A systematic review investigating the proportion of clinical images shared in prospective randomized controlled trials involving patients with atopic dermatitis and systemic pharmacotherapy.

For individuals with atopic dermatitis (AD), interpreting scientific papers that present clinical outcomes including the Eczema Area and Severity Index (EASI) and Investigators Global Assessment may be difficult. When compared to tabulated data and graphs, images from before and after treatment are often far more meaningful to these patients that ultimately will be candidates for the treatment. This systematic review focused on determining the frequency of clinical image sharing in AD research.Conducted in accordance with PRISMA guidelines, the review concentrated on randomized controlled trials that investigated predefined and available systemic treatments for AD. The search was performed in the MEDLINE database for studies published from the inception until 21 December 2023.The review included 60 studies, encompassing 17,799 randomized patients. Across these studies, 16 images representing 6 patients were shared in the manuscripts, leading to a sharing rate of 0.3‰.The almost missing inclusion of patient images in clinical trial publications hinders patient understanding. Adding images to scientific manuscripts could significantly improve patients' comprehension of potential treatment outcomes. This review highlights the need for authors, the pharmaceutical industry, study sponsors, and publishers to enhance and promote patient information through increased use of visual data.

[Is individualized education for a medical students desirable? A common policy on how to handle medical students requests for individual adaptations is needed]. / Bör individualiserad utbildning för läkarstudenter eftersträvas?

In Sweden, freedom of conscience for health care professionals is not legally permitted. However, requests from medical students to adjust and/or skip compulsory learning activities because of their religious and moral convictions appear to get more abundant. This creates problems when learning activities are directly related to the examination objectives stated by the Higher Education Ordinance of Sweden. Allowing students to abstain from certain parts of the medical program raises difficulties as to what kind of convictions that should be accepted and to what degree. Questions arise regarding equality of learning opportunities, assessment, and reasonable resource allocation. We call for national debate regarding these issues, which different universities now are forced to handle on their own, with the aim of establishing a common approach.

Evaluation of an artificial intelligence-based decision support for detection of cutaneous melanoma in primary care - a prospective, real-life, clinical trial.

BACKGROUND: Use of artificial intelligence, or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has in several retrospective studies shown high levels of diagnostic accuracy on par with, or even outperforming, experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary health care setting by primary care physicians; with or without access to teledermoscopic support from dermatology clinics. OBJECTIVE: To determine the diagnostic performance of an artificial intelligence-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. METHODS: This prospective, multicentre, clinical trial was conducted at 36 primary care centres in Sweden. The physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedure, by surgical excision or referral to dermatologist. After completed investigation, lesion diagnoses were collected from the patients' medical records and compared to app outcome and other lesion data. RESULTS: In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy (95% confidence interval) in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 (0.928-0.980), corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. CONCLUSIONS: The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively on primary care patients, which could add significant clinical value for primary care physicians in assessing skin lesions to detect melanoma. ClinicalTrials.gov Identifier: NCT05172232.

A systematic review investigating at what proportion clinical images are shared in prospective randomized controlled trials involving patients with psoriasis and biological agents.

For many patients including those with psoriasis, scientific manuscripts comprising clinical outcomes including psoriasis area severity index (PASI) and/or physician global assessment (PGA) may be difficult to understand. However, most patients can relate to images at baseline and follow-up, particularly for dermatological diseases. This study aimed to assess the proportion of shared clinical images in psoriasis trials. A systematic review adhering to the PRISMA guidelines was performed. The review was limited to randomized controlled trials, and among these, only investigations involving biological agents for treatment of psoriasis were included. The Embase, MEDLINE and Scopus databases were searched for eligible studies published from inception to October 26, 2021. In total, 152 studies were included. When combining these, 62,871 patients were randomized. Overall, 203 images were shared depicting 60 patients in the manuscripts yielding an overall sharing rate of 0.1%. Patient images are seldom incorporated in clinical trial manuscripts which impairs interpretation for patients. Inclusion of image material would strengthen the patients' perspective and understanding on what treatment effects that can be expected. As such, this systematic review should be an invitation to the pharmaceutical industry, other sponsors, and editorial offices to improve easy transfer of information to patients using image data.

Incidence and Prevalence of 73 Different Genodermatoses: A Nationwide Study in Sweden.

