Support Tool Research Articles

User-Centered Framework for Implementation of Technology (UFIT): Development of an Integrated Framework for Designing Clinical Decision Support Tools Packaged With Tailored Implementation Strategies.

Electronic health record-based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011.

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit(ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance - where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees - and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update.

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, supportand integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.

Policy, practice, and prediction: model-based approaches to evaluating N. gonorrhoeae antibiotic susceptibility test uptake in Australia.

Antimicrobial resistance (AMR) represents a significant threat to global health with Neisseria gonorrhoea emerging as a key pathogen of concern. In Australia, the Australian Gonococcal Surveillance Program (AGSP) plays a critical role in monitoring resistance patterns. However, antibiotic susceptibility test (AST) uptake - a crucial component for effective resistance surveillance - remains to be a limiting factor. The study aims to model the processes involved in generating AST tests for N. gonorrhoea isolates within the Australian healthcare system and assess the potential impact of systematic and policy-level changes. Two models were developed. The first model was a mathematical stochastic health systems model (SHSM) and a Bayesian Belief Network (BBN) to simulate the clinician-patient dynamics influencing AST initiation. Key variables were identified through systematic literature review to inform the construction of both models. Scenario analyses were conducted with the modification of model parameters. The SHSM and BBN highlighted clinician education and the use of clinical support tools as effective strategies to improve AST. Scenario analysis further identified adherence to guidelines and changes in patient-level factors, such as persistence of symptoms and high-risk behaviours, as significant determinants. Both models supported the notion of mandated testing to achieve higher AST initiation rates but with considerations necessary regarding practicality, laboratory constraints, and culture failure rate. The study fundamentally demonstrates a novel approach to conceptualising the patient-clinician dynamic within AMR testing utilising a model-based approach. It suggests targeted interventions to educational, support tools, and legislative framework as feasible strategies to improve AST initiation rates. However, the research fundamentally highlights substantial research gaps in the underlying understanding of AMR.

An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer.

Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer. This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT. While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs. Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.

GAP-LSTM: Graph-Based Autocorrelation Preserving Networks for Geo-Distributed Forecasting.

Forecasting methods are important decision support tools in geo-distributed sensor networks. However, challenges such as the multivariate nature of data, the existence of multiple nodes, and the presence of spatio-temporal autocorrelation increase the complexity of the task. Existing forecasting methods are unable to address these challenges in a combined manner, resulting in a suboptimal model accuracy. In this article, we propose, a novel geo-distributed forecasting method that leverages the synergic interaction of graph convolution, attention-based long short-term memory (LSTM), 2-D-convolution, and latent memory states to effectively exploit spatio-temporal autocorrelation in multivariate data generated by multiple nodes, resulting in improved modeling capabilities. Our extensive evaluation, involving real-world datasets on traffic, energy, and pollution domains, showcases the ability of our method to outperform state-of-the-art forecasting methods. An ablation study confirms that all method components provide a positive contribution to the accuracy of the extracted forecasts. The method also provides an interpretable visualization that complements forecasts with additional insights for domain experts.

Managing organic resources in agriculture: future challenges from a scientific perspective

