign', 'malignant', or 'unclear' and indicated their management decisions ('no action', 'follow-up', 'treatment/excision'). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images., Results: After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as 'unclear' and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 'follow-up' or 'no action') and 43.9% of 271 truly benign cases (119 'excision'). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained 'unclear' to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01)., Conclusions: Dermatologists mostly managed diagnostically 'unclear' FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Convolutional neural networks (CNN) achieve a level of performance comparable or even superior to dermatologists in the assessment of pigmented and nonpigmented skin lesions. In the analysis of images by artificial neural networks, images on a pixel level pass through various layers of the network with different graphic filters. Based on excellent study results, a first deep learning network (Moleanalyzer pro, Fotofinder Systems GmBH, Bad Birnbach, Germany) received market approval in Europe. However, such neural networks also reveal relevant limitations, whereby rare entities with insufficient training images are classified less adequately and image artifacts can lead to false diagnoses. Best results can ultimately be achieved in a cooperation of "man with machine". For future skin cancer screening, automated total body mapping is evaluated, which combines total body photography, automated data extraction and assessment of all relevant skin lesions., (© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Introduction: UV irradiation of nevi induces transient melanocytic activation with dermoscopic and histological changes., Objectives: We investigated whether UV irradiation of nevi may influence electrical impedance spectroscopy (EIS) or convolution neural networks (CNN)., Methods: Prospective, controlled trial in 50 patients undergoing phototherapy (selective UV phototherapy (SUP), UVA1, SUP/UVA1, or PUVA). EIS (Nevisense, SciBase AB) and CNN scores (Moleanalyzer-Pro, FotoFinder Systems) of nevi were assessed before (V1) and after UV irradiation (V2). One nevus (nevus irr ) was exposed to UV light, another UV-shielded (nevus non - irr )., Results: There were no significant differences in EIS scores of nevus irr before (2.99 [2.51-3.47]) and after irradiation (3.32 [2.86-3.78]; P = 0.163), which was on average 13.28 (range 4-47) days later. Similarly, UV-shielded nevus non - irr did not show significant changes of EIS scores (V1: 2.65 [2.19-3.11]), V2: 2.92 [2.50-3.34]; P = 0.094). Subgroup analysis by irradiation revealed a significant increase of EIS scores of nevus irr (V1: 2.69 [2.21-3.16], V2: 3.23 [2.72-3.73]; P = 0.044) and nevus non - irr (V1: 2.57 [2.07-3.07], V2: 3.03 [2.48-3.57]; P = 0.033) for patients receiving SUP. In contrast, CNN scores of nevus irr (P = 0.995) and nevus non - irr (P = 0.352) showed no significant differences before and after phototherapy., Conclusions: For the tested EIS system increased EIS scores were found in nevi exposed to SUP. In contrast, CNN results were more robust against UV exposure., Competing Interests: Competing Interests: None., (©2022 Winkler et al.)

Metabolic reprogramming mediated by hypoxia-inducible factors play a crucial role in many human cancers. HIF-1α is activated under hypoxic conditions and is considered a key regulator of oxygen homoeostasis during tumor proliferation under hypoxia. Aim of this research was to analyze the immunohistochemical expression of HIF-1α, VEGF-A, Glut-1, MCT4, and CAIX in atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS). 21 paraffin-embedded AFX and 22 PDS were analysed by immunohistochemistry, namely HIF-1α, VEGF-A (referred to as VEGF throughout the manuscript), Glut-1, MCT4, and CAIX. To quantify the protein expression, we considered the percentage of positive tumor cells (0: 0%, 1: up to 1%, 2: 2-10%, 3: 11-50%, 4: >50%) in relation to the staining intensity (0: negative, 1: low, 2: medium, 3: strong). HIF-1α expression (mean ± SD) in AFX (9.33±2.92) was significantly stronger than that in PDS (5.90±4.38; P= 0.007), whereas the expression of VEGF, Glut-1, MCT4, and CAIX did not show differences between AFX and PDS. When comparing all tumors without subgroup stratification, the expression of HIF-1α (P= 0.044) and MCT4 (P= 0.036) was significantly stronger in ulcerated tumors than in tumors without ulceration. Our findings provide the first evidence that HIF-1α-induced metabolic reprogramming may contribute to the pathogenesis of AFX and PDS. HIF-1α expression seems to be higher in AFX than in PDS, and ulcerated tumors show higher expression levels of HIF-1α and MCT4 irrespective of the diagnosis.

