Use of Smartphones for Early Detection of Melanoma: Systematic Review

Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks

Skin cancer is one of the most prevalent malignant tumors, and early detection is crucial for patient prognosis, leading to the development of mobile applications as screening tools. Recent advances in deep neural networks (DNNs) have accelerated the deployment of DNN-based applications for automated skin cancer detection. While DNNs have demonstrated remarkable capabilities, they are known to be vulnerable to adversarial attacks, where carefully crafted perturbations can manipulate model predictions. The vulnerability of deployed medical mobile applications to such attacks remains largely unexplored under real-world conditions. Here, we investigate the susceptibility of three DNN-based medical mobile applications to physical adversarial attacks using transparent camera stickers under black-box conditions where internal model architectures are inaccessible. Through digital experiments with various DNN architectures trained on a publicly available skin lesion dataset, we first demonstrate that camera-based adversarial patterns can achieve high transferability across different models. Using these findings, we implement physical attacks by attaching optimized transparent stickers to mobile device cameras. Our results show that these attacks successfully manipulate application predictions, particularly for melanoma images, with attack success rates reaching 50-80% across all applications while maintaining visual imperceptibility. Notably, melanoma images showed consistently higher vulnerability compared to nevus images across all tested applications. To the best of our knowledge, this is the first demonstration of real-world adversarial vulnerabilities in deployed medical mobile applications, revealing significant security concerns where prediction manipulation could affect diagnostic processes. Our study demonstrates the importance of security evaluation in deploying such applications in clinical settings.

Challenges in Teledermoscopy Diagnostic Outcome Studies: Scoping Review of Heterogeneous Study Characteristics

BACKGROUND

Teledermoscopy has demonstrated benefits such as decreased costs and enhanced access to dermatology care for skin cancer detection. However, the heterogeneity among teledermoscopy studies hinders the systematic reviews' synopsis of diagnostic outcomes, impeding trust and adoption in general practice and limiting overall health care benefits.

OBJECTIVE

This study aims to improve understanding and standardization of teledermoscopy diagnostic studies, by identifying and categorizing study characteristics contributing to heterogeneity. Subsequently, the variability and consistency of these characteristics were assessed.

METHODS

A review of systematic reviews regarding the diagnostic outcomes of teledermoscopy was performed to discern reported study characteristics contributing to heterogeneity. These characteristics were thematically grouped into 3 domains (population, index test, and reference standard), forming a data extraction framework. A scoping review on teledermoscopy diagnostic outcomes studies was performed, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Data pertaining to study characteristics from included studies were extracted and analyzed through descriptive content analysis. Systematic reviews' reference lists validated the scoping review query.

RESULTS

The literature search yielded 4 systematic reviews, revealing 15 heterogeneous studies across the population, index test, and reference standard domains. The scoping review identified 49 studies, with 27 overlapping with the systematic reviews. Population characteristics varied, with one-third (16/49, 33%) of studies reporting fewer than 100 samples; most studies (41/49, 84%) reported on the type of lesion, and most (20/49, 41%) teledermoscopy consultations took place in secondary care. One-fifth (11/49, 22%) did not describe inclusion or exclusion criteria, or the criteria varied highly. Index test characteristics showed differences in clinical expertise, profession, and training in dermatoscopic photography, and 59% (29/49) did not report on 1 or more index test characteristics. Image quality and clinical information reporting likewise varied. Reference standard characteristics involved teledermatologists' assessment, but 16 studies did not report teledermatologists' experience levels. Most studies (26/49, 53%) used histopathology as a gold standard.

CONCLUSIONS

The heterogeneity in the population, index tests, and reference standard domains across teledermoscopy diagnostic outcome studies underscores the need for standardized reporting. This hinders the synopsis of teledermoscopy diagnostic outcomes in systematic reviews and limits the integration of research results into practice. Adopting a (tailored) STARD (Standards for Reporting Diagnostic Accuracy Studies) checklist for teledermoscopy diagnostic outcome studies is recommended to enhance the consistency and comparability of outcomes. We suggest performing a Delphi study to gather consensus on the tailored STARD guideline.

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

BACKGROUND

Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking.

OBJECTIVE

The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India.

METHODS

This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model's performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1-score. Comparison of categorical variables was performed with the χ2 test and statistical significance was considered at P<.05.

RESULTS

A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001).

CONCLUSIONS

The Aysa app showed promising results in identifying most dermatoses.

Early diagnosis of melanoma: a randomized trial assessing the impact of the transmission of photographs taken with a smartphone from the general practitioner to the dermatologist on the time to dermatological consultation

BACKGROUND

Difficulty obtaining a dermatological consultation is an obstacle to the early diagnosis of melanoma. On the one hand, patients survival depends on the lesion thickness at the time of diagnosis. On the other hand, dermatologists treat many patients with benign lesions. Optimizing patient care pathways is a major concern. The aim of the present study was to assess whether the e-mail transmission of photographs of suspected melanoma lesions between general practitioners (GPs) and dermatologists reduces the time to dermatological consultation for patients whose suspicious skin lesions ultimately require resection.

METHODS

We conducted a cluster-randomized controlled study in primary care involving 51 French GPs between April 2017 and August 2019. A total of 250 patients referred to a dermatologist for a suspected melanoma lesion were included GPs were randomized to either the smartphone arm or the usual care arm. In the smartphone arm, the GPs referred patients to the dermatologist by sending 2 photographs of the suspicious lesion using their smartphone. The dermatologist then had to set up an appointment at an appropriate time. In the usual care arm, GPs referred patients to a dermatologist according to their usual practice. The primary outcome was the time to dermatological consultation for patients whose lesion ultimately required resection.

RESULTS

57 GPs volunteered were randomized (27 to the smartphone arm, and 30 to the usual care arm). A total of 125 patients were included in each arm (mean age: 49.8 years; 53% women) and followed 8 months. Twenty-three dermatologists participated in the study. The time to dermatological consultation for patients whose suspicious skin lesion required resection was 56.5 days in the smartphone arm and 63.7 days in the usual care arm (mean adjusted time reduction: -18.5 days, 95% CI [-74.1;23.5], p = .53).

CONCLUSIONS

The e-mail transmission of photographs from GPs to dermatologists did not improve the dermatological management of patients whose suspicious skin lesions ultimately required resection. Further research is needed to validate quality criteria that might be useful for tele-expertise in dermatology.

TRIAL REGISTRATION

Registered on ClinicalTrials.gov under reference number NCT03137511 (May 2, 2017).