Recycling of organic resources into agriculture has the potential to greatly increase nutrient use efficiency and improve soil carbon balance, but improper management can have adverse effects on the environment. Agriculture therefore faces large challenges to increase yields while decreasing these emissions to the environment. In this paper, we review (i) the availability and composition of organic resources, (ii) their agronomic value and risk of emissions, (iii) potential measures to reduce their emissions, and (iv) future challenges to support farmers and policy makers. The total amount of organic resource applied to soil amounted on average 41 kg nitrogen per ha agricultural land, 9 kg phosphorus per ha, and 456 kg carbon per ha in EU-27 + UK in 2017. Solid pig and cattle manures and cattle slurry are the most used organic resources. The availability of new organic resources from food processing, sewage sludge, municipal bio-wastes, and upcoming manure treatment techniques as fertilizer or soil conditioner is expected to strongly increase over the coming decade. Insight is needed into the composition of organic resources, the plant-availability of nutrients, the degradability of organic matter and the presence of contaminants. Measurement techniques become available to characterize soils, manures, crops, and emissions to the environment. However, the interpretation, and integration of data, and recommendations to farmers and policymakers using large amounts of data is expected to become more and more challenging. Many measures are available to improve nutrient and carbon management and to reduce emissions, including proper application, technological measures and structural changes in agriculture. For many measures, there is a risk of trade-offs that could lead to pollution swapping at different scales. We should focus on finding synergies between measures and no-regret management choices to develop effective mitigation strategies. The main future challenge for managing organic resources in agriculture is the development of an integrated nutrient management approach, including (i) the characterization of organic resources, their agronomic value and their environmental risks, (ii) knowledge of potential synergies and trade-offs between management measures, and (iii) implementation of this knowledge into decision support tools, models and legislation to support farmers and policy makers.

Detection of Urban Growth in Taiz City, Yemen, Between 1981 and 2022 by Using Google Earth™ and Geographical Information System Data

Rapid urbanization brought serious socio-economic and environmental problems in many cities around the world, particularly in developing countries like Yemen. Urban "sprawl", a synonym of rapid and unplanned urban growth, is considered an obstacle for sustainable development, which causes serious long- and short-term problems, such as extreme infrastructure expenses, economic inefficiency, environmental destruction, and social instability. This is the case of Taiz City, the third largest city in Yemen, which is facing a huge challenge to manage the spatial extension of its built-up area at the expense of the reduction of natural resources and environment. This study aims to investigate urban growth process in Taiz City, focusing on urban sprawl and agricultural land-use change during 1981–2022. The data used are high resolution geospatial data, which are the topographic map for 1981 and Google Earth (GE) images for 2003, 2012, and 2022. The methodology adopted was manual digitizing using Geographic Information System (GIS) software to extract urban land use features from the images of the different dates. Four land-use maps were produced and used for calculation the urban sprawl and land-use changes. The urban expansion rate and patterns were also identified in three phases: 1981–2003, 2003–2012, and 2012–2022. The results show that the built-up area increased six times from 6.2 km2 in 1981 to 34 km2 in 2022, while the agriculture area decreased by 77% from 18 km2 in 1981 to 4.2 km2 in 2022. The urban sprawl in Taiz covered about 75% of its municipal area and extended to the agricultural and rural areas. The sprawl index (USI) is measured to be 5.4%, indicating a high degree of sprawl in Taiz. There is no open space left for future development, and the water resources are at risk of population and depletion. The study demonstrated that informal and haphazard expansion must be controlled, a development strategy should be prepared, so that sustainable urban growth can be achieved. The results of this study could be used as a decision support tool for urban management activities. The vector layers produced by this study could provide the required input data for the future urban modeling of the city.

Social Media and the Spiritual Journey: The Place of Digital Technology in Enriching the Experience

This qualitative study explores the utilization of social media among Pakistani pilgrims during spiritual journeys and investigates its impact on their pilgrimage experiences. Thirty Pakistani pilgrims who had embarked on spiritual journeys to various religious sites were interviewed using semi-structured interviews. Thematic analysis was employed to analyze the interview transcripts, revealing five main stages of social media usage: pre-trip preparation, real-time updates and guidance, community building and support, sharing experiences and insights, and post-trip reflection and engagement. At each stage, social media played diverse roles, including providing information and support, fostering connections with fellow pilgrims, sharing personal experiences, and facilitating spiritual dialogue. This explorative study underscores the significance of social media in enhancing the pilgrimage experience for Pakistani pilgrims, serving as a valuable tool for information dissemination, community building, spiritual support, and personal reflection throughout the pilgrimage journey. The findings contribute to a deeper understanding of how social media shapes pilgrims’ experiences and fosters their spiritual growth, emphasizing the need for further research to explore the nuanced dynamics of social media usage in the context of pilgrimage. Overall, this study sheds light on the unique role of social media in the spiritual journeys of Pakistani pilgrims and highlights its implications for pilgrimage practices and the broader discourse on religious tourism.

