Usefulness of Smartphones in Dermatology: A US-Based Review

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.

The Level of Agreement between Self-Assessments and Examiner Assessments of Melanocytic Nevus Counts: Findings from an Evaluation of 4548 Double Assessments

Cutaneous melanoma (CM) is a candidate for screening programs because its prognosis is excellent when diagnosed at an early disease stage. Targeted screening of those at high risk for developing CM, a cost-effective alternative to population-wide screening, requires valid procedures to identify the high-risk group. Self-assessment of the number of nevi has been suggested as a component of such procedures, but its validity has not yet been established. We analyzed the level of agreement between self-assessments and examiner assessments of the number of melanocytic nevi in the area between the wrist and the shoulder of both arms based on 4548 study subjects in whom mutually blinded double counting of nevi was performed. Nevus counting followed the IARC protocol. Study subjects received written instructions, photographs, a mirror, and a "nevometer" to support self-assessment of nevi larger than 2 mm. Nevus counts were categorized based on the quintiles of the distribution into five levels, defining a nevus score. Cohen's weighted kappa coefficient (κ) was estimated to measure the level of agreement. In the total sample, the agreement between self-assessments and examiner assessments was moderate (weighted κ = 0.596). Self-assessed nevus counts were higher than those determined by trained examiners (mean difference: 3.33 nevi). The level of agreement was independent of sociodemographic and cutaneous factors; however, participants' eye color had a significant impact on the level of agreement. Our findings show that even with comprehensive guidance, only a moderate level of agreement between self-assessed and examiner-assessed nevus counts can be achieved. Self-assessed nevus information does not appear to be reliable enough to be used in individual risk assessment to target screening activities.

Mobile health technologies in an interventional hybrid study on actinic keratosis: Results from an early phase randomized controlled trial investigating the safety and efficacy of a cytosolic phospholipase A2 inhibitor gel in photodamaged skin

Hybrid trials are a new trend in dermatological research that leverage mobile health technologies to decentralize a subset of clinical trial elements and thereby reduce the number of in-clinic visits. In a Phase I/IIa randomized controlled hybrid trial, the safety and efficacy of an anti-proliferative and anti-inflammatory drug inhibiting cytosolic phospholipase A2 (AVX001) was tested using 1%, 3% or vehicle gel in 60 patients with actinic keratosis (AK) and assessed in-clinic as well as remotely. Over the course of 12 weeks, patients were assessed in-clinic at baseline, end of treatment (EOT) and end of study (EOS), as well as 9 times remotely on a weekly to biweekly basis. Safety outcomes comprising local skin reactions (LSR; 0-5), adverse events (AE) and cosmesis, were graded in-clinic and remotely using patient-obtained smartphone photographs (PSPs) and questionnaires; efficacy was assessed in-clinic based on clinically visible clearance of AK target area of >50%. A total of 55 participants (91.7%) completed the treatment course. The average submission rate of PSPs was high (≥85%), of which 93% were of sufficient quality. No serious AE were reported and only two experienced temporary LSR >2 (scale 0-4) and cosmesis remained stable throughout the study. Based on the mild AE and LSR profile, daily application of AVX001 gel for 1 month appears safe, tolerable, and cosmetically acceptable for use in patients with AK. At EOT, AVX001 achieved a subtle treatment response with clearance of AK target area of >50% in 18% of patients. Remote and in-clinic assessments of LSRs were in high agreement, suggesting that the use of mobile health technologies in early-phase hybrid studies of AK does not compromise patient safety.

The State of Artificial Intelligence in Skin Cancer Publications

BACKGROUND

Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting.

OBJECTIVES

To analyze the characteristics and trends of AI skin cancer publications from dermatology journals.

METHODS

AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI.

RESULTS

A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%).

CONCLUSIONS

Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.

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

BACKGROUND

As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.

OBJECTIVE

This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection.

METHODS

An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance.

RESULTS

Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users.

CONCLUSIONS

The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.

Challenging Patterns of Atypical Dermatofibromas and Promising Diagnostic Tools for Differential Diagnosis of Malignant Lesions

Dermatofibroma (DF) or fibrous histiocytoma is one of the most frequent benign cutaneous soft-tissue lesions, characterized by a post-inflammatory tissue reaction associated with fibrosis of the dermis. Clinically DFs have a polymorphous clinical aspect from the solitary, firm, single nodules to multiple papules with a relatively smooth surface. However, multiple atypical clinicopathological variants of DFs have been reported and, therefore, clinical recognition may become challenging, leading to a more burdensome identification and sometimes to misdiagnosis. Dermoscopy is considered an important tool in DFs diagnosis, as it improves diagnostic accuracy for clinically amelanotic nodules. Although typical dermoscopic patterns are most frequently seen in clinical practice, there have also been some atypical variants described, mimicking some underlying recurrent and sometimes harmful skin afflictions. Usually, no treatment is required, although an appropriate work-up may be necessary in specific cases, such as in the presence of atypical variants or a history of recent changes. This narrative review's aim is to summarize current evidence regarding clinical presentation, positive and differential diagnosis of atypical dermatofibromas and also to raise awareness about the importance of specific characteristics of atypical variants to better differentiate them from malignant conditions.

Patients' Experiences and Communication with Teledermatology versus Face-to-Face Dermatology

(1) Background: Teledermatology (TD) has exponentially grown since the onset of COVID-19, as the Face-to-Face Dermatology (F-F/D) modality changed within Public Health Systems. Although studies have been conducted on health results, we did not find any that analyzed the experiences of individuals who received care through TD. Therefore, the main objective of the study was to analyze the experiences of dermatology patients and the communication with health personnel. (2) Methods: A multicenter, controlled, randomized, non-blinded clinical trial was designed. Data were collected during the six months of follow-up. Four-hundred and fifty patients participated in the present study, who were assigned to two different groups: TD and F-F/D. The sociodemographic and clinical characteristics of the participants were collected. The ‘Generic Short Patient Experiences Questionnaire’ (GS-PEQ) was used to assess patients’ experiences, and the Healthcare Communication Questionnaire (HCCQ) was used to measure the communication of patients with healthcare professionals. (3) Results: After six months of follow-up, 450 patients completed the study (TD = 225; F-F/D = 225) of which 53.3% were women, with an average age of 52.16 (SD = 19.97). The main reasons for the consultations were skin lesions (51.56%) located on the head and neck (46.8%), followed by the legs (20.7%). According to the GS-PEQ, TD users indicated having a greater confidence in the professional skills of the doctors (p < 0.01). However, the F-F/D group indicated having received more adequate information about their diagnosis/afflictions (p < 0.01), were more involved in the decisions related to their treatment (p < 0.01), and more satisfied with the help and treatment received (p < 0.01). Regarding the HCCQ, the TD group obtained better assessments with respect to if the patients had been treated in a rude and hasty manner, if the health professionals had addressed them with a smile, and if these could adequately manage the reason for the consultation (p < 0.01). (4) Conclusions: The results of the study generally showed positive experiences and communication. The TD group indicated having received less information about the diagnosis, were less involved in the decisions, and were less satisfied with the help and treatment received. However, they indicated having more confidence on the professional skills of the doctors, and that the work at the institution was better organized. In addition, they perceived better communication skills of the health professionals, although less respect for their privacy.

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.