Ultraviolet-Induced Fluorescence Dermoscopy Reveals Fluorescent Clues in Pitted Keratolysis

Ultraviolet-Induced Fluorescence Dermatoscopy Reveals Fluorescent Clues in Pitted Keratolysi

Patterns of Recurrence of Cutaneous Melanoma: A Literature Review

The incidence of melanoma has been dramatically increasing over the last decades. Melanoma is considered to have a high metastatic potential and it can progress due to hematogenous metastasis or via lymphatic vessels. Different patterns of recurrence have been described, namely, local, satellite, and in transit metastasis (LCIT), lymphatic, and systemic metastasis. With a more advanced melanoma stage at diagnosis, there is a higher risk for systemic metastasis in comparison to LCIT; in contrast, early-stage melanoma tends to recur more frequently as LCIT and less commonly as systematic metastasis. The aim of this review was to summarize the patterns of recurrence of cutaneous melanoma, giving the clinician a practical summary for diagnosis, prognosis, and surveillance. There is a knowledge gap of the common patterns of recurrence that needs to be addressed to better identify patients at high risk of disease recurrence and personalize surveillance strategies as well as patient counseling

Assessment of the Safety Risk of Dermatoscope Magnets in Patients With Cardiovascular Implanted Electronic Devices

Importance Cardiovascular implanted electronic devices (CIEDs) are susceptible to electromagnetic interference. Dermatologists regularly use devices containing magnets, including dermatoscopes and their attachments, which could pose a hazard to patients with CIEDs. Objective To investigate the safety risk of magnets in dermatoscopes to patients with CIEDs. Design, Setting, and Participants This cross-sectional observational study was conducted between January 1, 2018, and March 31, 2018, in a controlled laboratory setting. Two experiments were performed. In the first experiment (performed in the Dermatology Service at Memorial Sloan Kettering Cancer Center, New York), dermatoscopes that contain magnets were obtained from 3 manufacturers. Using a magnometer, the magnetic field strength of the dermatoscopes was measured over the magnet; at the faceplate; and at a distance of 0.5 cm, 1 cm and 15 cm away from the faceplate. In the second experiment (performed in the University Heart Center Zurich, Zurich, Switzerland), ex vivo measurements were conducted to determine how the dermatoscopes affected old-generation and new generation CIEDs (pacemakers and implantable defibrillators). Main Outcomes and Measures Magnetic field strength as measured directly over the dermatoscope magnet; at the faceplate; and at distances of 0.5 cm, 1 cm, and 15 cm from the faceplate. Pacemaker and defibrillator operation when exposed to dermatoscopes. Results After conducting 24 measurements, the magnetic field (measured in gauss [G]) strength varied between 24.26 G and 163.04 G over the dermatoscope magnet, between 2.22 G and 9.98 G at the dermatoscope faceplate, between 0.82 G and 2.4 G at a distance of 0.5 cm, and between 0.5 G and 1.04 G at a distance of 1 cm; it was 0 for all devices at a 15 cm distance. The field strength at the faceplate was found to be generally below the CIED industry standard safety threshold. None of the dermatoscopes in the ex vivo experiment exerted any demonstrable disruptions or changes to the CIEDs. Conclusions and Relevance In real life, dermatoscope magnets likely present no measurable safety risk to patients with CIEDs. Using the polarized noncontact mode permits dermoscopy to be performed at least 0.5 cm from the skin surface, where the magnetic field strength was well below the 5-G safety threshold

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI‐assisted smartphone apps and web‐based services for skin disease

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.MethodsAn 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

Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration

BackgroundThere is no internationally vetted set of anatomic terms to describe human surface anatomy.ObjectiveTo establish expert consensus on a standardized set of terms that describe clinically relevant human surface anatomy.MethodsWe conducted a Delphi consensus on surface anatomy terminology between July 2017 and July 2019. The initial survey included 385 anatomic terms, organized in seven levels of hierarchy. If agreement exceeded the 75% established threshold, the term was considered - accepted- and included in the final list. Terms added by the participants were passed on to the next round of consensus. Terms with <75% agreement were included in subsequent surveys along with alternative terms proposed by participants until agreement was reached on all terms.ResultsThe Delphi included 21 participants. We found consensus (- ¥75% agreement) on 361/385 (93.8%) terms and eliminated one term in the first round. Of 49 new terms suggested by participants, 45 were added via consensus. To adjust for a recently published International Classification of Diseases- Surface Topography list of terms, a third survey including 111 discrepant terms was sent to participants. Finally, a total of 513 terms reached agreement via the Delphi method.ConclusionsWe have established a set of 513 clinically relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation.Linked Commentary: R.J.G. Chalmers. J Eur Acad Dermatol Venereol 2020; 34: 2456- 2457. https://doi.org/10.1111/jdv.16978.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163915/1/jdv16855_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163915/2/jdv16855-sup-0001-FigS1-S3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163915/3/jdv16855.pd

Human-computer collaboration for skin cancer recognition

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice

Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna

Introduction: Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up. Objectives: To obtain general consensus on the diagnosis, treatment, and follow-up for LM. Methods: A modified Delphi method was used. The invited participants were either members of the International Dermoscopy Society, academic experts, or authors of published articles relating to skin cancer and melanoma. Participants were required to respond across three rounds using a 4-point Likert scale). Consensus was defined as >75% of participants agreeing/strongly agreeing or disagreeing/strongly disagreeing. Results: Of the 31 experts invited to participate in this Delphi study, 29 participants completed Round 1 (89.9% response rate), 25/31 completed Round 2 (77.5% response rate), and 25/31 completed Round 3 (77.5% response rate). Experts agreed that LM diagnosis should be based on a clinical and dermatoscopic approach (92%) followed by a biopsy. The most appropriate primary treatment of LM was deemed to be margin-controlled surgery (83.3%), although non-surgical modalities, especially imiquimod, were commonly used either as alternative off-label primary treatment in selected patients or as adjuvant therapy following surgery; 62% participants responded life-long clinical follow-up was needed for LM. Conclusions: Clinical and histological diagnosis of LM is challenging and should be based on macroscopic, dermatoscopic, and RCM examination followed by a biopsy. Different treatment modalities and follow-up should be carefully discussed with the patient