Artificial intelligence in the detection of skin cancer: State of the art

The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are effective tools supporting diagnosticians in many medical specialties. In this contribution, a number of applications of different classes of AI algorithms for the detection of this skin melanoma are presented and evaluated. Both classic systems based on the analysis of dermatoscopic images as well as total body systems, enabling the analysis of the patient's whole body to detect moles and pathologic changes, are discussed. These increasingly popular applications that allow the analysis of lesion images using smartphones are also described. The quantitative evaluation of the discussed systems with particular emphasis on the method of validation of the implemented algorithms is presented. The advantages and limitations of AI in the analysis of lesion images are also discussed, and problems requiring a solution for more effective use of AI in dermatology are identified.

Impact of digital services on healthcare and social welfare: An umbrella review

BACKGROUND

Digital services can be effective and cost-efficient options for treating non-communicable diseases, but generalizability is limited due to heterogeneous treatment effects. This umbrella review aims to evaluate the impact of digital services on population health, costs, and patient and healthcare professional satisfaction, and to identify facilitators and barriers to using digital services in healthcare and social welfare.

METHODS

The protocol of the study was registered on the 4th of September 2022 to the International Prospective Register of Systematic Reviews, PROSPERO (CRD42022355635). The review was performed using the Centre for Reviews and Dissemination, Cochrane, Ovid Medline, Scopus, and Web of Science in June 2022. The methodological quality of the included reviews was assessed. The impact of digital services was categorized as no evidence, no dominance, and mixed and positive effect. Inductive content analysis was used to identify facilitators and barriers.

RESULTS

A total of 66 studies were included in the review, 64 % of which were evaluated as high quality. Studies on the impact of digital services in social welfare were not identified. Sixty-five percent of reviews evaluated the impact of digital services on population health with mixed effects; 21 % were on costs with mixed effects; 27 % were on patient satisfaction with positive effects; and 7.6 % were on healthcare professionals' satisfaction with mixed effects. Various features, allocation, end-user support, organized services, and service development facilitated the use of digital services. Correspondingly, barriers were related to service limitations, digital competency, funding- and service strategies, resources and change management.

CONCLUSIONS

Compared to usual care, digital services had a mixed impact on population health and costs with high satisfaction in patients. Mixed healthcare professionals' satisfaction was associated with the use of digital services, and it was less studied. To ensure successful implementation and sustainability of digital services, attention must be paid to address barriers and supporting facilitators at all levels.

Basic principles of artificial intelligence in dermatology explained using melanoma

The use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.

Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms

Der Einsatz von künstlicher Intelligenz (KI) setzt sich in den verschiedensten Bereichen der Medizin immer schneller durch. Dennoch fehlt vielen medizinischen Kollegen das technische Grundverständnis für die Funktionsweise dieser Technologie, was ihre Anwendung in Klinik und Forschung stark einschränkt. Daher möchten wir in dieser Übersichtsarbeit die Funktionsweise und Klassifizierung der KI am Beispiel des Melanoms erörtern, um ein Verständnis für die Technologie hinter der KI zu schaffen. Dazu werden ausführliche Illustrationen verwendet, die die Technologie schnell erklären. Bisherige Übersichten konzentrieren sich eher auf die potenziellen Anwendungen der KI und verpassen die Gelegenheit, ein tieferes Verständnis für die Materie herauszuarbeiten, das für die klinische Anwendung so wichtig ist. Das maligne Melanom ist zu einer erheblichen Belastung für die Gesundheitssysteme geworden. Bei frühzeitiger Entdeckung ist eine bessere Prognose zu erwarten, weshalb das Hautkrebs‐Screening immer populärer und von den Krankenkassen unterstützt wird. Die Zahl der Fachärzte ist jedoch begrenzt, was ihre Verfügbarkeit einschränkt und zu längeren Wartezeiten führt. Daher müssen innovative Ideen umgesetzt werden, um die notwendige Versorgung zu gewährleisten. Das maschinelle Lernen bietet die Möglichkeit, Melanome auf Bildern zu erkennen, und zwar auf einem Niveau, das mit dem von erfahrenen Dermatologen – unter optimierten Bedingungen – vergleichbar ist.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

The NLR SkinApp: Testing a Supporting mHealth Tool for Frontline Health Workers Performing Skin Screening in Ethiopia and Tanzania

Background: The prevalence of skin diseases such as leprosy, and limited dermatological knowledge among frontline health workers (FHWs) in rural areas of Sub-Saharan Africa, led to the development of the NLR SkinApp: a mobile application (app) that supports FHWs to promptly diagnose and treat, or suspect and refer patients with skin diseases. The app includes common skin diseases, neglected tropical skin diseases (skin NTDs) such as leprosy, and HIV/AIDS-related skin conditions. This study aimed to test the supporting role of the NLR SkinApp by examining the diagnostic accuracy of its third edition. Methods: A cross-sectional study was conducted in East Hararghe, Ethiopia, as well as the Mwanza and Morogoro region, Tanzania, in 2018-2019. Diagnostic accuracy was measured against a diagnosis confirmed by two dermatologists/dermatological medical experts (reference standard) in terms of sensitivity, specificity, positive predictive value, and negative predictive value. The potential negative effect of an incorrect management recommendation was expressed on a scale of one to four. Results: A total of 443 patients with suspected skin conditions were included. The FHWs using the NLR SkinApp diagnosed 45% of the patients accurately. The values of the sensitivity of the FHWs using the NLR SkinApp in determining the correct diagnosis ranged from 23% for HIV/AIDS-related skin conditions to 76.9% for eczema, and the specificity from 69.6% for eczema to 99.3% for tinea capitis/corporis. The inter-rater reliability among the FHWs for the diagnoses made, expressed as the percent agreement, was 58% compared to 96% among the dermatologists. Of the management recommendations given on the basis of incorrect diagnoses, around one-third could have a potential negative effect. Conclusions: The results for diagnosing eczema are encouraging, demonstrating the potential contribution of the NLR SkinApp to dermatological and leprosy care by FHWs. Further studies with a bigger sample size and comparing FHWs with and without using the NLR SkinApp are needed to obtain a better understanding of the added value of the NLR SkinApp as a mobile health (mHealth) tool in supporting FHWs to diagnose and treat skin diseases.

The Role of Dermoscopy in Provider-to-Provider Store-and-Forward Dermatology eConsults: A Scoping Review of the Recent Literature

Purpose of Review

This scoping review maps recent literature on dermatology provider-to-provider asynchronous store-and-forward (SAF) electronic consult (eConsult) platforms with dermoscopy. It offers a descriptive overview, highlighting benefits and challenges.

Recent Findings

Incorporating dermoscopy into SAF eConsults improves diagnostic accuracy for benign and malignant skin neoplasms. Diagnostic and treatment concordance with traditional face-to-face (FTF) visits is high. SAF eConsults with dermoscopy enhance access to dermatological care by improving triage and reducing wait times for FTF visits. Pediatric patients benefit with improved evaluation of melanocytic and vascular growths. eConsult platforms with dermoscopy serve as a telementoring opportunity for clinicians interested in improving their dermoscopy skills.

