Fighting Melanoma with Smartphones: A Snapshot of Where We are a Decade after App Stores Opened Their Doors

An Evaluation of Interactive mHealth Applications for Adults Living with Cancer

This study evaluated the quality and usefulness of interactive mobile health (mHealth) applications (apps) for adults with cancer. The PRISMA guidelines were followed to add rigor to the search, as well as to the data collection and analysis. The apps available in the most used app stores (Google Play and Apple) with interactive tailored features were identified. To supplement this, a Google web search was also conducted. The apps were evaluated for their quality using the validated Mobile App Rating Scale (MARS) and for their usefulness using a checklist of end users' desired features derived from the literature. The searches returned 3046 apps and 17 were retained for evaluation. The average quality score of the apps across the sample was 3.62/5 (SD 0.26, range: 3.14-4.06), with Outcomes4me scoring the highest. On average, the apps scored 50% (SD 2.5, range: 31-88%) on the usefulness checklist, with Cancer.net scoring the highest. The lowest-scoring categories were communications features on the usefulness checklist and "information" on the MARS, indicating areas for future work. The findings identified the apps of an acceptable quality and usefulness that could be recommended to those with cancer.

Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients' Perception

Simple Summary

Early detection and resection of cutaneous melanoma are crucial for a good prognosis. However, visual distinction of early melanomas from benign nevi remains challenging. New artificial intelligence-based approaches for risk stratification of pigmented skin lesions provide screening methods for laypersons with increasing use of smartphone applications (apps). Our study aims to prospectively investigate the diagnostic accuracy of a CE-certified smartphone app, SkinVision^®, in melanoma recognition. Based on classification into three different risk scores, the app provides a recommendation to consult a dermatologist. In addition, both patients’ and dermatologists’ perspectives towards AI-based mobile health apps were evaluated. We observed that the app classified a significantly higher number of lesions as high-risk than dermatologists, which would have led to a clinically harmful number of unnecessary excisions. The diagnostic performance of the app in dichotomous classification of 1204 pigmented skin lesions (risk classification for nevus vs. melanoma) remained below advertised rates with low sensitivity (41.3–83.3%) and specificity (60.0–82.9%). The confidence in the app was low among both patients and dermatologists, and no patient favored an assessment by the app alone. Although smartphone apps are a potential medium for increasing awareness of melanoma screening in the lay population, they should be evaluated for certification with prospective real-world evidence.

Abstract

The exponential increase in algorithm-based mobile health (mHealth) applications (apps) for melanoma screening is a reaction to a growing market. However, the performance of available apps remains to be investigated. In this prospective study, we investigated the diagnostic accuracy of a class 1 CE-certified smartphone app in melanoma risk stratification and its patient and dermatologist satisfaction. Pigmented skin lesions ≥ 3 mm and any suspicious smaller lesions were assessed by the smartphone app SkinVision^® (SkinVision^® B.V., Amsterdam, the Netherlands, App-Version 6.8.1), 2D FotoFinder ATBM^® master (FotoFinder ATBM^® Systems GmbH, Bad Birnbach, Germany, Version 3.3.1.0), 3D Vectra^® WB360 (Canfield Scientific, Parsippany, NJ, USA, Version 4.7.1) total body photography (TBP) devices, and dermatologists. The high-risk score of the smartphone app was compared with the two gold standards: histological diagnosis, or if not available, the combination of dermatologists’, 2D and 3D risk assessments. A total of 1204 lesions among 114 patients (mean age 59 years; 51% females (55 patients at high-risk for developing a melanoma, 59 melanoma patients)) were included. The smartphone app’s sensitivity, specificity, and area under the receiver operating characteristics (AUROC) varied between 41.3–83.3%, 60.0–82.9%, and 0.62–0.72% according to two study-defined reference standards. Additionally, all patients and dermatologists completed a newly created questionnaire for preference and trust of screening type. The smartphone app was rated as trustworthy by 36% (20/55) of patients at high-risk for melanoma, 49% (29/59) of melanoma patients, and 8.8% (10/114) of dermatologists. Most of the patients rated the 2D TBP imaging (93% (51/55) resp. 88% (52/59)) and the 3D TBP imaging (91% (50/55) resp. 90% (53/59)) as trustworthy. A skin cancer screening by combination of dermatologist and smartphone app was favored by only 1.8% (1/55) resp. 3.4% (2/59) of the patients; no patient preferred an assessment by a smartphone app alone. The diagnostic accuracy in clinical practice was not as reliable as previously advertised and the satisfaction with smartphone apps for melanoma risk stratification was scarce. MHealth apps might be a potential medium to increase awareness for melanoma screening in the lay population, but healthcare professionals and users should be alerted to the potential harm of over-detection and poor performance. In conclusion, we suggest further robust evidence-based evaluation before including market-approved apps in self-examination for public health benefits.

