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.Melanoma Research Alliance and La Marató de TV3.Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved

Association between footwear use and neglected tropical diseases: a systematic review and meta-analysis

BACKGROUND The control of neglected tropical diseases (NTDs) has primarily focused on preventive chemotherapy and case management. Less attention has been placed on the role of ensuring access to adequate water, sanitation, and hygiene and personal preventive measures in reducing exposure to infection. Our aim was to assess whether footwear use was associated with a lower risk of selected NTDs. METHODOLOGY We conducted a systematic review and meta-analysis to assess the association between footwear use and infection or disease for those NTDs for which the route of transmission or occurrence may be through the feet. We included Buruli ulcer, cutaneous larva migrans (CLM), leptospirosis, mycetoma, myiasis, podoconiosis, snakebite, tungiasis, and soil-transmitted helminth (STH) infections, particularly hookworm infection and strongyloidiasis. We searched Medline, Embase, Cochrane, Web of Science, CINAHL Plus, and Popline databases, contacted experts, and hand-searched reference lists for eligible studies. The search was conducted in English without language, publication status, or date restrictions up to January 2014. Studies were eligible for inclusion if they reported a measure of the association between footwear use and the risk of each NTD. Publication bias was assessed using funnel plots. Descriptive study characteristics and methodological quality of the included studies were summarized. For each study outcome, both outcome and exposure data were abstracted and crude and adjusted effect estimates presented. Individual and summary odds ratio (OR) estimates and corresponding 95% confidence intervals (CIs) were calculated as a measure of intervention effect, using random effects meta-analyses. PRINCIPAL FINDINGS Among the 427 studies screened, 53 met our inclusion criteria. Footwear use was significantly associated with a lower odds of infection of Buruli ulcer (OR=0.15; 95% CI: 0.08-0.29), CLM (OR=0.24; 95% CI: 0.06-0.96), tungiasis (OR=0.42; 95% CI: 0.26-0.70), hookworm infection (OR=0.48; 95% CI: 0.37-0.61), any STH infection (OR=0.57; 95% CI: 0.39-0.84), strongyloidiasis (OR=0.56; 95% CI: 0.38-0.83), and leptospirosis (OR=0.59; 95% CI: 0.37-0.94). No significant association between footwear use and podoconiosis (OR=0.63; 95% CI: 0.38-1.05) was found and no data were available for mycetoma, myiasis, and snakebite. The main limitations were evidence of heterogeneity and poor study quality inherent to the observational studies included. CONCLUSIONS/SIGNIFICANCE Our results show that footwear use was associated with a lower odds of several different NTDs. Access to footwear should be prioritized alongside existing NTD interventions to ensure a lasting reduction of multiple NTDs and to accelerate their control and elimination. PROTOCOL REGISTRATION PROSPERO International prospective register of systematic reviews CRD42012003338

Medium-term health of seniors following exposure to a natural disaster

The article aims to describe the medium-term impacts of a major earthquake event (Chile, February 27, 2010) on 26 seniors. The authors adopted a qualitative study approach. Data obtained using the Impact of Event Scale–Revised (IES-R) show the presence of manifestations of posttraumatic stress in the majority of respondents. In addition, data collected in interviews demonstrated a progressive deterioration of the health of respondents over a period of 4 years following the disaster. Seniors are particularly vulnerable to the effects of material loss, emotional stress, and postdisaster health complications. These impacts are exacerbated by low economic status. Furthermore, broader research is necessary involving elderly living in poverty who have survived natural disasters and others without such experiences, in order to better identify and differentiate between health complications associated with exposure to disaster events and those linked more strictly with natural aging processes

How Schools Affect Student Well-Being: A Cross-Cultural Approach in 35 OECD Countries

A common approach for measuring the effectiveness of an education system or a school is the estimation of the impact that school interventions have on students’ academic performance. However, the latest trends aim to extend the focus beyond students’ acquisition of knowledge and skills, and to consider aspects such as well-being in the academic context. For this reason, the 2015 edition of the international assessment system Programme for International Student Assessment (PISA) incorporated a new tool aimed at evaluating the socio-emotional variables related to the well-being of students. It is based on a definition focused on the five dimensions proposed in the PISA theoretical framework: cognitive, psychological, social, physical, and material. The main purpose of this study is to identify the well-being components that significantly affect student academic performance and to estimate the magnitude of school effects on the wellbeing of students in OECD countries, the school effect being understood as the ability of schools to increase subjective student well-being. To achieve this goal, we analyzed the responses of 248,620 students from 35 OECD countries to PISA 2015 questionnaires. Specifically, we considered non-cognitive variables in the questionnaires and student performance in science. The results indicated that the cognitive well-being dimension, composed of enjoyment of science, self-efficacy, and instrumental motivation, as well as test anxiety all had a consistent relationship with student performance across countries. In addition, the school effect, estimated through a two-level hierarchical linear model, in terms of student well-being was systematically low. While the school effect accounted for approximately 25% of the variance in the results for the cognitive dimension, only 5–9% of variance in well-being indicators was attributable to it. This suggests that the influence of school on student welfare is weak, and the effect is similar across countries. The present study contributes to the general discussion currently underway about the definition of well-being and the connection between well-being and achievement. The results highlighted two complementary concerns: there is a clear need to promote socioemotional education in schools, and it is important to develop a rigorous framework for well-being assessment. The implications of the results and proposals for future studies are discussed.Spain Ministry of Science, Innovation and Universities PSI2017-85724-P2E Estudios, Evaluaciones e Investigacion, S.

Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

<p>Abstract</p> <p>Background</p> <p>2-D Echo is often performed in patients without history of coronary artery disease (CAD). We sought to determine echo features predictive of CAD.</p> <p>Methods</p> <p>2-D Echo of 328 patients without known CAD performed within one year prior to stress myocardial SPECT and angiography were reviewed. Echo features examined were left ventricular and atrial enlargement, LV hypertrophy, wall motion abnormality (WMA), LV ejection fraction (EF) < 50%, mitral annular calcification (MAC) and aortic sclerosis/stenosis (AS). High risk myocardial perfusion abnormality (MPA) was defined as >15% LV perfusion defect or multivessel distribution. Severe coronary artery stenosis (CAS) was defined as left main, 3 VD or 2VD involving proximal LAD.</p> <p>Results</p> <p>The mean age was 62 ± 13 years, 59% men, 29% diabetic (DM) and 148 (45%) had > 2 risk factors. Pharmacologic stress was performed in 109 patients (33%). MPA was present in 200 pts (60%) of which, 137 were high risk. CAS was present in 166 pts (51%), 75 were severe. Of 87 patients with WMA, 83% had MPA and 78% had CAS. Multivariate analysis identified age >65, male, inability to exercise, DM, WMA, MAC and AS as independent predictors of MPA and CAS. Independent predictors of high risk MPA and severe CAS were age, DM, inability to exercise and WMA.</p> <p>2-D echo findings offered incremental value over clinical information in predicting CAD by angiography. (Chi square: 360 vs. 320 p = 0.02).</p> <p>Conclusion</p> <p>2-D Echo was valuable in predicting presence of physiological and anatomical CAD in addition to clinical information.</p

Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine

In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown

A high performance liquid chromatographic assay of Mefloquine in saliva after a single oral dose in healthy adult Africans

<p>Abstract</p> <p>Background</p> <p>Mefloquine-artesunate is a formulation of artemisinin based combination therapy (ACT) recommended by the World Health Organization and historically the first ACT used clinically. The use of ACT demands constant monitoring of therapeutic efficacies and drug levels, in order to ensure that optimum drug exposure is achieved and detect reduced susceptibility to these drugs. Quantification of anti-malarial drugs in biological fluids other than blood would provide a more readily applicable method of therapeutic drug monitoring in developing endemic countries. Efforts in this study were devoted to the development of a simple, field applicable, non-invasive method for assay of mefloquine in saliva.</p> <p>Methods</p> <p>A high performance liquid chromatographic method with UV detection at 220 nm for assaying mefloquine in saliva was developed and validated by comparing mefloquine concentrations in saliva and plasma samples from four healthy volunteers who received single oral dose of mefloquine. Verapamil was used as internal standard. Chromatographic separation was achieved using a Hypersil ODS column.</p> <p>Results</p> <p>Extraction recoveries of mefloquine in plasma or saliva were 76-86% or 83-93% respectively. Limit of quantification of mefloquine was 20 ng/ml. Agreement between salivary and plasma mefloquine concentrations was satisfactory (r = 0.88, <it>p </it>< 0.001). Saliva:plasma concentrations ratio was 0.42.</p> <p>Conclusion</p> <p>Disposition of mefloquine in saliva paralleled that in plasma, making salivary quantification of mefloquine potentially useful in therapeutic drug monitoring.</p

Discordant American College of Physicians and international rheumatology guidelines for gout management: consensus statement of the Gout, Hyperuricemia and Crystal-Associated Disease Network (G-CAN).

In November 2016, the American College of Physicians (ACP) published a clinical practice guideline on the management of acute and recurrent gout. This guideline differs substantially from the latest guidelines generated by the American College of Rheumatology (ACR), European League Against Rheumatism (EULAR) and 3e (Evidence, Expertise, Exchange) Initiative, despite reviewing largely the same body of evidence. The Gout, Hyperuricemia and Crystal-Associated Disease Network (G-CAN) convened an expert panel to review the methodology and conclusions of these four sets of guidelines and examine possible reasons for discordance between them. The G-CAN position, presented here, is that the fundamental pathophysiological knowledge underlying gout care, and evidence from clinical experience and clinical trials, supports a treat-to-target approach for gout aimed at lowering serum urate levels to below the saturation threshold at which monosodium urate crystals form. This practice, which is truly evidence-based and promotes the steady reduction in tissue urate crystal deposits, is promoted by the ACR, EULAR and 3e Initiative recommendations. By contrast, the ACP does not provide a clear recommendation for urate-lowering therapy (ULT) for patients with frequent, recurrent flares or those with tophi, nor does it recommend monitoring serum urate levels of patients prescribed ULT. Results from emerging clinical trials that have gout symptoms as the primary end point are expected to resolve this debate for all clinicians in the near term future