Summary

Adding dermoscopy to SAF eConsults is valuable and results in improved diagnostic accuracy and reduced need for FTF visits. Implementation barriers can be overcome through collaboration between primary care and dermatology. Dermoscopy in SAF eConsults has significant potential for managing skin conditions and reducing the burden caused by unnecessary FTF visit and biopsies.

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

Digital health and telehealth in cancer care: a scoping review of reviews

The COVID-19 pandemic necessitated remote cancer care delivery via the internet and telephone, rapidly accelerating an already growing care delivery model and associated research. This scoping review of reviews characterised the peer-reviewed literature reviews on digital health and telehealth interventions in cancer published from database inception up to May 1, 2022, from PubMed, Cumulated Index to Nursing and Allied Health Literature, PsycINFO, Cochrane Reviews, and Web of Science. Eligible reviews conducted a systematic literature search. Data were extracted in duplicate via a pre-defined online survey. Following screening, 134 reviews met the eligibility criteria. 77 of those reviews were published since 2020. 128 reviews summarised interventions intended for patients, 18 addressed family caregivers, and five addressed health-care providers. 56 reviews did not target a specific phase of the cancer continuum, whereas 48 reviews tended to address the active treatment phase. 29 reviews included a meta-analysis, with results showing positive effects on quality of life, psychological outcomes, and screening behaviours. 83 reviews did not report intervention implementation outcomes but when reported, 36 reported acceptability, 32 feasibility, and 29 fidelity outcomes. Several notable gaps were identified in these literature reviews on digital health and telehealth in cancer care. No reviews specifically addressed older adults, bereavement, or sustainability of interventions and only two reviews focused on comparing telehealth to in-person interventions. Addressing these gaps with rigorous systematic reviews might help guide continued innovation in remote cancer care, particularly for older adults and bereaved families, and integrate and sustain these interventions within oncology.

Empowerment through health self-testing apps? Revisiting empowerment as a process

Empowerment, an already central concept in public health, has gained additional relevance through the expansion of mobile health (mHealth). Especially direct-to-consumer self-testing app companies mobilise the term to advertise their products, which allow users to self-test for various medical conditions independent of healthcare professionals. This article first demonstrates the absence of empowerment conceptualisations in the context of self-testing apps by engaging with empowerment literature. It then contrasts the service these apps provide with two widely cited empowerment definitions by the WHO, which describe the term as a process that, broadly, leads to knowledge and control of health decisions. We conclude that self-testing apps can only partly empower their users, as they, we argue, do not provide the type of knowledge and control the WHO definitions describe. More importantly, we observe that this shortcoming stems from the fact that in the literature on mHealth and in self-testing marketing, empowerment is understood as a goal rather than a process. This characterises a shift in the meaning of empowerment in the context of self-testing and mHealth, one that reveals a lack of awareness for relational and contextual factors that contribute to empowerment. We argue that returning to a process-understanding of empowerment helps to identify these apps' deficits, and we conclude the article by briefly suggesting several strategies to increase self-testing apps' empowerment function.

The role of mobile teledermoscopy in skin cancer triage and management during the COVID-19 pandemic

The unprecedented onset of the COVID-19 crisis poses a significant challenge to all fields of medicine, including dermatology. Since the start of the coronavirus outbreak, a stark decline in new skin cancer diagnoses has been reported by countries worldwide. One of the greatest challenges during the pandemic has been the reduced access to face-to-face dermatologic evaluation and non-urgent procedures, such as biopsies or surgical excisions. Teledermatology is a well-integrated alternative when face-to-face dermatological assistance is not available. Teledermoscopy, an extension of teledermatology, comprises consulting dermoscopic images to improve the remote assessment of pigmented and non-pigmented lesions when direct visualisation of lesions is difficult. One of teledermoscopy's greatest strengths may be its utility as a triage and monitoring tool, which is critical in the early detection of skin cancer, as it can reduce the number of unnecessary referrals, wait times, and the cost of providing and receiving dermatological care. Mobile teledermoscopy may act as a communication tool between medical practitioners and patients. By using their smartphone (mobile phone) patients can monitor a suspicious skin lesion identified by their medical practitioner, or alternatively self-detect concerning lesions and forward valuable dermoscopic images for remote medical evaluation. Several mobile applications that allow users to photograph suspicious lesions with their smartphones and have them evaluated using artificial intelligence technology have recently emerged. With the growing popularity of mobile apps and consumer-involved healthcare, this will likely be a key component of skin cancer screening in the years to come. However, most of these applications apply artificial intelligence technology to assess clinical images rather than dermoscopic images, which may lead to lower diagnostic accuracy. Incorporating the direct-to-consumer mobile dermoscopy model in combination with mole-scanning artificial intelligence as a mobile app may be the future of skin cancer detection.

Artificial Intelligence in Dermatology: Challenges and Perspectives

Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.

Telemedicina: validación de un cuestionario para evaluar la experiencia de los profesionales de la salud

Objetivo.

El siguiente trabajo tiene como objetivo desarrollar y validar un cuestionario para evaluar la experiencia de los profesionales de la salud con los sistemas de telemedicina.

Métodos.

A partir de la versión abreviada en español y validada localmente del cuestionario para pacientes desarrollado por Parmanto y col., un grupo de expertos consensuó una versión para evaluar la experiencia de profesionales de la salud que brindan servicios de telemedicina. El comportamiento psicométrico de los ítems se testeó en una primera muestra de 129 profesionales a través de un análisis factorial exploratorio. Luego, se evaluó su comprensibilidad a través de entrevistas cognitivas. Por último, en una nueva muestra de 329 profesionales, se evaluó la validez de constructo del cuestionario mediante un análisis factorial confirmatorio (AFC), y su validez de criterio externo, mediante la evaluación de su puntaje con el de una pregunta de resumen.

Resultados.

Se obtuvo un cuestionario de 12 ítems con una estructura de dos factores con indicadores de ajuste aceptables, documentada mediante AFC. La fiabilidad, la validez convergente y la validez discriminante fueron apropiadas. La validez de criterio externo mostró resultados óptimos.

Conclusiones.

El instrumento obtenido cuenta con propiedades psicométricas adecuadas y contribuirá a la evaluación objetiva de la experiencia de los profesionales que realizan telemedicina.

[Translated article] Current State of Skin Cancer Prevention: A Systematic Review

Skin cancer deaths continue to rise despite the implementation of numerous preventive campaigns and programs. The aim of this systematic review was to evaluate reviews of primary and secondary skin cancer prevention strategies as reported over the past 10 years. We analyzed 63 systematic reviews and meta-analyses: 30 (46.6%) addressing primary interventions and 35 (55.6%) addressing secondary interventions. Two of the reviews covered both. The most widely reported primary prevention approaches were education programs (63.3%), followed by risk modeling to identify individuals at high risk for melanoma (17.6%), and the promotion of sunscreen use (11.8%). The most widely reported secondary prevention measures concerned imaging systems for early skin cancer detection (40%), smartphones and new technologies (22.9%), and visual diagnosis in population-based screening (17.4%). The most effective measures were primary prevention education programs to improve sun protection habits.