Skin Lesion Segmentation Based on Vision Transformers and Convolutional Neural Networks—A Comparative Study

Melanoma skin cancer is considered as one of the most common diseases in the world. Detecting such diseases at early stage is important to saving lives. During medical examinations, it is not an easy task to visually inspect such lesions, as there are similarities between lesions. Technological advances in the form of deep learning methods have been used for diagnosing skin lesions. Over the last decade, deep learning, especially CNN (convolutional neural networks), has been found one of the promising methods to achieve state-of-art results in a variety of medical imaging applications. However, ConvNets’ capabilities are considered limited due to the lack of understanding of long-range spatial relations in images. The recently proposed Vision Transformer (ViT) for image classification employs a purely self-attention-based model that learns long-range spatial relations to focus on the image’s relevant parts. To achieve better performance, existing transformer-based network architectures require large-scale datasets. However, because medical imaging datasets are small, applying pure transformers to medical image analysis is difficult. ViT emphasizes the low-resolution features, claiming that the successive downsampling results in a lack of detailed localization information, rendering it unsuitable for skin lesion image classification. To improve the recovery of detailed localization information, several ViT-based image segmentation methods have recently been combined with ConvNets in the natural image domain. This study provides a comprehensive comparative study of U-Net and attention-based methods for skin lesion image segmentation, which will assist in the diagnosis of skin lesions. The results show that the hybrid TransUNet, with an accuracy of 92.11% and dice coefficient of 89.84%, outperforms other benchmarking methods.

Artificial intelligence: how it works and criteria for assessment

Artificial intelligence is a term used to describe computer technology in the modeling of intelligent behavior and critical thinking comparable to that of humans. To date, some of the first areas of medicine to be influenced by advances in artificial intelligence technologies will be those most dependent on imaging. These include ophthalmology, radiology, and dermatology. In connection with the emergence of numerous medical applications, scientists have formulated criteria for their assessment. This list included: clinical validation, regular application updates, functional focus, cost, availability of an information block for specialists and patients, compliance with the conditions of government regulation, and registration. One of the applications that meet all the requirements is the ProRodinki software package, developed for use by patients and specialists in the Russian Federation. Taking into account a widespread and rapidly developing competitive environment, it is necessary to soberly treat the resources of such applications, not exaggerating their capabilities and not considering them as a substitute for a specialist.

Skin Lesion Segmentation Using Deep Learning with Auxiliary Task

Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of 79.46, Accuracy (ACC) of 94.32, SEN of 88.76 with only one integrated model, which can be learned in an end-to-end manner.

Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time?

Smartphone applications (apps) are available to consumers for skin cancer prevention and early detection. This study aims to review changes over time in the skin cancer apps available to consumers as well as their functionality and costs. Apps for the prevention of skin cancer were searched on two major smartphone app stores (Android and iOS) in June 2019. The number, functionality, ratings, and price of the apps were described and compared to similar reviews of the skin cancer app market from 2014 to 2017. Overall, the June 2019 search identified 66 apps. Of 39 apps found in 2014, 30 were no longer available in 2019 representing an attrition rate of 77%; of 43 apps available in 2017, attrition was 46.5%. In 2019, 63.6% (n = 42/66) of apps were free to download compared to 53.5% (n = 23/43) in 2017. Input from clinician/professional bodies was evident for 47.0% (n = 31/66) of the apps in 2019 compared to 34.9% (15/43) in 2017. The most common app functionality offered in 2019 was monitoring/tracking of lesions at 48.5% (n = 32/66). Since 2014, there has been a steady increase in the number of apps available for the general public to support the prevention or early detection of skin cancers. There continues to be a high turnover of apps, and many apps still appear to lack clinician input and/or evidence for their safety and value.

The role of technology in melanoma screening and diagnosis

Melanoma presents challenges for timely and accurate diagnosis. Expert panels have issued risk-based screening guidelines, with recommended screening by visual inspection. To assess how recent technology can impact the risk/benefit considerations for melanoma screening, we comprehensively reviewed non-invasive visual-based technologies. Dermoscopy increases lesional diagnostic accuracy for both dermatologists and primary care providers; total body photography and sequential digital dermoscopic imaging also increase diagnostic accuracy, are supported by automated lesion detection and tracking, and may be best suited to use by dermatologists for longitudinal follow-up. Specialized imaging modalities using non-visible light technology have unproven benefit over dermoscopy and can be limited by cost, access, and training requirements. Mobile apps facilitate image capture and lesion tracking. Teledermatology has good concordance with face-to-face consultation and increases access, with increased accuracy using dermoscopy. Deep learning models can surpass dermatologist accuracy, but their clinical utility has yet to be demonstrated. Technology-aided diagnosis may change the calculus of screening; however, well-designed prospective trials are needed to assess the efficacy of these different technologies, alone and in combination to support refinement of guidelines for melanoma screening.