This retrospective registry-based cohort study aimed to estimate the incidence and prevalence of genodermatoses in the Swedish population and to analyse associated healthcare usage. Patients diagnosed with genodermatoses were identified from the patient registry of Sahlgrenska University Hospital (Gothenburg, Sweden) between 2016 and 2020. Clinical data from medical records were used to verify diagnoses recorded in the National Patient Registry (NPR). The NPR was then searched for International Classification of Diseases, Tenth Revision (ICD-10) codes Q80-82 and Q84 from 2001 to 2020. The local cohort included 298 patients with 36 unique genodermatosis diagnoses. Verification of these diagnoses in the NPR showed positive predictive values of over 90%. The NPR search yielded 13,318 patients with 73 unique diagnoses, including ichthyoses (n = 3,341; 25%), porokeratosis (n = 2,277; 17%), palmoplantar keratodermas (n = 1,754; 13%), the epidermolysis bullosa group (n = 1011; 7%); Darier disease (n = 770; 6%), Hailey-Hailey disease (n = 477; 4%) and Gorlin syndrome (n = 402; 3%). The incidence and prevalence of each diagnosis were calculated based on the nationwide cohort and are reported. A total of 149,538 outpatient visits were registered, a mean of 4.6 visits per patient. This study provides a valuable resource for the epidemiology of genodermatoses by reporting on the incidence and prevalence of 73 different genodermatoses.

Attitudes Towards Artificial Intelligence Among Dermatologists Working in Saudi Arabia.

INTRODUCTION: Artificial intelligence (AI) and its applications are among the most discussed modern technologies today. Despite the rapidly expanding use of AI in medicine, and specifically in dermatology, only a few studies have studied the attitude of physicians toward AI. OBJECTIVE: To recognize the attitudes towards AI among dermatologists in the Kingdom of Saudi Arabia. METHODS: A cross-sectional survey was done among dermatologists in Saudi Arabia. Questionnaires were distributed through several online channels. RESULTS: Overall, 103 dermatologists filled out the survey. The majority saw very strong or strong potential for AI in the automated detection of skin diseases based on dermatological clinical images (50.9%), dermoscopic images (66.6%) and within dermatopathology (66.6%). In regard to results of attitudes towards AI, 56.6% and 52. 8% agreed that AI will revolutionize medicine and dermatology, respectively. However, many of the respondents disagreed that AI will replace physicians (41.5%) and human dermatologists (39.6%) in the future. Age did not impact the overall attitude of dermatologists. CONCLUSION: Dermatologists in Saudi Arabia showed an optimistic attitude towards AI in dermatology and medicine. However, dermatologists believe that AI will not replace humans in the future.

Use of methotrexate and risk of skin cancer: a nationwide case-control study.

BACKGROUND: Methotrexate (MTX) use has been suspected of increasing the risk of skin cancer. The aim of this investigation was to examine the association between the use of MTX and the risk of basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC) and cutaneous malignant melanoma (CMM). METHODS: In a nationwide Danish case-control study, we identified incident, histologically verified cases of BCC (n = 131,447), cSCC (n = 18,661) or CMM (26,068) from 2004 to 2018. We matched 10 controls to each case on sex and birth year using risk-set sampling and computed crude and adjusted odds ratios (ORs) using conditional logistic regression for the use of MTX (≥2.5 g) compared with never-use. RESULTS: Use of MTX was associated with increased risk of BCC, cSCC and CMM with adjusted ORs of (95% confidence interval) 1.29 (1.20-1.38), 1.61 (1.37-1.89) and 1.35 (1.13-1.61), respectively. For BCC and cSCC, ORs increased with higher cumulative doses. When restricting the study population to patients with psoriasis, the ORs were 1.43 (1.23-1.67), 1.18 (0.80-1.74) and 1.15 (0.77-1.72), respectively. CONCLUSIONS: We observed an increased risk of BCC and cSCC associated with the use of MTX with evidence of a dose-response pattern; however, the association was not consistent when restricting the study population to patients with psoriasis.

Dermoscopic Findings in Intraepidermal Carcinoma: an Interobserver Agreement Study.