Metabolic reprogramming mediated by hypoxia-inducible factors and its downstream targets plays a crucial role in many human malignancies. Excessive proliferation of tumor cells under hypoxic conditions leads to metabolic reprogramming and altered gene expression enabling tumors to adapt to their hypoxic environment. Here we analyzed the metabolic signatures of primary cutaneous melanomas with positive and negative sentinel node status in order to evaluate potential differences in their metabolic signature. We found a positive correlation of the expression of glucose transporter 1 (GLUT-1) with tumor thickness and ulceration in all melanomas with subgroup analyses as well as in the subgroup with a negative sentinel node. Furthermore, the expression of vascular endothelial growth factor (VEGF) was positively correlated with the presence of ulceration in melanomas with positive sentinel node., (© 2022. The Author(s).)

Background: Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany)., Methods: We screened open-access dermoscopic image repositories widely used for CNN training for distribution of sex. Moreover, the sex-related diagnostic performance of the market-approved CNN was tested in 1549 dermoscopic images stratified by sex (female n = 773; male n = 776)., Results: Most open-access repositories showed a marked under-representation of images originating from female (40%) versus male (60%) patients. Despite these imbalances and well-known sex-related differences in skin anatomy or skin-directed behaviour, the tested CNN achieved a comparable sensitivity of 87.0% [80.9%-91.3%] versus 87.1% [81.1%-91.4%], specificity of 98.7% [97.4%-99.3%] versus 96.9% [95.2%-98.0%] and ROC-AUC of 0.984 [0.975-0.993] versus 0.979 [0.969-0.988] in dermoscopic images of female versus male origin, respectively. In the sample at hand, sex-related differences in ROC-AUCs were not statistically significant in the per-image analysis nor in an additional per-individual analysis (p ≥ 0.59)., Conclusion: Design and training of artificial intelligence algorithms for medical applications should generally acknowledge sex and gender dimensions. Despite sex-related imbalances in open-access training data, the diagnostic performance of the tested CNN showed no sex-related bias in the classification of skin lesions., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: HA Haenssle received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening: Scibase AB, FotoFinder Systems GmbH, Heine Optotechnik GmbH, Magnosco GmbH. C Fink received travel expenses from Magnosco GmbH. All other authors declared no conflict of interest. FotoFinder Systems GmbH was neither involved in the planning nor implementation of the study and had no role in the analysis or interpretation of the study data., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Sequential digital dermoscopy (SDD) is applied for early melanoma detection by uncovering dynamic changes of monitored lesions. Convolutional neural networks (CNN) are capable of high diagnostic accuracies similar to trained dermatologists., Objectives: To investigate the capability of CNN to correctly classify melanomas originally diagnosed by mere dynamic changes during SDD., Methods: A retrospective cross-sectional study using image quartets of 59 high-risk patients each containing one melanoma diagnosed by dynamic changes during SDD and three nevi (236 lesions). Two validated CNN classified quartets at baseline or after SDD follow-up at the time of melanoma diagnosis. Moreover, baseline quartets were rated by 26 dermatologists. The main outcome was the number of quartets with correct classifications., Results: CNN-1 correctly classified 9 (15.3%) and CNN-2 8 (13.6%) of 59 baseline quartets. In baseline images, CNN-1 attained a sensitivity of 25.4% (16.1%-37.8%) and specificity of 92.7% (87.8%-95.7%), whereas CNN-2 of 28.8% (18.8%-41.4%) and 75.7% (68.9%-81.4%). Expectedly, after SDD follow-up CNN more readily detected melanomas resulting in improved sensitivities (CNN-1: 44.1% [32.2%-56.7%]; CNN-2: 49.2% [36.8%-61.6%]). Dermatologists were told that each baseline quartet