Consumer Preferences for Skin Cancer Screening Using Mobile Teledermoscopy: A Qualitative Study

BACKGROUND

Mobile teledermoscopy is a rapidly advancing technology that promotes early detection and management of skin cancers. Whilst the use of teledermoscopy has proven to be effective and has a role in the detection of skin cancers, patients' attitudes towards the multiple ways in which this technology can be utilised has not been explored.

METHODS

Data were obtained from a large randomised controlled trial comparing mobile teledermoscopy-enhanced skin self-examinations (SSEs) with naked-eye SSE. A semi-structured interview guide was developed by the investigators with questions focusing on people's previous skin screening behaviours and 2 of the major pathways which can be utilised in mobile teledermoscopy: (i) direct-to-consumer and (ii) doctor-to-doctor. All interviews were tape-recorded and transcribed verbatim. Thematic analysis was undertaken by 2 independent researchers.

RESULTS

Twenty-eight participants were interviewed. Eighty-six percent of participants (n = 24/28) had previously had a clinical skin examination. Only 18% of participants (n = 5/28) visited the same doctor for each clinical skin examination. Five main themes were identified in the interviews that affected how people felt about the integration of mobile teledermoscopy into skin screening pathways: history of clinical skin examinations, continuity of the doctor-patient relationship, convenience of the direct-to-consumer teledermoscopy, expedited review enhancing the doctor-to-doctor setting and mobile teledermoscopy as a partner-assisted task.

CONCLUSIONS

Overall mobile teledermoscopy was viewed positively for both direct-to-consumer and doctor-to-doctor interaction. Continuity of care in the doctor-patient relationship was not found to be a priority for clinical skin examination with most participants visiting several doctors throughout their clinical skin examination history.

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.

The impact of patient clinical information on automated skin cancer detection

Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient clinical information, an important clue towards clinical diagnosis. In this work, we present an approach to fill this gap. First, we introduce a new dataset composed of clinical images, collected using smartphones, and clinical data related to the patient. Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep learning models to combine features from images and clinical data. Last, we carry out experiments to compare the models' performance with and without using this mechanism. The results present an improvement of approximately 7% in balanced accuracy when the aggregation method is applied. Overall, the impact of clinical data on models' performance is significant and shows the importance of including these features on automated skin cancer detection.

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.

Mobile applications in oncology: A systematic review of health science databases

INTRODUCTION

In recent years there has been an exponential growth in the number of mobile applications (apps) relating to the early diagnosis of cancer and prevention of side effects during cancer treatment. For health care professionals and users, it can thus be difficult to determine the most appropriate app for given needs and assess the level of scientific evidence supporting their use. Therefore, this review aims to examine the research studies that deal with this issue and determine the characteristics of the apps involved.

METHODOLOGY

This study involved a systematic review of the scientific literature on randomized clinical trials that use apps to improve cancer management among patients, using the Pubmed (Medline), Latin America and the Caribbean in Health Sciences (LILACS), and Cochrane databases. The search was limited to articles written in English and Spanish published in the last 10 years. A search of the App Store for iOS devices and Google Play for Android devices was performed to find the apps identified in the included research articles.

RESULTS

In total, 54 articles were found to analyze the development of an application in the field of oncology. These articles were most frequently related to the use of apps for the early detection of cancer (n = 28), particularly melanoma (n = 9). In total, 21 studies reflected the application used. The apps featured in nine articles were located using the App Store and Google Play (n = 9), of which five were created to manage cancer-related issues. The rest of the apps were designed for use in the general population (n = 4).

CONCLUSIONS

There is an increasing number of research articles that study the use of apps in the field of oncology; however, these mobile applications tend to disappear from app stores after the studies are completed.

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.

The Utility of Smartphone Applications and Technology in Wound Healing

Appropriate management of both acute and chronic wounds is a dynamic practice that consumes large amounts of time and financial resources within the health care system. Traditionally, wounds were measured clinically and subjectively, leading to inaccuracies in assessing wound progression and healing over time. The recent development of mobile applications and digital equipment in medicine provides an opportunity for significant improvement in wound care through the incorporation of “smart” technologies in clinical practice. The utility of these technologies has been assessed regarding the treatment of diabetic foot ulcers, burns, and general wounds. The focus of this review is to provide an update on the current status of mobile applications and digital technology in the management of wounds.