Powering the Internet of Things Through Light Communication

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Novel solutions are required to connect billions of devices to the network as envisioned by the IoT. In this article we propose to use LiFi, which is based on off-the-shelf LEDs, as an enabler for the IoT in indoor environments. We present LiFi4IoT, a system which, in addition to communication, provides three main services that the radio frequency (RF) IoT networks struggle to offer: precise device positioning; the possibility of delivering power, since energy can be harvested from light; and inherent security due to the propagation properties of visible light. We analyze the application space of IoT in indoor scenarios, and propose a LiFi4IoT access point (AP) that communicates simultaneously with IoT devices featuring different types of detectors, such as CMOS camera sensors, PDs, and solar cells. Based on the capabilities of these technologies, we define three types of energy self-sufficient IoT "motes" and analyze their feasibility. Finally, we identify the main research directions to enable the LiFi4IoT vision and provide preliminary results for several of these.Peer ReviewedPostprint (author's final draft

Machine Learning in Melanoma Diagnosis. Limitations About to be Overcome

[spa] Antecedentes: La clasificación automática de imágenes es una rama prometedora del aprendi-zaje automático (de sus siglas en inglés Machine Learning [ML]), y es una herramienta útil enel diagnóstico de cáncer de piel. Sin embargo, poco se ha estudiado acerca de las limitacionesde su uso en la práctica clínica diaria.Objetivo: Determinar las limitaciones que existen en cuanto a la selección de imágenes usadaspara el análisis por ML de las neoplasias cutáneas, en particular del melanoma.Métodos: Se dise ̃nó un estudio de cohorte retrospectivo, donde se incluyeron de forma conse-cutiva 2.849 imágenes dermatoscópicas de alta calidad de tumores cutáneos para su valoraciónpor un sistema de ML, recogidas entre los a ̃nos 2010 y 2014. Cada imagen dermatoscópica fueclasificada según las características de elegibilidad para el análisis por ML.Resultados: De las 2.849 imágenes elegidas a partir de nuestra base de datos, 968 (34%) cum-plieron los criterios de inclusión. De los 528 melanomas, 335 (63,4%) fueron excluidos. Laausencia de piel normal circundante (40,5% de todos los melanomas de nuestra base de datos)y la ausencia de pigmentación (14,2%) fueron las causas más frecuentes de exclusión para elanálisis por ML.Discusión: Solo el 36,6% de nuestros melanomas se consideraron aceptables para el análisispor sistemas de ML de última generación. Concluimos que los futuros sistemas de ML deberánser entrenados a partir de bases de datos más grandes que incluyan imágenes representativasde la práctica clínica habitual. Afortunadamente, muchas de estas limitaciones están siendosuperadas gracias a los avances realizados recientemente por la comunidad científica, como seha demostrado en trabajos recientes. [eng] Background: Automated image classification is a promising branch of machine learning (ML)useful for skin cancer diagnosis, but little has been determined about its limitations for generalusability in current clinical practice.Objective: To determine limitations in the selection of skin cancer images for ML analysis,particularly in melanoma.Methods: Retrospective cohort study design, including 2,849 consecutive high-quality dermos-copy images of skin tumors from 2010 to 2014, for evaluation by a ML system. Each dermoscopyimage was assorted according to its eligibility for ML analysis.Results: Of the 2,849 images chosen from our database, 968 (34%) met the inclusion criteriafor analysis by the ML system. Only 64.7% of nevi and 36.6% of melanoma met the inclusioncriteria. Of the 528 melanomas, 335 (63.4%) were excluded. An absence of normal surroundingskin (40.5% of all melanomas from our database) and absence of pigmentation (14.2%) were themost common reasons for exclusion from ML analysis.Discussion: Only 36.6% of our melanomas were admissible for analysis by state-of-the-art MLsystems. We conclude that future ML systems should be trained on larger datasets which includerelevant non-ideal images from lesions evaluated in real clinical practice. Fortunately, many ofthese limitations are being overcome by the scientific community as recent works show

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

Skin manifestations in COVID-19: prevalence and relationship with disease severity