Use of the supportive and palliative care indicators tool (SPICT™) for end-of-life discussions: a scoping review

BackgroundIn order to mitigate the distress associated with life limiting conditions it is essential for all health professionals not just palliative care specialists to identify people with deteriorating health and unmet palliative care needs and to plan care. The SPICT™ tool was designed to assist with this.AimThe aim was to examine the impact of the SPICT™ on advance care planning conversations and the extent of its use in advance care planning for adults with chronic life-limiting illness.MethodsIn this scoping review records published between 2010 and 2024 reporting the use of the SPICT™, were included unless the study aim was to evaluate the tool for prognostication purposes. Databases searched were EBSCO Medline, PubMed, EBSCO CINAHL, APA Psych Info, ProQuest One Theses and Dissertations Global.ResultsFrom the search results 26 records were reviewed, including two systematic review, two theses and 22 primary research studies. Much of the research was derived from primary care settings. There was evidence that the SPICT™ assists conversations about advance care planning specifically discussion and documentation of advance care directives, resuscitation plans and preferred place of death. The SPICT™ is available in at least eight languages (many versions have been validated) and used in many countries.ConclusionsUse of the SPICT™ appears to assist advance care planning. It has yet to be widely used in acute care settings and has had limited use in countries beyond Europe. There is a need for further research to validate the tool in different languages.

Management of physical and psychological trauma resulting from motor vehicle crashes in Australian general practice: a mixed-methods approach

BackgroundIn Australia, motor vehicle crashes (MVC)-related health data are available from insurance claims and hospitals but not from primary care settings. This study aimed to identify the frequency of MVC-related consultations in Australian general practices, explore the pharmacological management of health conditions related to those crashes, and investigate general practitioners’ (GPs) perceived barriers and enablers in managing these patients.MethodsMixed-methods study. The quantitative component explored annual MVC-related consultation rates over seven years, the frequency of chronic pain, depression, anxiety or sleep issues after MVC, and management with opioids, antidepressants, anxiolytics or sedatives in a sample of 1,438,864 patients aged 16 + years attending 402 Australian general practices (MedicineInsight). Subsequently, we used content analysis of 81 GPs’ qualitative responses to an online survey that included some of our quantitative findings to explore their experiences and attitudes to managing patients after MVC.ResultsMVC-related consultation rates remained stable between 2012 and 2018 at around 9.0 per 10,000 consultations. In 2017/2018 compared to their peers, those experiencing a MVC had a higher frequency of chronic pain (48% vs. 26%), depression/anxiety (20% vs. 13%) and sleep issues (7% vs. 4%). In general, medications were prescribed more after MVC. Opioid prescribing was much higher among patients after MVC than their peers, whether they consulted for chronic pain (23.8% 95%CI 21.6;26.0 vs. 15.2%, 95%CI 14.5;15.8 in 2017/2018, respectively) or not (15.8%, 95%CI 13.9;17.6 vs. 6.7%, 95% CI 6.4;7.0 in 2017/2018). Qualitative analyses identified a lack of guidelines, local referral pathways and decision frameworks as critical barriers for GPs to manage patients after MVC. GPs also expressed interest in having better access to management tools for specific MVC-related consequences (e.g., whiplash/seatbelt injuries, acute/chronic pain management, mental health issues).ConclusionChronic pain, mental health issues and the prescription of opioids were more frequent among patients experiencing MVC. This reinforces the relevance of appropriate management to limit the physical and psychological impact of MVC. GPs identified a lack of available resources (e.g. education, checklists and management support tools) for managing MVC-related consequences, and the need for local referral pathways and specific guidelines to escalate treatments.