contained one melanoma, and on average, correctly classified 24 (22-27) of 59 quartets. Correspondingly, accepting a baseline quartet to be appropriately classified whenever the highest malignancy score was assigned to the melanoma within, CNN-1 and CNN-2 correctly classified 28 (47.5%) and 22 (37.3%) of 59 quartets, respectively., Conclusions: The tested CNN could not replace the strategy of SDD. There is a need for CNN capable of integrating information on dynamic changes into analyses., Competing Interests: Conflict of interest statement P Tschandl reports grants from MetaOptima and Lilly, consulting fees from Silverchair, and speaker honoraria from Lilly and FotoFinder, outside the submitted work. A Enk reports a DFG grant, honoraria from Biotest, MSD, Janssen-Cilag and BMS, participation in an MSD Data Safety Monitoring Board and leadership in the EDF and IDSI outside the submitted work. H Kittler states honoraria from Fotofinder and Pelpharma, as well as receipt of equipment from Fotofinder, Heine, 3Gen, Derma Medical outside the submitted work. HA Haenssle received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening, Scibase AB, FotoFinder Systems GmbH, Heine Optotechnik GmbH, Magnosco GmbH. All other authors state ‘no conflict of interest’., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice., Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians., Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma. The search terms skin cancer classification, deep learning, convolutional neural network (CNN), melanoma (detection), digital biomarkers, histopathology and whole slide imaging were combined. Based on the search results, only studies that considered direct comparison of AI results with clinicians and had a diagnostic classification as their main objective were included., Results: A total of 19 reader studies fulfilled the inclusion criteria. Of these, 11 CNN-based approaches addressed the classification of dermoscopic images; 6 concentrated on the classification of clinical images, whereas 2 dermatopathological studies utilised digitised histopathological whole slide images., Conclusions: All 19 included studies demonstrated superior or at least equivalent performance of CNN-based classifiers compared with clinicians. However, almost all studies were conducted in highly artificial settings based exclusively on single images of the suspicious lesions. Moreover, test sets mainly consisted of holdout images and did not represent the full range of patient populations and melanoma subtypes encountered in clinical practice., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.S.U. is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LEO Pharma, Merck Sharp and Dohme, Novartis, Pierre Fabre and Roche, outside the submitted work. M.G. has received speaker's honoraria and/or has served as a consultant and/or member of advisory boards for Almirall, Argenx, Biotest, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis and UCB, outside the submitted work. H.A.H. worked as a consultant or received honoraria and travel support from Heine Optotechnik GmbH, JenLab GmbH, FotoFinder Systems GmbH, Magnosco GmbH, SciBase AB, Beiersdorf AG, Almirall Hermal GmbH and Galderma Laboratorium GmbH. V.M.R. is on the advisory board or has received honoraria or ownership in Inhabit Brands, Inc. unrelated to this work. Sondermann W. reports grants from medi GmbH Bayreuth, personal fees from Janssen, grants and personal fees from Novartis, personal fees from Lilly, personal fees from UCB, personal fees from Almirall, personal fees from LEO Pharma and personal fees from Sanofi Genzyme, outside the submitted work. H.P.S. is a shareholder of MoleMap NZ Limited and e-derm consult GmbH and undertakes regular tele-dermatological reporting for both companies. H.P.S. is a medical consultant for Canfield Scientific, Inc., MoleMap Australia Pty Ltd and Revenio Research Oy and a medical advisor for First Derm. M.L-V. has received speaker's honoraria and/or received grants and/or participated in clinical trials of AbbVie, Almirall, Amgen, Celgene, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis and UCB, outside the submitted work. A.Z. has been an advisor and/or received speaker's honoraria and/or received grants and/or participated in clinical trials of AbbVie, Almirall, Amgen, Beiersdorf Dermo Medical, Bencard Allergy, Celgene, Eli Lilly, Janssen Cilag, LEO Pharma, Novartis, Sanofi-Aventis and UCB Pharma, outside the submitted work. Kittler H. received speaker's honoraria from FotoFinder Systems GmbH and received non-financial support from Heine Optotechnik GmbH, Derma Medical and 3Gen. T.J.B. reports owning a company that develops mobile apps, including the teledermatology services AppDoc (https://online-hautarzt.de) and Intimarzt (https://Intimarzt.de); Smart Health Heidelberg GmbH, Handschuhsheimer Landstr. 9/1, 69120 Heidelberg, https://smarthealth.de. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Melanoma of the external ear is a rare condition accounting for 7-20% of all melanomas of the head and neck region. They present classical features of extra-facial melanomas clinically and dermoscopically. In contrast, facial melanomas show peculiar patterns in dermoscopy., Objectives: To evaluate whether there are clinical and/or dermoscopic differences in melanocytic lesions located either at the external ear or on the face., Methods: In this retrospective study we reviewed an image database for clinical and dermoscopic images of melanomas and nevi located either on the face or at the level of the external ear., Results: 65 patients (37 men; 63.8%) with 65 lesions were included. We found no significant differences in comparing face melanomas with melanomas at the level of the external ear, neither clinically nor dermoscopically. However, we provided evidence for differences in some clinical and dermoscopic features of melanomas and nevi of the external ear., Conclusions: In this study, we reported no significant differences in comparing melanomas on the face with melanomas of the external ear, both clinically and dermoscopically. Furthermore, we provided data on clinical and dermoscopic differences comparing nevi and melanoma of the external ear., Competing Interests: Competing interests: None., (©2021 Deinlein et al.)

Background and Objectives: Convolutional neural networks (CNN) enable accurate diagnosis of medical images and perform on or above the level of individual physicians. Recently, collective human intelligence (CoHI) was shown to exceed the diagnostic accuracy of individuals. Thus, diagnostic performance of CoHI (120 dermatologists) versus individual dermatologists versus two state-of-the-art CNN was investigated., Patients and Methods: Cross-sectional reader study with presentation of 30 clinical cases to 120 dermatologists. Six diagnoses were offered and votes collected via remote voting devices (quizzbox®, Quizzbox Solutions GmbH, Stuttgart, Germany). Dermatoscopic images were classified by a binary and multiclass CNN (FotoFinder Systems GmbH, Bad Birnbach, Germany). Three sets of diagnostic classifications were scored against ground truth: (1) CoHI, (2) individual dermatologists, and (3) CNN., Results: CoHI attained a significantly higher accuracy [95 % confidence interval] (80.0 % [62.7 %-90.5 %]) than individual dermatologists (75.7 % [73.8 %-77.5 %]) and CNN (70.0 % [52.1 %-83.3 %]; all P < 0.001) in binary classifications. Moreover, CoHI achieved a higher sensitivity (82.4 % [59.0 %-93.8 %]) and specificity (76.9 % [49.7 %-91.8 %]) than individual dermatologists (sensitivity 77.8 % [75.3 %-80.2 %], specificity 73.0 % [70.6 %-75.4 %]) and CNN (sensitivity 70.6 % [46.9 %-86.7 %], specificity 69.2 % [42.4 %-87.3 %]). The diagnostic accuracy of CoHI was superior to that of individual dermatologists (P < 0.001) in multiclass evaluation, with the accuracy of the latter comparable to multiclass CNN., Conclusions: Our analysis revealed that the majority vote of an interconnected group of dermatologists (CoHI) outperformed individuals and CNN in a demanding skin lesion classification task., (© 2021 The Authors. Journal der Deutschen Dermatologischen Gesellschaft published by John Wiley & Sons Ltd on behalf of Deutsche Dermatologische Gesellschaft.)