Store-and-Forward Images in Teledermatology: Narrative Literature Review

Background

Store-and-forward (SAF) teledermatology uses electronically stored information, including patient photographs and demographic information, for clinical decision-making asynchronous to the patient encounter. The integration of SAF teledermatology into clinical practice has been increasing in recent years, especially during the COVID-19 pandemic. Despite this growth, data regarding the outcomes of SAF teledermatology are limited. A key distinction among current literature involves comparing the quality and utility of images obtained by patients and trained clinicians, as these metrics may vary by the clinical expertise of the photographer.

Objective

This narrative literature review aimed to characterize the outcomes of SAF teledermatology through the lens of patient- versus clinician-initiated photography and highlight important future directions for and challenges of the field.

Methods

A literature search of peer-reviewed research was performed between February and April 2021. Key search terms included patient-initiated, patient-submitted, clinician-initiated, clinician-submitted, store-and-forward, asynchronous, remote, image, photograph, and teledermatology. Only studies published after 2001 in English were included. In total, 47 studies were identified from the PubMed electronic database and Google Scholar after omitting duplicate articles.

Results

Image quality and diagnostic concordance are generally lower and more variable with patient-submitted images, which may impact their decision-making utility. SAF teledermatology can improve the efficiency of and access to care when photographs are taken by either clinicians or patients. The clinical outcomes of clinician-submitted images are comparable to those of in-person visits in the few studies that have investigated these outcomes. Coinciding with the onset of the COVID-19 pandemic, asynchronous teledermatology helped minimize unnecessary in-person visits in the outpatient setting, as many uncomplicated conditions could be adequately managed remotely via images captured by patients and referring clinicians. For the inpatient setting, SAF teledermatology minimized unnecessary contact during dermatology consultations, although current studies are limited by the heterogeneity of their outcomes.

Conclusions

In general, photographs taken by trained clinicians are higher quality and have better and more relevant diagnostic and clinical outcomes. SAF teledermatology helped clinicians avoid unnecessary physical contact with patients in the outpatient and inpatient settings during the COVID-19 pandemic. Asynchronous teledermatology will likely play a greater role in the future as SAF images become integrated into synchronous teledermatology workflows. However, the obstacles summarized in this review should be addressed before its widespread implementation into clinical practice.

Digital skin imaging applications, part I: Assessment of image acquisition technique features

BACKGROUND

The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality.

MATERIALS AND METHODS

Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both).

RESULTS

Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching.

CONCLUSION

Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.

LncRNA LNCOC1 is Upregulated in Melanoma and Serves as a Potential Regulatory Target of miR-124 to Suppress Cancer Cell Invasion and Migration

Background

A cascade of genes and pathways have been reported in the precise regulation of malignant melanoma (MM). Previous study has demonstrated that lncRNA LNCOC1 is an oncogenic factor in the pathogenesis and development of various cancers. The present study explored the functionalities of LNCOC1 and its interactions with miR-124 in MM.

Methods

A total of 65 melanoma patients were enrolled in this study. The expression of LNCOC1 and miR-124 after cell transfection were detected by RT-qPCR. The migration rates of SK-MEL-3 and A375 cells after transient transfection with LNCOC1 expression vector and miR-124 mimic was detected by trans-well assay.

Results

LNCOC1 was accumulated to high levels in melanoma, and it was significantly correlated with the low survival rate of melanoma patients. Our bioinformatics data showed that miR-124 could target LNCOC1. Overexpression of miR-124 could downregulate LNCOC1. However, up-regulated the expression of LNCOC1 did not affect the expression of miR-124. Our correlation analysis also revealed that the expression of LNCOC1 and miR-124 were inversely correlated in both melanoma tissues and non-tumor tissues. The trans-well invasion and migration assays indicated that overexpression of miR-124 inhibited the melanoma cell invasion and migration. However, overexpression of LNCOC1 promoted melanoma cell invasion and migration.

Conclusion

LNCOC1 is upregulated in melanoma, which can be considered as a potential target of miR-124 in modulating melanoma cell invasion and migration. LNCOC1 may also be an interfering target of MM therapy.

The Future of Precision Prevention for Advanced Melanoma

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.

Clinical Decision Support Systems

BACKGROUND

By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge.

SUMMARY AND KEY MESSAGES

Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

Remote Skin Cancer Diagnosis: Adding Images to Electronic Referrals Is More Efficient Than Wait-Listing for a Nurse-Led Imaging Clinic

Simple Summary

Skin cancer is a significant cause of death and disability, particularly in New Zealand. Expert diagnosis reduces unnecessary excision of benign lesions, reduces patient anxiety, and allows early identification of skin cancer, particularly of melanoma. The study assessed an electronic referral pathway for teledermatology—diagnosing skin lesions remotely using a standardised template with regional, close-up, and dermoscopic images—and compared this to scheduled nurse-led teledermoscopy clinics. A dermatology opinion was reached more rapidly with comparable efficacy when referrals include good quality images, compared to nurse-led imaging clinics.

Abstract

We undertook a retrospective comparison of two teledermatology pathways that provide diagnostic and management advice for suspected skin cancers, to evaluate the time from referral to diagnosis and its concordance with histology. Primary Care doctors could refer patients to either the Virtual Lesion Clinic (VLC), a nurse-led community teledermoscopy clinic or, more recently, to the Suspected Skin Cancer (SSC) pathway, which requires them to attach regional, close-up, and dermoscopic images. The primary objective of this study was to determine the comparative time course between the SSC pathway and VLC. Secondary objectives included comparative diagnostic concordance, skin lesion classification, and evaluation of missed skin lesions during subsequent follow-up. VLC referrals from July to December 2016 and 2020 were compared to SSC referrals from July to December 2020. 408 patients with 682 lesions in the VLC cohort were compared with 480 patients with 548 lesions from the 2020 SSC cohort, matched for age, sex, and ethnicity, including histology where available. Median time (SD) from referral to receipt of teledermatology advice was four (2.8) days and 50 (43.0) days for the SSC and VLC cohorts, respectively (p < 0.001). Diagnostic concordance between teledermatologist and histopathologist for benign versus malignant lesions was 70% for 114 lesions in the SSC cohort, comparable to the VLC cohort (71% of 122 lesions). Referrals from primary care, where skin lesions were imaged with variable devices and quality resulted in faster specialist advice with similar diagnostic performance compared to high-quality imaging at nurse-led specialist dermoscopy clinics.