Towards FAIR Data Management in Heritage Science Research: Updates and Progress on the INFRA-ART Spectral Library

The heritage science sector is facing a critical need for accessible and comprehensive data resources to facilitate research, preservation efforts, and interdisciplinary collaboration. The concept of FAIR data management involves embracing principles and practices that ensure that data are Findable, Accessible, Interoperable, and Reusable. This work presents an overview of the latest updates on the INFRA-ART Spectral Library, an open access spectral database of cultural-heritage-related materials that was designed as a digital support tool for heritage research specialists that work with (portable) non- or minimally invasive spectroscopic techniques such as X-ray fluorescence (XRF), attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, or Raman spectroscopy, among others. The database is an ongoing compilation of high-quality curated data that currently incorporates primary ATR-FTIR and XRF spectra and a preliminary dataset of Raman and short-wave infrared (SWIR) reflectance spectra on over 900 different materials typically found in painted works of art. For increased and sustainable accessibility, the database follows the European Commission’s recommendations on access to scientific information, as well as the FAIR guiding principles on research data that result from publicly funded research. The INFRA-ART Spectral Library is registered as a resource within the Open Science Cloud (EOSC) Portal and is among the services offered by the Romanian hub within E-RIHS (European Research Infrastructure for Heritage Science) DIGILAB.

A Multi-Approach and Collaborative Pathway towards Designing Climate Strategies: The Case of Trentino, a Mountainous Region in Italy

Mountainous and rural territories are considered among the most vulnerable to the effects of the Climate Crisis. Their geographical and topographical conditions, together with the complexity of the system of relations between human and natural elements, amplify the impacts and risks associated with extreme climatic events. Therefore, the consequences on the socio-economic fabric are systemic and require an immediate response from all the actors in a region. Public administration and politics can play a pivotal role within the planning framework in contrasting the impacts of the climate crisis. This paper demonstrates the multi-level and multi-stakeholder approach applied during the development process of a regional adaptation strategy. The Autonomous Province of Trento has initiated an innovative approach to understanding and managing the effects of the climate crisis on its landscape and resources and has aligned the process of definition of its long-term strategy with the guidance provided by the EU Commission and the Italian Government. The methodology used is based on the tools provided by the Climate-ADAPT platform (in particular, the Regional Adaptation Support Tool—RAST), and the “impact chains” method is specifically implemented for climate risk assessment. The RAST is applied according to a multi-stakeholder and multi-level approach to capitalise on previously established and ongoing initiatives and working groups. The research suggests that the Climate Strategy model presented in this paper needs to be at the core of the broader framework adopted by the Autonomous Province of Trento and that it can represent an important example for other regions aiming to actively involve local stakeholders in acting for climate neutrality and resilience.

A predictive algorithm for the analysis of AMR trends and healthcare decision support

Background Translating available AMR surveillance data to observe evolving patterns of microbial resistance to antimicrobial agents while identifying regions at high risk of resistant cases and serving as a decision-support tool is an aspect of AMR surveillance that is rarely explored nationwide and uncommon globally. Therefore, we developed a two-tier dashboard algorithm (PATHFINDER) that can analyse antimicrobial surveillance datasets, observe evolving global spatiotemporal patterns of AMR, and integrate local AMR gene resources to identify functional AMR determinant genes and antibiotic classes from the query organism genome. Methods The Python-based plotly library was used to develop the interactive variables of the Antimicrobial Testing Leadership Surveillance (ATLAS) dataset in an adjustable spatiotemporal environment. A lightweight database containing multiple known resistant genes from the ResFinder database was used as a prototype to identify unique AMR determinant genes from query nucleotide sequences. In R, a function was created to accept query genome sequences and generate Kmers of length 250 using the blaster package. The GPT-4 API plug-in was embedded with adequate prompt parsing for it as an interpretation LLM function. Results AMR trendline plots were designed for invasive infections and customised based on the class of antibiotics and infection types on the surveillance dashboard. The decision-support tool correctly predicted resistant genes with a sensitivity of 75% on pre-confirmed organisms. The observed specificity score (51.5%) was due to the need for more filtering and optimisation and not to PATHFINDER performance. When run against the reference gene dataset containing pre-identified AMR genes, the support tool generated a BLAST table with identified AMR gene determinants in a nucleotide sequence. Conclusions The PATHFINDER algorithm has the potential to revolutionise healthcare decision-making. It can inform targeted interventions, guide antimicrobial stewardship efforts at a national level, promote appropriate antibiotic use, and significantly reduce the risk of resistance development.