Background and Objectives: Convolutional neural networks (CNN) have proven dermatologist-level performance in skin lesion classification. Prior to a broader clinical application, an assessment of limitations is crucial. Therefore, the influence of a dark tubular periphery in dermatoscopic images (also called dark corner artefact [DCA]) on the diagnostic performance of a market-approved CNN for skin lesion classification was investigated., Patients and Methods: A prospective image set of 233 skin lesions (60 malignant, 173 benign) without DCA (control-set) was modified to show small, medium or large DCA. All 932 images were analyzed by a market-approved CNN (Moleanalyzer-Pro ® , FotoFinder Systems), providing malignancy scores (range 0-1) with the cut-off > 0.5 indicating malignancy., Results: In the control-set the CNN achieved a sensitivity of 90.0 % (79.9 % - 95.3 %), a specificity of 96.5 % (92.6 % - 98.4 %), and an area under the curve (AUC) of receiver operating characteristics (ROC) of 0.961 (0.932 - 0.989). Comparable diagnostic performance was observed in the DCAsmall-set and DCAmedium-set. Conversely, in the DCAlarge-set significantly increased malignancy scores triggered a significantly decreased specificity (87.9 % [82.2 % - 91.9 %], P < 0.001), non-significantly increased sensitivity (96.7 % [88.6 % - 99.1 %]) and unchanged ROC-AUC of 0.962 (0.935 - 0.989)., Conclusions: Convolutional neural network classification was robust in images with small and medium DCA, but impaired in images with large DCA. Physicians should be aware of this limitation when submitting images to CNN classification., (© 2021 The Authors. Journal der Deutschen Dermatologischen Gesellschaft published by John Wiley & Sons Ltd on behalf of Deutsche Dermatologische Gesellschaft.)

Chronic prurigo is characterized by persistent itching und partly accompanied by secondary skin excoriation. Diagnostic evaluation is of special relevance and atopic diathesis is a frequent pathogenic factor. We present a patient with prurigo of multifactorial etiology (atopic diathesis, impaired kidney function, diabetes and polyneuropathy). After several unsuccessful prior treatment approaches, the patient was treated with dupilumab, which resulted in a tremendous improvement of itching, skin lesions, and quality of life.

Hintergrund Und Ziele: Systeme künstlicher Intelligenz (durch "deep learning" faltende neuronale Netzwerke; engl. convolutional neural networks, CNN) erreichen inzwischen bei der Klassifikation von Hautläsionen vergleichbar gute Ergebnisse wie Dermatologen. Allerdings müssen die Limitationen solcher Systeme vor flächendeckendem klinischem Einsatz bekannt sein. Daher haben wir den Einfluss des "dunklen Rand-Artefakts" (engl. dark corner artefact; DCA) in dermatoskopischen Bildern auf die diagnostische Leistung eines CNN mit Marktzulassung zur Klassifikation von Hautläsionen untersucht., Patienten Und Methoden: Ein Datensatz aus 233 Bildern von Hautläsionen (60 maligne und 173 benigne) ohne DCA (Kontrolle) wurde digital so modifiziert, dass kleine, mittlere oder große DCA zu sehen waren. Alle 932 Bilder wurden dann mittels CNN mit Marktzulassung (Moleanalyzer-Pro ® , FotoFinder Systems) auf Malignitätsscores hin analysiert. Das Spektrum reichte von 0-1; ein Score von > 0,5 wurde als maligne klassifiziert., Ergebnisse: In der Kontrollserie ohne DCA erreichte das CNN eine Sensitivität von 90,0 % (79,9 %-95,3 %), eine Spezifität von 96,5 % (92,6 %-98,4 %) sowie eine Fläche unter der Kurve (AUC, area under the curve) der "receiver operating characteristic" (ROC) von 0,961 (0,932-0,989). In den Datensätzen mit kleinen beziehungsweise mittleren DCA war die diagnostische Leistung vergleichbar. In den Bildersätzen mit großen DCA wurden allerdings signifikant höhere Malignitätsscores erzielt. Dies führte zu einer signifikant verminderten Spezifität (87,9 % [82,2 %-91,9 %], P < 0,001) sowie einer nicht signifikant erhöhten Sensitivität (96,7 % [88,6 %-99,1 %]). Die ROC-AUC blieb mit 0,962 (0,935-0,989) unverändert., Schlussfolgerungen: Die Klassifizierung mittels des CNN war bei dermatoskopischen Bildern mit kleinen oder mittleren DCA nicht beeinträchtigt, das System zeigte jedoch Schwächen bei großen DCA. Wenn Ärzte solche Bilder zur Klassifikation mittels CNN einreichen, sollten sie sich dieser Grenzen der Technologie bewusst sein., (© 2021 The Authors. Journal der Deutschen Dermatologischen Gesellschaft published by John Wiley & Sons Ltd on behalf of Deutsche Dermatologische Gesellschaft.)