Perception of Purposeful and Recreational Smartphone Use in Physiotherapy: Randomized Controlled Trial

Background

Many people constantly use their smartphones in all kinds of situations. Often smartphones are used in a meaningful and targeted way, but frequently they are used as a pastime without any purpose. This also applies to patients and therapists in treatment situations.

Objective

The aim of this study was to investigate how purposeful smartphone use compared with recreational smartphone use (by a physiotherapist or by a patient) influenced the perception of a physiotherapeutic treatment situation. We examined the impact of smartphone use during a physiotherapy session on the perception of the physiotherapist, evaluation of attentiveness, and evaluation of smartphone use in physiotherapy in general.

Methods

Members of various music and sports clubs were invited to participate in an online randomized controlled trial. Participants were randomly assigned to one of four conditions. They watched a video in which a physiotherapeutic treatment was shown and in which a smartphone was used or not used in the following four different ways: (1) therapeutically purposeful use, (2) recreational use by the physiotherapist (looking at the phone from time to time with no therapeutic purpose), (3) recreational use by the patient, and (4) no smartphone use (control condition). After watching the video, the participants indicated their perception of the physiotherapist’s professional competence, social competence, and empathetic behavior. They also rated the physiotherapist’s and patient’s attentiveness and evaluated the usage of smartphones generally in physiotherapy.

Results

The analysis included 118 participants (63 women and 55 men). When the physiotherapist used the smartphone in a purposeful way, the physiotherapist was perceived as more professionally competent (P=.007), socially competent (P=.03), and empathetic (P=.04) than if the physiotherapist used it with no therapeutic purpose. These effects occurred because recreational smartphone use by the physiotherapist was evaluated more negatively than the behavior in the control condition (professional competence: P=.001; social competence: P=.03; empathy: P=.04). Moreover, when the physiotherapist used the smartphone in a recreational way, the physiotherapist was perceived as being less attentive (P<.001). Likewise, when the patient used the smartphone in a recreational way, the patient was perceived as being less attentive (P<.001). Finally, smartphone use in physiotherapy was rated as more positive in general when the smartphone was used in a purposeful way compared with the conditions in which the physiotherapist or patient looked at the smartphone with no therapeutic purpose (P<.001). This positive evaluation occurred because purposeful use led to a more positive rating than no smartphone use (P<.001, R=0.42).

Conclusions

Smartphones are only appropriate for therapists and patients if they are used directly for a therapeutic purpose. Otherwise, it is better not to use smartphones during treatment.

Trial Registration

AsPredicted (aspredicted.org) #24740; https://aspredicted.org/blind.php?x=vv532i

The clinical utility of teledermoscopy in the era of telemedicine

Teledermoscopy is a novel diagnostic tool for the prevention, diagnosis, and treatment of skin disease when direct visualization of lesions is difficult. It is an economically viable option that can complement telehealth visits and that providers can utilize to identify melanocytic lesions and optimize care with diagnostic accuracy comparable to face-to-face (FTF) diagnosis. Teledermoscopy is invaluable in monitoring chronic conditions that require frequent follow-up and treatment optimization. Inclusion of clinical and dermoscopic images has been shown to improve the diagnostic accuracy of teledermatology services, thereby reducing healthcare costs. Teledermoscopy is also non-discriminatory, as diagnostic accuracy is similar in lighter and darker skin types. It has been shown to improve patient access to specialty services and reduce the number of "no-shows" at FTF clinics and length of surgery waiting times. Mobile teledermoscopy is user-friendly, feasible, and economically viable, as inexpensive mobile dermatoscopes have emerged on the market to reduce consumer out-of-pocket costs. Research is limited on teledermoscopy's utility in diagnosing pre-cancerous and cancerous skin lesions in adults, particularly complex pigmented lesions. Further research is recommended to investigate the role of dermoscopic expertise and artificial intelligence on the evaluation of teledermoscopic images.

Mobile Health Strategies to Tackle Skin Neglected Tropical Diseases With Recommendations From Innovative Experiences: Systematic Review

Background

Neglected tropical diseases (NTDs) represent a diverse group of 20 communicable diseases that occur in tropical and subtropical areas in 149 countries, affecting over 1 billion people and costing developing economies billions of dollars every year. Within these diseases, those that present lesions on the skin surface are classified as skin NTDs (sNTDs). Mobile health interventions are currently being used worldwide to manage skin diseases and can be a good strategy in the epidemiological and clinical management of sNTDs.

Objective

We aimed to analyze existing evidence about mobile health interventions to control and manage sNTDs in low- and middle-income countries (LMICs) and make recommendations for what should be considered in future interventions.

Methods

A systematic review was conducted of the MEDLINE, Embase, and Scopus databases over 10 years up to April 30, 2020. All types of clinical studies were considered. Data were synthesized into evidence tables. Apps were selected through a comprehensive systematic search in the Google Play Store and Apple App Store conducted between March 20 and April 15, 2020.

Results

From 133 potentially relevant publications, 13 studies met our criteria (9.8%). These analyzed eight different interventions (three SMS text messaging interventions and five app interventions). Six of the 13 (46%) studies were community-based cross-sectional studies intended to epidemiologically map a specific disease, mainly lymphatic filariasis, but also cutaneous leishmaniasis, leprosy, and NTDs, as well as sNTDs in general. Most of the studies were considered to have a high (5/13, 39%) or moderate (4/13, 31%) risk of bias. Fifteen apps were identified in the Google Play Store, of which three were also in the Apple App Store. Most of the apps (11/15, 73%) were targeted at health care professionals, with only four targeted at patients. The apps focused on scabies (3/15, 20%), lymphatic filariasis (3/15, 20%), cutaneous leishmaniasis (1/15, 7%), leprosy (1/15, 7%), yaws and Buruli ulcer (1/15, 7%), tropical diseases including more than one sNTDs (3/15, 20%), and NTDs including sNTDs (2/15, 13%). Only 1 (7%) app focused on the clinical management of sNTDs.

Conclusions

All mobile health interventions that were identified face technological, legal, final user, and organizational issues. There was a remarkable heterogeneity among studies, and the majority had methodological limitations that leave considerable room for improvement. Based on existing evidence, eight recommendations have been made for future interventions.

Availability of Spanish-Language Medical Apps in Google Play and the App Store: Retrospective Descriptive Analysis Using Google Tools

BACKGROUND

The number of medical and health apps in the App Store and Google Play repositories has been increasing in the recent years, and most of these apps are in English. However, little is known about the domain of Spanish health apps and their evolution.

OBJECTIVE

The aim of this study was to perform a retrospective descriptive analysis of medical apps for patients in the Spanish language by using Google search tools over a 5-year period and to compare the results by using a reproducible methodology to obtain a better knowledge of the medical apps available in the Spanish Language.