Developing Robust AI Applications for Clinical Use: The Special Case of Pathology

Robustness is a key requirement for any method in medicine, especially when the method in question is being used as part of a diagnostic process. This is particularly true for artificial intelligence-based decision support systems, which, although being used as a supportive tool, will ultimately influence diagnostic assessments. In pathology, attaining clinical robustness in AI methods poses a particularly challenging task, primarily due to the extensive diversity of digital images, which humans can adapt to far more easily. This paper presents factors that contribute to this challenge, but also identifies and evaluates common solutions to counteract domain shift, which is known to deteriorate the performance of artificial intelligence models.

The quality of universal language supporting provision in maintained nursery classrooms in economically disadvantaged areas of England: Identifying strengths and areas for development

Background: Oral language competence provides children with an essential foundation for academic achievement and emotional well-being, yet many children enter school with delayed language and those living in economically disadvantaged areas are at disproportionate risk of experiencing language difficulties. A tiered system, offering high-quality language teaching for all children in early years classrooms, is widely acknowledged as an appropriate means of supporting language learning need. An understanding of the current quality of language supporting practices in English early years settings is essential to inform teaching practice and professional development, but evidence is limited. Methods: This study employed the Communication Supporting Classroom Observation tool in 39 nursery classrooms in economically disadvantaged areas of England to identify strengths of practice and key areas for development. Results: Observations revealed well-equipped physical learning environments and responsive teaching practices, but few structured opportunities for language learning and development. Variation in quality was observed across the nursery session. Conclusions: Findings suggest a need for adaptive professional development programmes supporting the more consistent use of structured language learning opportunities, the enhanced use of interaction promoting strategies and the provision of more complex and contingent language models. Further investigation of language supporting practice at a fine-grained level is desirable.

Effect of an in-hospital medication self-management intervention (SelfMED) on medication adherence in polypharmacy patients postdischarge: protocol of a pre–post intervention study

IntroductionHealthcare providers usually manage medication for patients during hospitalisation, although patients are expected to self-manage their medication after discharge. A lack of self-management competencies is found to be associated with...

What Role Does Simulation Play in Sustainable Industrial Development?

Sustainability as a concept is present in most aspects of our everyday life, and industry is no exception. Likewise, there is no doubt that the necessity to produce goods in a sustainable way and to ensure that products are sustainable is gaining more and more attention from producers, customers, governments, and various organizations. Understandably, there are several ways to increase the sustainable development of industrial production. One effective tool is simulation, which can have a significant impact on improving environmental, economic, and social sustainability. This paper explores the role of simulation as a powerful scientific and engineering solution in advancing sustainability within industrial ecosystems. Its main scope is to map the existing literature on the usage of simulation as a supportive tool for achieving this goal. For this purpose, a bibliometric analysis was conducted, allowing for tailored insights into the use of simulation in sustainable production.

Cutaneous Melanoma: A Case Report of a Nodular Melanoma, One of the Most Aggressive Presentations and with the Worst Prognosis

Melanoma is a malignant melanocytic tumor that constitutes the sixth most frequent cancer in the general population and has a high capacity to produce metastasis [1]. Cutaneous melanoma is the most common subtype, accounting for 91% cases. Even though it is reported less frequently than basal cell carcinoma or squamous cell carcinoma, it accounts for 70% mortality due to cutaneous neoplasms [5]. Diagnosis begins with a complete skin examination. Dermoscopy is an excellent support tool that suggests the diagnosis of melanoma [2].