Background: Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application., Objectives: The objective of the study was to investigate whether scale bars in dermoscopic images are associated with the diagnostic accuracy of a market-approved CNN., Methods: This cross-sectional analysis applied a CNN trained with more than 150,000 images (Moleanalyzer-pro®, FotoFinder Systems Inc., Bad Birnbach, Germany) to investigate seven dermoscopic image sets depicting the same 130 melanocytic lesions (107 nevi, 23 melanomas) without or with digitally superimposed scale bars of different manufacturers. Sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for the CNN's binary classification of images with or without superimposed scale bars were assessed., Results: Six dermoscopic image sets with different scale bars and one control set without scale bars (overall 910 images) were submitted to CNN analysis. In images without scale bars, the CNN attained a sensitivity [95% confidence interval] of 87.0% [67.9%-95.5%] and a specificity of 87.9% [80.3%-92.8%]. ROC AUC was 0.953 [0.914-0.992]. Scale bars were not associated with significant changes in sensitivity (range 87%-95.7%, all p ≥ 1.0). However, four scale bars induced a decrease of the CNN's specificity (range 0%-43.9%, all p < 0.001). Moreover, ROC AUC was significantly reduced by two scale bars (range 0.520-0.848, both p ≤ 0.042)., Conclusions: Superimposed scale bars in dermoscopic images may impair the CNN's diagnostic accuracy, mostly by increasing the rate of the false-positive diagnoses. We recommend avoiding scale bars in images intended for CNN analysis unless specific measures counteracting effects are implemented., Clinical Trial Number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; URL: https://www.drks.de/drks&#95;web/)., Competing Interests: Conflict of interest statement HA Haenssle received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening: SciBase AB, FotoFinder Systems GmbH, Heine Optotechnik GmbH and Magnosco GmbH. C Fink received travel expenses from Magnosco GmbH. All other authors state no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with dermatologists in FSL are lacking., Methods: A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL. The same lesions were used in a two-level reader study including 64 dermatologists (level I: dermoscopy only; level II: dermoscopy, clinical close-up images, textual information). Primary endpoints were the CNN's sensitivity and specificity in comparison with the dermatologists' management decisions in level II. Generalizability of the CNN results was tested by using four additional external data sets., Results: The CNN's sensitivity, specificity and ROC AUC were 96.2% [87.0%-98.9%], 68.8% [54.7%-80.1%] and 0.929 [0.880-0.978], respectively. In level II, the dermatologists' management decisions showed a mean sensitivity of 84.2% [82.2%-86.2%] and specificity of 69.4% [66.0%-72.8%]. When fixing the CNN's specificity at the dermatologists' mean specificity (69.4%), the CNN's sensitivity (96.2% [87.0%-98.9%]) was significantly higher than that of dermatologists (84.2% [82.2%-86.2%]; p < 0.001). Dermatologists of all training levels were outperformed by the CNN (all p < 0.001). In confirmation, the CNN's accuracy (83.0%) was significantly higher than dermatologists' accuracies in level II management decisions (all p < 0.001). The CNN's performance was largely confirmed in three additional external data sets but particularly showed a reduced specificity in one Australian data set including FSL on severely sun-damaged skin., Conclusions: When applied as an assistant system, the CNN's higher sensitivity at an equivalent specificity may result in an improved early detection of face and scalp skin cancers., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: A Blum received honoraria and/or travel expenses from Heine Optotechnik GmbH and FotoFinder Systems GmbH. C Fink received travel expenses from Magnosco GmbH. HA Haenssle received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening: Scibase AB, FotoFinder Systems GmbH, Heine Optotechnik GmbH, Magnosco GmbH. P Tschandl has received honoraria from Silverchair, and an unrestricted research grant from MetaOptima Technology Inc. R Hofmann-Wellenhof received honoraria and/or travel expenses