METHODS

Over a 5-year period, medical apps were catalogued using a Google-based methodology. Keywords of the first 14 categories of the International Classification of Diseases, Tenth Revision, were selected, and in December of each year, searches of the URLs of Google Play and the App Store were conducted using Google Advanced Search. The first 10 results were taken, and apps meeting the inclusion criteria were selected and rated with the iSYScore method.

RESULTS

Out of a sample of 1358 apps, 136 met the inclusion criteria. The 3 main categories of the medical apps were in the fields of endocrinology (diabetes), respiratory (chronic obstructive pulmonary disease, asthma, and allergies), and neurology (multiple sclerosis, Parkinson disease, and Alzheimer disease). Few apps were maintained over the 5 years. Only 10 of the 136 apps were maintained for 3 years or more. There was a large number of original apps in other languages that were translated into Spanish (56/136, 41.2%). In the last year of the study, the main reason (73/280, 26.1%) for discarding an app was the date of the last update.

CONCLUSIONS

The market of Spanish apps is poor; only few apps have appeared repeatedly over 5 years. Differences were found with the international market in terms of apps related to mental health, heart and circulatory system, and cancer, and coincidences were found in the relevance of apps for diabetes control.

Use of Mobile Applications in Dermatology

Smartphones and applications related to the same are ubiquitous now. As dermatologists, we have a wide array of smartphone apps at our disposal which we can use to improve our practice in all aspects—clinical, academic, research, and administrative. This article provides an overview of available apps, tips on using apps—both general and specific for dermatology, as well as discusses the scientific validity of some of these apps and the future of smartphone apps in the context of dermatology.

Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia

We undertook this study in light of an uncontrolled rise of melanoma incidence and mortality rates in the United Kingdom (UK). We aim to assess the effectiveness of prevention and early melanoma diagnosis in the UK's National Health Service (NHS) in comparison to the Australian system that has limited the melanoma rise. We compare the prevention campaigns against skin cancer and the stage at which melanoma is diagnosed. We analyse key drivers of early diagnosis. Overall, Australia has performed better than the UK and provides an example for the UK's NHS for better preventing melanoma and diagnosing it. Technologies under development, such as tele-dermatology and artificial intelligence applications, could aid in making melanoma early diagnosis easier, more cost-efficient, and lessen the burden on health systems. Diagnoses also provide the data to help public health officials target prevention programs.

Feasibility Study of a Smartphone Application for Detecting Skin Cancers in People With Albinism

PURPOSE

Albinism affects some facets of the eye’s function and coloration, as well as hair and skin color. The prevalence of albinism is estimated to be one in 2,000-5,000 people in sub-Saharan Africa and one in 270 in Tanzania. People in Tanzania with albinism experience sociocultural and economic disparities. Because of stigma related to albinism, they present to hospitals with advanced disease, including skin cancers. Mobile health (mHealth) can help to bridge some of the gaps in detection and treatment of skin cancers affecting this population.

METHODS

We assessed the feasibility of using a mobile application (app) for detection of skin cancers among people with albinism. The study was approved by the Ocean Road Cancer Institute institutional review board. Data, including pictures of the lesions, were collected using a mobile smartphone and submitted to expert reviewers. Expert reviewers’ diagnosis options were benign, malignant, or unevaluable.

RESULTS

A total of 77 lesions from different body locations of 69 participants were captured by the NgoziYangu mobile app. Sixty-two lesions (81%) were considered malignant via the app and referred for biopsy and histologic diagnosis. Of those referred, 55 lesions (89%) were biopsied, and 47 lesions (85%) were confirmed as skin malignancies, whereas eight (15%) were benign.

CONCLUSION

With an increasing Internet coverage in Africa, there is potential for smartphone apps to improve health care delivery channels. It is important that mobile apps like NgoziYangu be explored to reduce diagnostic delay and improve the accuracy of detection of skin cancer, especially in stigmatized groups.

[Experience of tele-expertise in cardiology in Pays de la Loire]

INTRODUCTION

General practitioners regularly need specialized advice for therapeutic adaptation, ECG interpretation or to facilitate referral to the local cardiologist. Tele-expertise could amplify these possibilities of coordination between professionals. An experiment in tele-expertise in cardiology was carried out by the URML in Pays de la Loire between 2016 and 2017. This experiment allowed GPs to seek the advice of a liberal cardiologist via a link card. The cardiologist received the card, accompanied by the ECG and responded within 48 hours.

OBJECTIVES

The objective was to study the acceptability of the experiment and to study the impact on the coordination of health care professionals and on delays.

METHOD

This evaluation required a mixed methodology with a before/after the experiment survey, semi-directive interviews with GP and cardiologists, ad hoc data collection and data from national health insurance claim database.

RESULTS

In 15 months of experimentation, 22% of GPs and 52% of liberal cardiologists of the territory participated. 959 reviews were requested by the GPs or 74 cards per month. The cardiologists were solicited 2.3 times a month. The cardiologist rated the ECG abnormal or doubtful for 31.9% of exams. An appointment was scheduled on average within 24 days. Few difficulties in using the service have been reported by users.

DISCUSSION

The results show that tele-expertise without any complex computer tools to work, helps to improve the organization of the gradation of the response of the specialized offer, to improve the confidence of GPs in their own ECG analysis. It constitutes a full-scale observatory of the implementation of tele-expertise in cardiology, upstream of its generalization announced for 2020 in France.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

A review of literature supporting the development of practice guidelines for teledermatology in Australia

Despite the potential of teledermatology to increase access to dermatology services and improve patient care, it is not widely practised in Australia. In an effort to increase uptake of teledermatology, Australian-specific practice guidelines for teledermatology are being developed by the Australasian College of Dermatologist. This paper reports finding from literature reviews that were undertaken to inform the development of these guidelines. Results cover the following sections: Modalities of teledermatology; Patient selection and consent; Imaging; Quality and safety; Privacy and security; Communication; and Documentation and retention. The document educates providers about the benefits and limitations of telehealth while articulating how to enhance patient care and reduce risk when practicing teledermatology.

Is Teledermoscopy Ready to Replace Face-to-Face Examinations for the Early Detection of Skin Cancer? Consumer Views, Technology Acceptance, and Satisfaction with Care

BACKGROUND

Previous cross-sectional research indicates high acceptance of mobile teledermoscopy-enhanced skin self-examination (SSE) by consumers based on the technology acceptance model (TAM) domains: perceived usefulness, ease of use, compatibility, attitude and intention, subjective norms, facilitator, and trust. However, no study has assessed this outcome longitudinally among people who actually used the technology in their own homes.

METHODS

Participants were living in Brisbane, Australia, aged 18 years or older, and at high risk of skin cancer. Participants randomly assigned to the intervention group (n = 98) completed a self-administered questionnaire on mobile teledermoscopy acceptance for skin cancer detection both before use and after performing mobile teledermoscopy-enhanced SSE in their homes. The survey included a 25-item scale assessing seven TAM domains. Item scores ranged from 5 (strongly agree) to 1 (strongly disagree). Participants also answered survey questions on satisfaction with use of teledermoscopy, and a 9-item "thoughts about melanoma" scale that measures cancer worry.