Background: Data on the clinical patterns and histopathology of SARS-CoV-2 related skin lesions, as well as on their relationship with the severity of COVID-19 are limited. Methods and Materials: Retrospective analysis of a prospectively collected cohort of patients with SARS-CoV-2 infection in a teaching hospital in Barcelona, Spain, from 1 April to 1 May 2020. Clinical, microbiological and therapeutic characteristics, clinicopathological patterns of skin lesions, and direct immunofluorescence and immunohistochemical findings in skin biopsies were analyzed. Results: Fifty-eight out of the 2761 patients (2.1%) either consulting to the emergency room or admitted to the hospital for COVID-19 suspicion during the study period presented COVID-19 related skin lesions. Cutaneous lesions could be categorized into six patterns represented by the acronym "GROUCH": Generalized maculo-papular (20.7%), Grover's disease and other papulo-vesicular eruptions (13.8%), livedo Reticularis (6.9%), Other eruptions (22.4%), Urticarial (6.9%), and CHilblain-like (29.3%). Skin biopsies were performed in 72.4%, including direct immunofluorescence in 71.4% and immunohistochemistry in 28.6%. Patients with chilblain-like lesions exhibited a characteristic histology and were significantly younger and presented lower rates of systemic symptoms, radiological lung infiltrates and analytical abnormalities, and hospital and ICU admission compared to the rest of patients. Conclusion: Cutaneous lesions in patients with COVID-19 appear to be relatively rare and varied. Patients with chilblain-like lesions have a characteristic clinicopathological pattern and a less severe presentation of COVID-19

Global patterns of care in advanced stage mycosis fungoides/Sezary syndrome: a multicenter retrospective follow-up study from the Cutaneous Lymphoma International Consortium

ABSTRACT Background Advanced-stage mycosis fungoides (MF)/Sezary syndrome (SS) patients are weighted by an unfavorable prognosis and share an unmet clinical need of effective treatments. International guidelines are available detailing treatment options for the different stages but without recommending treatments in any particular order due to lack of comparative trials. The aims of this second CLIC study were to retrospectively analyze the pattern of care worldwide for advanced-stage MF/SS patients, the distribution of treatments according to geographical areas (USA versus non-USA), and whether the heterogeneity of approaches has potential impact on survival. Patients and methods This study included 853 patients from 21 specialist centers (14 European, 4 USA, 1 each Australian, Brazilian, and Japanese). Results Heterogeneity of treatment approaches was found, with up to 24 different modalities or combinations used as first-line and 36% of patients receiving four or more treatments. Stage IIB disease was most frequently treated by total-skin-electron-beam radiotherapy, bexarotene and gemcitabine; erythrodermic and SS patients by extracorporeal photochemotherapy, and stage IVA2 by polychemotherapy. Significant differences were found between USA and non-USA centers, with bexarotene, photopheresis and histone deacetylase inhibitors most frequently prescribed for first-line treatment in USA while phototherapy, interferon, chlorambucil and gemcitabine in non-USA centers. These differences did not significantly impact on survival. However, when considering death and therapy change as competing risk events and the impact of first treatment line on both events, both monochemotherapy (SHR = 2.07) and polychemotherapy (SHR = 1.69) showed elevated relative risks. Conclusion This large multicenter retrospective study shows that there exist a large treatment heterogeneity in advanced MF/SS and differences between USA and non-USA centers but these were not related to survival, while our data reveal that chemotherapy as first treatment is associated with a higher risk of death and/or change of therapy and thus other therapeutic options should be preferable as first treatment approach

Brentuximab vedotin in the treatment of cutaneous T-cell lymphomas: Data from the Spanish Primary Cutaneous Lymphoma Registry

[Background] Brentuximab vedotin (BV) has been approved for CD30-expressing cutaneous T-cell lymphoma (CTCL) after at least one previous systemic treatment. However, real clinical practice is still limited.[Objectives] To evaluate the response and tolerance of BV in a cohort of patients with CTCL.[Methods] We analysed CTCL patients treated with BV from the Spanish Primary Cutaneous Lymphoma Registry (RELCP).[Results] Sixty-seven patients were included. There were 26 females and the mean age at diagnosis was 59 years. Forty-eight were mycosis fungoides (MF), 7 Sézary syndrome (SS) and 12 CD30+ lymphoproliferative disorders (CD30 LPD). Mean follow-up was 18 months. Thirty patients (45%) showed at least 10% of CD30+ cells among the total lymphocytic infiltrate. The median number of BV infusions received was 7. The overall response rate (ORR) was 67% (63% in MF, 71% in SS and 84% in CD30 LPD). Ten of 14 patients with folliculotropic MF (FMF) achieved complete or partial response (ORR 71%). The median time to response was 2.8 months. During follow-up, 36 cases (54%) experienced cutaneous relapse or progression. The median progression free survival (PFS) was 10.3 months. The most frequent adverse event was peripheral neuropathy (PN) (57%), in most patients (85%), grades 1 or 2.[Conclusions] These results confirm the efficacy and safety of BV in patients with advanced-stage MF, and CD30 LPD. In addition, patients with FMF and SS also showed a favourable response. Our data suggest that BV retreatment is effective in a proportion of cases.The Spanish Primary Cutaneous Lymphoma Registry (RELCP) is promoted by the Fundación Piel Sana Academia Española de Dermatología y Venereología, which received an unrestricted grant support from Kyowa Kirin.Peer reviewe