INTRODUCTION: A wide range of descriptive terms have been used for dermoscopic findings in intraepidermal carcinoma (IEC) and the clinical diagnostic accuracy of IEC can be challenging. Furthermore, dermoscopic findings in IEC have only rarely been evaluated in fair-skinned populations. OBJECTIVES: To measure the interobserver agreement between dermatologists for dermoscopic findings in IEC. Furthermore, to describe the frequency of these findings in a predominantly fair-skinned population. METHODS: One hundred dermoscopic images of histopathologically verified IECs were collected. The 11 most common dermoscopic findings described in previous studies were re-defined in a new terminology in a pre-study consensus meeting. Images were assessed by eight experienced international dermoscopists. The frequency of findings and the interobserver agreement was analyzed. RESULTS: Scales (83%), dotted/glomerular vessels (77%), pinkish-white areas (73%) and hemorrhage (46%) were the most commonly present dermoscopic findings. Pigmented structures were found in 32% and shiny white structures (follicular or stromal) in 54% of the IEC. Vascular structures (vessels and/or hemorrhage) could be seen in 89% of the lesions. Overall, the interobserver agreement for the respective dermoscopic findings was poor to moderate, with the highest kappa values noted for scales (0.55) and hemorrhage (0.54) and the lowest for pinkish-white areas (0.015). CONCLUSION: Our results confirm those of previous studies on dermoscopy in IEC, including the frequency of pigmented structures despite the fair-skinned population. The interobserver agreement was relatively low. The proposed new terminology and our findings can hopefully serve as a guideline for researchers, teachers and students on how to identify IEC.

Porokeratosis is one of the most common genodermatoses and is associated with an increased risk of keratinocyte cancer and melanoma.

BACKGROUND: Porokeratosis is a clinically heterogeneous group of keratinization disorders with a genetic background mainly affecting the mevalonate pathway, which is involved in the synthesis of cholesterol, an essential component for the formation of the extracellular lipid lamellae in the stratum corneum. Porokeratosis is reportedly associated with an increased risk of keratinocyte cancer, but to date, no large epidemiological studies have been conducted to further address this association. OBJECTIVES: The first objective was to characterize a cohort of patients diagnosed with porokeratosis at the Department of Dermatology and Venereology, Sahlgrenska University Hospital (SU), Gothenburg, Sweden. The second objective was to conduct a nationwide registry-based cohort study to investigate the association, if any, between porokeratosis and the cutaneous malignancies squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and melanoma. METHODS: For the SU cohort, the hospital registry was searched for patients with a diagnosis of porokeratosis recorded between 2016 and 2020. Clinical data were extracted from the records of the identified patients. For the nationwide cohort, national registries were searched to identify patients with a diagnosis of porokeratosis between 2001 and 2020. A tenfold control cohort was formed by Statistics Sweden. The data was cross-referenced with the Swedish Cancer Register to study the associations between porokeratosis and SCC, BCC and melanoma. RESULTS: Disseminated superficial actinic porokeratosis was the most common clinical type among the 108 patients in the SU cohort. In the nationwide search, 2277 patients with porokeratosis were identified (prevalence 1/4132). Porokeratosis was associated with an increased risk for SCC, BCC and melanoma with hazard ratios (95% CI) of 4.3 (3.4-5.4), 2.42 (1.97-2.98) and 1.83 (1.18-2.82), respectively, in the patient cohort, compared to the matched control group. CONCLUSION: Porokeratosis is a common genodermatosis, and it is associated with an enhanced risk of skin cancer.

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions.

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasive melanoma, 88 melanoma in situ, 115 dysplastic naevi, and 48 non-dysplastic naevi. The study included a training set of 358,800 pixels and a validation set of 7,313 pixels, which was then tested with a training set of 24,375 pixels. The majority vote classification achieved high overall sensitivity of 95% and a specificity of 92% (95% confidence interval (95% CI) 0.024-0.029) in differentiating malignant from benign lesions. In the pixel-wise classification, the overall sensitivity and specificity were both 82% (95% CI 0.005-0.005). When divided into 4 subgroups, the diagnostic accuracy was lower. Hyperspectral imaging provides high sensitivity and specificity in distinguishing between naevi and melanoma. This novel method still needs further validation.

Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network.

Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 independent dermatologists. The secondary aim was to address which clinical and dermoscopic features dermatologists found to be suggestive of invasive and in situ melanomas, respectively. A retrospective investigation was conducted including 1,578 cases of paired images of invasive (n = 728, 46.1%) and in situ melanomas (n = 850, 53.9%). All images were obtained from the Department of Dermatology and Venereology at Sahlgrenska University Hospital and were randomized to a training set (n = 1,078), a validation set (n = 200) and a test set (n = 300). The area under the receiver operating characteristics curve (AUC) among the dermatologists ranged from 0.75 (95% confidence interval 0.70-0.81) to 0.80 (95% confidence interval 0.75-0.85). The combined dermatologists' AUC was 0.80 (95% confidence interval 0.77-0.86), which was significantly higher than the CNN model (0.73, 95% confidence interval 0.67-0.78, p = 0.001). Three of the dermatologists significantly outperformed the CNN. Shiny white lines, atypical blue-white structures and polymorphous vessels displayed a moderate interobserver agreement, and these features also correlated with invasive melanoma. Prospective trials are needed to address the clinical usefulness of CNN models in this setting.

Ability to Predict Melanoma Within 5 Years Using Registry Data and a Convolutional Neural Network: A Proof of Concept Study.

Research relating to machine learning algorithms, including convolutional neural networks, has increased during the past 5 years. The aim of this pilot study was to investigate how accurately a convolutional neural network, trained on Swedish registry data, could perform in predicting cutaneous invasive and in situ melanoma (CMM) within 5 years. A cohort of 1,208,393 individuals was used. Registry data ranged from 4 July 2005 to 31 December 2011, predicting CMM between 1 January 2012 and 31 December 2016. A convolutional neural network with one-dimensional convolutions with respect to time was trained using healthcare databases and registers. The algorithm was trained on 23,886 individuals. Validation was performed on a holdout validation set including 6,000 individuals. After training and validation, the convolutional neural network was evaluated on a test set (1,000 individuals with an CMM occurring within 5 years and 5,000 without). The area under the receiver-operating characteristic curve was 0.59 (95% confidence interval (95% CI) 0.57-0.61). The point on the receiver-operating characteristic curve where sensitivity equalled specificity had a value of 56% (sensitivity 95% CI 53-60% and specificity 95% CI 55-58%). Albeit at an early stage, this pilot investigation demonstrates potential usefulness for machine learning algorithms in predicting melanoma risk.

Frequency of Publication of Dermoscopic Images in Inter-observer Studies: A Systematic Review.

Research interest in dermoscopy is increasing, but the complete dermoscopic image sets used in inter-observer studies of skin tumours are not often shared in research publications. The aim of this systematic review was to analyse what proportion of images depicting skin tumours are published in studies investigating inter-observer variations in the assessment of dermoscopic features and/or patterns. Embase, MEDLINE and Scopus databases were screened for eligible studies published from inception to 2 July 2020. For included studies the proportion of lesion images presented in the papers and/or supplements was extracted. A total of 61 studies (53 original studies and 8 shorter reports (i.e. research letters or concise reports)). published in the period 1997 to 2020 were included. These studies combined included 14,124 skin tumours, of which 373 (3%) images were published. This systematic review highlights that the vast majority of images included in dermoscopy research are not published. Data sharing should be a requirement for future studies, and must be enabled and standardized by the dermatology research community and editorial offices.

Discrimination Between Invasive and In Situ Melanomas Using Clinical Close-Up Images and a De Novo Convolutional Neural Network.

Background: Melanomas are often easy to recognize clinically but determining whether a melanoma is in situ (MIS) or invasive is often more challenging even with the aid of dermoscopy. Recently, convolutional neural networks (CNNs) have made significant and rapid advances within dermatology image analysis. The aims of this investigation were to create a de novo CNN for differentiating between MIS and invasive melanomas based on clinical close-up images and to compare its performance on a test set to seven dermatologists. Methods: A retrospective study including clinical images of MIS and invasive melanomas obtained from our department during a five-year time period (2016-2020) was conducted. Overall, 1,551 images [819 MIS (52.8%) and 732 invasive melanomas (47.2%)] were available. The images were randomized into three groups: training set (n = 1,051), validation set (n = 200), and test set (n = 300). A de novo CNN model with seven convolutional layers and a single dense layer was developed. Results: The area under the curve was 0.72 for the CNN (95% CI 0.66-0.78) and 0.81 for dermatologists (95% CI 0.76-0.86) (P < 0.001). The CNN correctly classified 208 out of 300 lesions (69.3%) whereas the corresponding number for dermatologists was 216 (72.0%). When comparing the CNN performance to each individual reader, three dermatologists significantly outperformed the CNN. Conclusions: For this classification problem, the CNN was outperformed by the dermatologist. However, since the algorithm was only trained and validated on 1,251 images, future refinement and development could make it useful for dermatologists in a real-world setting.