RESULTS

Participants were 19-73 years old, had high skin cancer risk, blue or grey eyes (53.1%), fair or very fair skin (88.8%), and previous skin cancer treatments (61.2%). Participants were more accepting of mobile teledermoscopy at baseline: mean TAM score of 4.15 (SE 0.05); their level of acceptance decreased significantly after teledermoscopy use: mean score 3.94 (SE 0.05; p = 0.001). In linear regression analysis, the decrease in TAM scores was similar across demographic and skin cancer risk categories. Ninety-two percent (n = 90) of participants agreed that mobile teledermoscopy was easy to use. The mean score of the "thoughts about melanoma" scale did not change significantly from baseline to follow-up.

CONCLUSION

Consumers had high TAM scores before they used mobile teledermoscopy within a randomised control trial. At the end of the intervention period, TAM scores decreased, although participants' average score still indicated "agreement" that mobile teledermoscopy was acceptable.

Accuracy of mobile digital teledermoscopy for skin self-examinations in adults at high risk of skin cancer: an open-label, randomised controlled trial

BACKGROUND

Skin self-examinations supplemented with mobile teledermoscopy might improve early detection of skin cancers compared with naked-eye skin self-examinations. We aimed to assess whether mobile teledermoscopy-enhanced skin self-examination can improve sensitivity and specificity of self-detection of skin cancers when compared with naked-eye skin self-examination.

METHODS

This randomised, controlled trial was done in Brisbane (QLD, Australia). Eligible participants (aged ≥18 years) had at least two skin cancer risk factors as self-reported in the eligibility survey and had to own or have access to an iPhone compatible with a dermatoscope attachment (iPhone versions 5-8). Participants were randomly assigned (1:1), via a computer-generated randomisation procedure, to the intervention group (mobile dermoscopy-enhanced self-skin examination) or the control group (naked-eye skin self-examination). Control group and intervention group participants received web-based instructions on how to complete a whole body skin self-examination. All participants completed skin examinations at baseline, 1 month, and 2 months; intervention group participants submitted photographs of suspicious lesions to a dermatologist for telediagnosis after each skin examination and control group participants noted lesions on a body chart that was sent to the research team after each skin examination. All participants had an in-person whole-body clinical skin examination within 3 months of their last skin self-examination. Primary outcomes were sensitivity and specificity of skin self-examination, patient selection of clinically atypical lesions suspicious for melanoma or keratinocyte skin cancers (body sites examined, number of lesions photographed, types of lesions, and lesions missed), and diagnostic concordance of telediagnosis versus in-person whole-body clinical skin examination diagnosis. All primary outcomes were analysed in the modified intention-to-treat population, which included all patients who had a clinical skin examination within 3 months of their last skin self-examination. This trial was registered with the Australian and New Zealand Clinical Trials Registry, ACTRN12616000989448.

FINDINGS

Between March 6, 2017, and June 7, 2018, 234 participants consented to enrol in the study, of whom 116 (50%) were assigned to the intervention group and 118 (50%) were assigned to the control group. 199 participants (98 participants in the intervention group and 101 participants in the control group) attended the clinical skin examination and thus were eligible for analyses. Participants in the intervention group submitted 615 lesions (median 6·0 per person; range 1-24) for telediagnosis and participants in the control group identified and recorded 673 lesions (median 6·0 per person; range 1-16). At the lesion level, sensitivity for lesions clinically suspicious for skin cancer was 75% (95% CI 63-84) in the intervention group and 88% (95% CI 80-91) in the control group (p=0·04). Specificity was 87% (95% CI 85-90) in the intervention group and 89% (95% CI 87-91) in the control group (p=0·42). At the individual level, the intervention group had a sensitivity of 87% (95% CI 76-99) compared with 97% (95% CI 91-100) in the control group (p=0·26), and a specificity of 95% (95% CI 90-100) compared with 96% (95% CI 91-100) in the control group. The overall diagnostic concordance between the telediagnosis and in-person clinical skin examination was 88%.

INTERPRETATION

The use of mobile teledermoscopy did not increase sensitivity for the detection of skin cancers compared with naked-eye skin self-examination; thus, further evidence is necessary for inclusion of skin self-examination technology for public health benefit.

FUNDING

National Health and Medical Research Council (Australia).

Skin Doctor Consultations Using Mobile Teledermoscopy: Exploring Virtual Care Business Models

Background: Technological advances have given rise to virtual health care services, resulting in a shift in how traditional health care services are being delivered. Consumers are increasingly demanding efficient access to health care information and services irrespective of time and distance, which is further driving the digitization of health care. This digital economy has created new opportunities for innovative new business models to meet the needs of these new markets. This study explores several in-use business models of virtual health care service platforms that incorporate mobile teledermoscopy (MTD) technologies. By comparing the different building blocks of these commercial ventures, we provide insights on business model choices and discuss the elements that contribute to economically sustainable and viable service offerings incorporating MTD applications. Materials and Methods: We searched the literature on teledermatology, complemented by searches using Google and other mobile app store platforms, and identified seven commercial ventures using teledermoscopy. We analyzed the building blocks of each business model by using an adapted version of Ash Maurya's Lean Canvas and Alexander Osterwalder's Business Model Canvas. Results: We identified three business elements that support the viability, sustainability, and growth of online dermatology services: developing key partnerships, clinician involvement in the design and implementation process, and managing the medico-legal risks and liabilities that are relevant for each country. Conclusions: Leveraging mobile technologies to deliver virtual health care present new business opportunities for health care providers. A better understanding of the business features associated with existing commercial ventures may increase uptake and improve financial viability of MTD applications as a complementary tool to traditional patient care models.

Will applications on smartphones allow a generalization of telemedicine?

Background

Telemedicine is one of the healthcare sectors that has developed the most in recent years. Currently, telemedicine is mostly used for patients who have difficulty attending medical consultations because of where they live (teleconsultation) or for specialist referrals when no specialist of a given discipline is locally available (telexpertise). However, the use of specific equipment (with dedicated cameras, screens, and computers) and the need for institutional infrastructure made the deployment and use of these systems expensive and rigid. Although many telemedicine systems have been tested, most have not generally gone beyond local projects.

Our hypothesis is that the use of smartphones will allow health care providers to overcome some of the limitations that we have exposed, thus allowing the generalization of telemedicine.

Main body

This paper addresses the problem of telemedicine applications, the market of which is growing fast. Their development may completely transform the organization of healthcare systems, change the way patients are managed and revolutionize prevention. This new organization should facilitate the lives of both patients and doctors.