from FotoFinder Systems GmbH and is founder and shareholder of e-derm-consult GmbH. All other authors declared no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Metabolic reprogramming and altered gene expression mediated by hypoxia-inducible factors play crucial roles during tumour growth and progression. Nevertheless, studies analysing the expression of hypoxia-inducible factor-1α and its downstream targets in Merkel cell carcinoma (MCC) are lacking but are warranted to shed more light on MCC pathogenesis and to potentially provide new therapeutic options., Objectives: To analyse the immunohistochemical expression of hypoxia-inducible factor-1α (HIF-1α), vascular endothelial growth factor-A (referred to as VEGF throughout the manuscript), VEGF receptor-2 (VEGFR-2), VEGF receptor-3 (VEGFR-3), glucose transporter-1 (Glut-1), monocarboxylate transporter 4 (MCT4) and carbonic anhydrase IX (CAIX) in primary cutaneous MCC., Methods: The 16 paraffin-embedded primary cutaneous MCCs (Merkel cell polyomavirus (McPyV) positive/negative: 11/5) were analysed by immunohistochemistry, namely HIF-1α, VEGF, VEGFR-2 (KDR), VEGFR-3 (FLT4), Glut-1, MCT4 and CAIX. An established quantification score (QS) was applied to quantitate the protein expression by considering the percentage of positive tumour cells (0: 0%; 1: up to 1%; 2: 2-10%; 3: 11-50%; 4: >50%) in relation to the staining intensity (0: negative; 1: low; 2: medium; 3: strong)., Results: HIF-1α was expressed in all MCCs and predominantly found at the invading edges of tumour margins. The HIF-1α downstream factors Glut-1, MCT4 and CAIX were expressed in 13 of 16 MCC (81%), 14 of 16 MCC (88%) and 16 of 16 MCC (100%), respectively. Interestingly, VEGF and VEGFR-2 were not expressed in tumour cells, whereas VEGFR-3 was expressed in all MCCs. HIF-1α was expressed significantly stronger in McPyV + tumours (QS: 10.36 ± 2.41) than in McPyV - tumours (QS: 5.40 ± 1.34; P = 0.002). Similarly, VEGFR-3 was also expressed significantly stronger in McPyV + tumours (QS: 10.00 ± 2.52) than in McPyV - tumours (QS: 5.40 ± 3.43, P = 0.019)., Conclusions: Our data provide first evidence for a role of HIF-1α in induced metabolic reprogramming contributing to MCC pathogenesis. The metabolic signatures of McPyV + and McPyV - tumours seem to show relevant differences., (© 2020 The Authors. Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.)

Tattoos, including permanent makeup, may entail diverse complications like viral or bacterial infections and allergic and inflammatory reactions. In the latter case, besides exogenous pigment, histology shows an either lymphocytic or histiocytic-granulomatous infiltrate, depending on the predominant reaction pattern. We report successful treatment with intralesional triamcinolone acetonide injections in two individuals who developed granulomatous inflammation after tattooing.

Advantages of Artificial Intelligence (ai): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer., Requirements and Possible Use of Smartphone Program Applications: Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.

This study analysed the expression of vascular endothelial growth factor-A (VEGF), VEGFR-2, and VEGFR-3 in primary cutaneous melanomas with positive and negative sentinel node status (SLN) (a total of 58 specimens divided into 2 groups of 29 for each status). The specimens were collected from the pathological archive of the department of Dermatology, Venereology and Allergology of the University Medical Center Heidelberg. A quantification score was developed for protein expression, by considering the percentage of positive melanoma cells (0: 0%, 1: up to 1%, 2: 2-10%, 3: 11-50%, and 4: > 50%) in relation to the intensity of staining (0: negative, 1: low, 2: medium, 3: strong). Tumoural VEGFR-3 expression (mean ± standard deviation) in SLN+ tumours (9.62 ± 3.09) was significantly stronger than in SLN- tumours (6.13 ± 3.87; p < 0.001). A binary logistic regression model proved VEGFR-3 expression and tumour thickness to be significant independent predictors of SLN. These data provide evidence that VEGFR-3 expression may play a critical role in the pathogenesis of malignant melanoma and that its investigation may help to improve the selection of patients with primary cutaneous melanoma for sentinel node biopsy.

