BCN20000: dermoscopic lesions in the wild

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019 [8], where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically.Peer ReviewedPreprin

Ultrasonic Investigation of Hepatic Mechanical Properties: Quantifying Tissue Stiffness and Deformation with Increasing Portal Venous Pressure

<p>In this work, I investigate the mechanical response of the liver to increasing pressure in the portal vein using ultrasonic approaches. In advancing liver disease, portal venous pressure increases lead to severe clinical problems and death. Monitoring these pressure increases can predict patient outcomes and guide treatment. Current methods for measurement of portal venous pressure are invasive, expensive, and therefore are rarely repeated. Ultrasonic methods show promise because they are noninvasive, but traditional ultrasound images and doppler measurements do not yield accurate repeatable measures of hepatic pressure. However, increases in portal venous pressure have been associated with higher estimates of liver stiffness using ultrasound-based shear wave speed estimation algorithms. These quantitative estimates of shear wave speed may provide a mechanism for noninvasive hepatic pressure characterization, but they cannot currently be distinguished from the increases in shear wave speed estimates that are also observed in patients with normal portal venous pressures with advancing liver diseases. Thus, a better understanding of the mechanisms by which hepatic pressure modulates estimates of liver stiffness could provide information needed to distinguish increasing hepatic pressure from advancing brosis stage. This work is devoted to identifying and characterizing the underlying mechanism behind the observed increases in hepatic shear wave speed with pressurization.</p><p>Two experiments were designed in order to dene the mechanical properties of liver tissue that underlie the observed increase in shear wave speeds with increasing portal venous pressure. First, the behavior of the liver was shown to be nonlinear (or strain-dependent) by comparing stiness estimates in livers that were free to expand and constrained from expansion at increasing hepatic pressures. Shear wave speeds were observed to increase only in the unconstrained case in which the liver was observed to qualitatively deform. Second, the deformation of the liver was quantied using a clinical scanner and 3-D transducer to generate estimates of axial strain during pressurization. Axial strain was found to increase with elevation in portal venous pressure. This axial expansion of the liver also corresponded to increases in shear wave speed estimates with portal venous pressure.</p><p>The techniques developed herein were used to elucidate mechanical properties of the pressurized liver by concurrent ultrasound-based quantication of hepatic deformation and stiffness. This work shows that increasing shear wave speed estimates with hepatic pressurization are associated with increases in hepatic axial strain measurements. These results provide the basis for quantifying the relationship between pressurization and hepatic strain, laying the foundation for hyperelastic material modeling of the liver. Such nonlinear mechanical models can provide the basis for noninvasive characterization of hepatic pressure using stiffness metrics in the future.</p>Dissertatio

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

Background Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images. Objective The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts. Methods First, we obtained labels of 248 images of melanocytic lesions with 31 dermoscopic “subfeatures” labeled by 20 dermoscopy experts. These were then collapsed into 6 dermoscopic “superfeatures” based on structural similarity, due to low interrater reliability (IRR): dots, globules, lines, network structures, regression structures, and vessels. These images were then used as the gold standard for the crowd study. The commercial platform DiagnosUs was used to obtain annotations from a nonexpert crowd for the presence or absence of the 6 superfeatures in each of the 248 images. We replicated this methodology with a group of 7 dermatologists to allow direct comparison with the nonexpert crowd. The Cohen κ value was used to measure agreement across raters. Results In total, we obtained 139,731 ratings of the 6 dermoscopic superfeatures from the crowd. There was relatively lower agreement for the identification of dots and globules (the median κ values were 0.526 and 0.395, respectively), whereas network structures and vessels showed the highest agreement (the median κ values were 0.581 and 0.798, respectively). This pattern was also seen among the expert raters, who had median κ values of 0.483 and 0.517 for dots and globules, respectively, and 0.758 and 0.790 for network structures and vessels. The median κ values between nonexperts and thresholded average–expert readers were 0.709 for dots, 0.719 for globules, 0.714 for lines, 0.838 for network structures, 0.818 for regression structures, and 0.728 for vessels. Conclusions This study confirmed that IRR for different dermoscopic features varied among a group of experts; a similar pattern was observed in a nonexpert crowd. There was good or excellent agreement for each of the 6 superfeatures between the crowd and the experts, highlighting the similar reliability of the crowd for labeling dermoscopic images. This confirms the feasibility and dependability of using crowdsourcing as a scalable solution to annotate large sets of dermoscopic images, with several potential clinical and educational applications, including the development of novel, explainable ML tools

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice

A reinforcement learning model for AI-based decision support in skin cancer

: We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms

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.MethodsAn 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

Immunologic Profiling of Immune-Related Cutaneous Adverse Events with Checkpoint Inhibitors Reveals Polarized Actionable Pathways

Purpose: Immune-related cutaneous adverse events (ircAEs) occur in ≥50% of patients treated with checkpoint inhibitors (CPI), but mechanisms are poorly understood. Experimental Design: Phenotyping/biomarker analyses were conducted in 200 patients on CPIs (139 with ircAEs, 61 without, control) to characterize their clinical presentation and immunologic endotypes. Cytokines were evaluated in skin biopsies, skin tape strip (STS) extracts and plasma using real-time PCR and Meso Scale Discovery multiplex cytokine assays. Results: Eight ircAE phenotypes were identified: pruritus (26%), maculopapular rash (MPR; 21%), eczema (19%), lichenoid (11%), urticaria (8%), psoriasiform (6%), vitiligo (5%), and bullous dermatitis (4%). All phenotypes showed skin lymphocyte and eosinophil infiltrates. Skin biopsy PCR revealed the highest increase in IFN-gamma mRNA in patients with lichenoid (p&lt;0.0001) and psoriasiform dermatitis (p&lt;0.01) as compared to patients without ircAEs, while the highest IL-13 mRNA levels were detected in the eczema (p&lt;0.0001, compared to control). IL-17A mRNA was selectively increased in psoriasiform (p&lt;0.001), lichenoid (p&lt;0.0001), bullous dermatitis (p&lt;0.05) and MPR (p&lt;0.001), compared to control. Distinct cytokine profiles were confirmed in STS and plasma. Analysis determined increased skin/plasma IL-4 cytokine in pruritus, skin IL-13 in eczema, plasma IL-5 and IL-31 in eczema and urticaria, and mixed-cytokine pathways in MPR. Broad inhibition via corticosteroids or type 2-cytokine targeted inhibition resulted in clinical benefit in these ircAEs. In contrast, significant skin upregulation of type 1/type 17 pathways was found in psoriasiform, lichenoid, bullous dermatitis, and type 1 activation in vitiligo. Conclusions: Distinct immunologic ircAE endotypes suggest actionable targets for precision medicine-based interventions

BCN20000: dermoscopic lesions in the wild

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019 [8], where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically.Peer Reviewe