In this paper, we examine why telemedicine has failed for years to take its rightful place in many European healthcare systems although there was a real need. By developing the example of France, this article analyses the reasons most commonly put forth: the administrative and legal difficulties, and the lack of funding. We argue that the real reason telemedicine struggled to find its place was because the technology was not close enough to the patient.

Conclusion

Finally, we explain how the development of smartphones and their current ubiquitousness should allow the generalization of telemedicine in France and on a global scale.

Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies

OBJECTIVE

To examine the validity and findings of studies that examine the accuracy of algorithm based smartphone applications ("apps") to assess risk of skin cancer in suspicious skin lesions.

DESIGN

Systematic review of diagnostic accuracy studies.

DATA SOURCES

Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, CPCI, Zetoc, Science Citation Index, and online trial registers (from database inception to 10 April 2019).

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies of any design that evaluated algorithm based smartphone apps to assess images of skin lesions suspicious for skin cancer. Reference standards included histological diagnosis or follow-up, and expert recommendation for further investigation or intervention. Two authors independently extracted data and assessed validity using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2 tool). Estimates of sensitivity and specificity were reported for each app.

RESULTS

Nine studies that evaluated six different identifiable smartphone apps were included. Six verified results by using histology or follow-up (n=725 lesions), and three verified results by using expert recommendations (n=407 lesions). Studies were small and of poor methodological quality, with selective recruitment, high rates of unevaluable images, and differential verification. Lesion selection and image acquisition were performed by clinicians rather than smartphone users. Two CE (Conformit Europenne) marked apps are available for download. SkinScan was evaluated in a single study (n=15, five melanomas) with 0% sensitivity and 100% specificity for the detection of melanoma. SkinVision was evaluated in two studies (n=252, 61 malignant or premalignant lesions) and achieved a sensitivity of 80% (95% confidence interval 63% to 92%) and a specificity of 78% (67% to 87%) for the detection of malignant or premalignant lesions. Accuracy of the SkinVision app verified against expert recommendations was poor (three studies).

CONCLUSIONS

Current algorithm based smartphone apps cannot be relied on to detect all cases of melanoma or other skin cancers. Test performance is likely to be poorer than reported here when used in clinically relevant populations and by the intended users of the apps. The current regulatory process for awarding the CE marking for algorithm based apps does not provide adequate protection to the public.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42016033595.

Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review

Although artificial intelligence has been available for some time, it has garnered significant interest recently and has been popularized by major companies with its applications in image identification, speech recognition and problem solving. Artificial intelligence is now being increasingly studied for its potential uses in medicine. A sound understanding of the concepts of this emerging field is essential for the dermatologist as dermatology has abundant medical data and images that can be used to train artificial intelligence for patient care. There are already a number of artificial intelligence studies focusing on skin disorders such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article aims to present a basic introduction to the concepts of artificial intelligence as well as present an overview of the current research into artificial intelligence in dermatology, examining both its current applications and its future potential.

Automatic Focus Assessment on Dermoscopic Images Acquired with Smartphones

Over recent years, there has been an increase in popularity of the acquisition of dermoscopic skin lesion images using mobile devices, more specifically using the smartphone camera. The demand for self-care and telemedicine solutions requires suitable methods to guide and evaluate the acquired images’ quality in order to improve the monitoring of skin lesions. In this work, a system for automated focus assessment of dermoscopic images was developed using a feature-based machine learning approach. The system was designed to guide the user throughout the acquisition process by means of a preview image validation approach that included artifact detection and focus validation, followed by the image quality assessment of the acquired picture. This paper also introduces two different datasets, dermoscopic skin lesions and artifacts, which were collected using different mobile devices to develop and test the system. The best model for automatic preview assessment attained an overall accuracy of 77.9% while focus assessment of the acquired picture reached a global accuracy of 86.2%. These findings were validated by implementing the proposed methodology within an android application, demonstrating promising results as well as the viability of the proposed solution in a real life scenario.

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

BACKGROUND

Machine learning algorithms achieve expert-level accuracy in skin lesion classification based on clinical images. However, it is not yet shown whether these algorithms could have high accuracy when embedded in a smartphone app, where image quality is lower and there is high variability in image taking scenarios by users. In the past, these applications were criticized due to lack of accuracy.

OBJECTIVE

In this study, we evaluate the accuracy of the newest version of a smartphone application (SA) for risk assessment of skin lesions.

METHODS

This SA uses a machine learning algorithm to compute a risk rating. The algorithm is trained on 131 873 images taken by 31 449 users in multiple countries between January 2016 and August 2018 and rated for risk by dermatologists. To evaluate the sensitivity of the algorithm, we use 285 histopathologically validated skin cancer cases (including 138 malignant melanomas), from two previously published clinical studies (195 cases) and from the SA user database (90 cases). We calculate the specificity on a separate set from the SA user database containing 6000 clinically validated benign cases.

RESULTS

The algorithm scored a 95.1% (95% CI, 91.9-97.3%) sensitivity in detecting (pre)malignant conditions (93% for malignant melanoma and 97% for keratinocyte carcinomas and precursors). This level of sensitivity was achieved with a 78.3% (95% CI, 77.2-79.3%) specificity.

CONCLUSIONS

This SA provides a high sensitivity to detect skin cancer; however, there is still room for improvement in terms of specificity. Future studies are needed to assess the impact of this SA on the health systems and its users.

Poor agreement between the automated risk assessment of a smartphone application for skin cancer detection and the rating by dermatologists

Background

Several smartphone applications (app) with an automated risk assessment claim to be able to detect skin cancer at an early stage. Various studies that have evaluated these apps showed mainly poor performance. However, all studies were done in patients and lesions were mainly selected by a specialist.

Objectives

To investigate the performance of the automated risk assessment of an app by comparing its assessment to that of a dermatologist in lesions selected by the participants.

Methods

Participants of a National Skin Cancer Day were enrolled in a multicentre study. Skin lesions indicated by the participants were analysed by the automated risk assessment of the app prior to blinded rating by the dermatologist. The ratings of the automated risk assessment were compared to the assessment and diagnosis of the dermatologist. Due to the setting of the Skin Cancer Day, lesions were not verified by histopathology.

Results

We included 125 participants (199 lesions). The app was not able to analyse 90 cases (45%) of which nine BCC, four atypical naevi and one lentigo maligna. Thirty lesions (67%) with a high and 21 with a medium risk (70%) rating by the app were diagnosed as benign naevi or seborrhoeic keratoses. The interobserver agreement between the ratings of the automated risk assessment and the dermatologist was poor (weighted kappa = 0.02; 95% CI −0.08‐0.12; P = 0.74).

Conclusions

The rating of the automated risk assessment was poor. Further investigations about the diagnostic accuracy in real‐life situations are needed to provide consumers with reliable information about this healthcare application.