Dermatoscopy as a noninvasive diagnostic tool is not only useful in the differentiation of malignant and benign skin tumors, but is also effective in the diagnosis of inflammatory, infiltrative and infectious dermatoses. As a result, the need for diagnostic punch biopsies in dermatoses could be reduced. Hereby the selection of affected skin areas is essential. The diagnostic accuracy is independent of the skin type. Helpful dermatoscopic features include vessels morphology and distribution, scales colors and distribution, follicular findings, further structures such as colors and morphology as well as specific clues. The dermatoscopic diagnosis is made based on the descriptive approach in clinical routine, teaching and research. In all clinical and dermatoscopic diagnoses that remain unclear, a punch biopsy with histopathology should be performed. The dermatoscope should be cleaned after every examination according to the guidelines.

Background: Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation and weighting of handcrafted features, to a CNN trained by deep learning., Methods: Cross-sectional study using a real-world, prospectively acquired, dermoscopic dataset of 1981 skin lesions to compare the diagnostic performance of a market-approved CNN (Moleanalyzer-Pro™, developed in 2018) to a CIA (Moleanalyzer-3™/Dynamole™; developed in 2004, all FotoFinder Systems Inc, Germany). As a reference standard, we used histopathological diagnoses (n = 785) or, in non-excised benign lesions (n = 1196), expert consensus plus an uneventful follow-up by sequential digital dermoscopy for at least 2 years., Results: A total of 281 malignant lesions and 1700 benign lesions from 435 patients (62.2% male, mean age: 52 years) were prospectively imaged. The CNN showed a sensitivity of 77.6% (95% confidence interval [CI]: [72.4%-82.1%]), specificity of 95.3% (95% CI: [94.2%-96.2%]), and receiver operating characteristic (ROC)-area under the curve (AUC) of 0.945 (95% CI: [0.930-0.961]). In contrast, the CIA achieved a sensitivity of 53.4% (95% CI: [47.5%-59.1%]), specificity of 86.6% (95% CI: [84.9%-88.1%]) and ROC-AUC of 0.738 (95% CI: [0.701-0.774]). The data set included melanomas originally diagnosed by dynamic changes during sequential digital dermoscopy (52 of 201, 20.6%), which reduced the sensitivities of both classifiers. Pairwise comparisons of sensitivities, specificities, and ROC-AUCs indicated a clear outperformance by the CNN (all p < 0.001)., Conclusions: The superior diagnostic performance of the CNN argues against a continued application of former CIAs as an aide to physicians' clinical management decisions., Competing Interests: Conflict of interest statement A.B. received honoraria and travel expenses from FotoFinder Systems Inc. and Heine Optotechnik Inc. H.A.H. received honoraria and/or travel expenses from companies involved in the development of devices for skin cancer screening: Scibase AB, FotoFinder Systems Inc., Heine Optotechnik Inc., Magnosco Inc. All the other authors had no conflict of interest. FotoFinder Systems Inc. had no role regarding study design or data interpretation., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: The melanin fluorescence of skin lesions is measurable with two-photon excitation, a process termed dermatofluoroscopy, which has shown a shift from the green spectra in benign melanocytic lesions to the red spectra in melanoma. This study addressed the question as to which kind of pigmented lesions can be correctly diagnosed as melanin-bearing malignant tumors., Methods: 476 pigmented lesions including 101 cutaneous melanomas were analyzed with dermatofluoroscopy, measuring the melanin fluorescence in a grid-like fashion with a separation of measurement points of 0.2 mm. The results of the dermatofluoroscopy are presented as a diagnostic score with a cut-off score of ≥ 28 for the diagnosis of melanin-bearing malignant tumors, and were compared to the gold standard of histopathology., Results: A highly significant difference (p < 0.0001) between the diagnostic scores of different skin tumors was found. Dermatofluoroscopy scores showed the highest sensitivity for melanomas (92.1 %). Interestingly, most pigmented basal cell carcinomas (BCCs, 88.9 %) were diagnosed as melanin-bearing malignant tumors. A higher sensitivity for the correct diagnosis was observed in older patients (≥ 53 years, p = 0.003), in patients with skin tanning (p = 0.025), and in patients with freckles during childhood (p = 0.046)., Conclusions: Two-photon fluorescence is an innovative technique for the diagnosis of pigmented skin lesions, and shows a high sensitivity for detection of melanomas and pigmented BCCs., (© 2020 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd.)