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1.
Rev. bioét. derecho ; (50): 315-331, nov. 2020.
Artigo em Espanhol | IBECS | ID: ibc-191360

RESUMO

La inteligencia artificial y el Big Data se articulan para poder lidiar con diferentes problemas relacionados con el análisis de datos masivos, en particular información de la COVID-19. En el presente artículo se muestran algunos proyectos de investigación relacionados con el aprendizaje profundo, el aprendizaje automático, el Big Data y la ciencia de datos, tendientes a dar soluciones plausibles bien en el monitoreo, detección, diagnóstico y tratamiento de las enfermedades asociadas con el virus. Con esto en mente, se muestra la correspondencia entre las tecnologías disruptivas y la información crítica, creando sinergias que permiten elaborar sistemas más avanzados de estudio y análisis facilitando la obtención de datos relevantes para la toma de decisiones sanitarias


Artificial intelligence and Big Data are articulated to be able to deal with different problems related to the analysis of big data, in particular, information from the COVID-19. In this sense, this article shows some research projects related to deep learning, machine learning, Big Data and data science, aimed to provide plausible solutions in monitoring, detection, diagnosis and treatment of diseases associated with the virus. The correspondence between disruptive technologies and critical information is shown, creating synergies that allow the development of more advanced systems of study and analysis, facilitating the obtaining of relevant data for health decision-making


La Intel·ligència Artificial I el Big Data s'articulen per poder fer front a diferents problemes relacionats amb l'anàlisi de dades massiu, concretament, informació relativa a la COVID-19. En aquest sentit, en el present article es mostren alguns projectes d'investigació relacionats amb l'aprenentatge profund, l'aprenentatge automàtic, el Big Data I la ciència de dades, capaços de donar solucions plausibles en el monitoratge, detecció, diagnòstic I tractament de les malalties associades amb el virus. Amb això en ment, es mostra la correspondència entre les tecnologies disruptives I la informació crítica, creant sinergies que permeten elaborar sistemes més avançats d'estudi I anàlisi facilitant l'obtenció de dades rellevants per a la presa de decisions sanitàries


Assuntos
Humanos , Inteligência Artificial , Big Data , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Pandemias , Tomada de Decisões , Betacoronavirus , Previsões
2.
Vestn Oftalmol ; 136(4. Vyp. 2): 300-309, 2020.
Artigo em Russo | MEDLINE | ID: mdl-32880155

RESUMO

The incidence of diabetes in the world is steadily increasing, and so is growing the number of cases of vision loss and blindness resulting from diabetic retinopathy (DR). This pathology is asymptomatic in the initial stages, but only the early treatment can be effective. In this regard, DR screening is an important and actual problem. This article reviews the principles, criteria, and problems of the currently run DR screening programs that are based on digital photography of the fundus. Special attention is paid to the displayed biomarkers and their role in DR screening. Various research methods are described, such as fluorescence angiography, optical coherence tomography, optical coherence tomography agniography, laser scanning ophthalmoscopy, which can be used to visualize pathological changes in the retina associated with DR. These changes were considered as potential screening biomarkers for DR. The review also describes new areas of screening based on telemedicine, artificial intelligence, and mobile photo-registering devices.


Assuntos
Diabetes Mellitus , Retinopatia Diabética , Edema Macular , Inteligência Artificial , Humanos , Retina , Tomografia de Coerência Óptica
3.
Zhongguo Yi Liao Qi Xie Za Zhi ; 44(4): 307-310, 2020 Apr 08.
Artigo em Chinês | MEDLINE | ID: mdl-32762202

RESUMO

Multi-parameters patient monitors are widely used in hospitals as medical device products, which have important clinical value. It expounds the core technologies of a miniature wearable multi-parameters patient monitor, and looks forward to its application prospects. In addition to traditional applications, when combined with a networked health service platforms, its applications will be greatly expanded in the context of big data and artificial intelligence technologies. The laboratory prototype of this product has been completed and has achieved the anticipative design goal.


Assuntos
Dispositivos Eletrônicos Vestíveis , Inteligência Artificial , Humanos , Monitorização Fisiológica
5.
Artigo em Espanhol | PAHO-IRIS | ID: phr-52559

RESUMO

[RESUMEN]. Objetivo. Proponer un modelo de atención en salud que integra tecnologías que pueden emplearse en el lugar de atención (point-of-care) y técnicas de inteligencia artificial. Métodos. Se usó un modelo teórico en el que un millón de personas accedieron a la aplicación móvil CoronApp-Colombia, que recoge datos personales, signos, síntomas y nexos epidemiológicos compatibles con COVID-19. Empleando la información de la app se aplicaron técnicas de inteligencia artificial (ciencias de datos) en una sala situacional virtual. Resultados. Los usuarios compatibles con COVID-19 serían priorizados y sometidos a una prueba de diagnóstico rápido para la búsqueda de anticuerpos anti-SARS-CoV-2. El tamizaje con la prueba de diagnóstico rápido permitiría detectar a las personas serorreactivas, en quienes se llevaría a cabo la confirmación diagnóstica mediante biología molecular (PCR). La información de los casos positivos confirmados por PCR se sometería nuevamente a técnicas de inteligencia artificial y estadística espacial para determinar los focos geográficos de infección. En estos focos se puede hacer búsqueda activa de contactos con los casos índices positivos y activar nuevamente la ruta de diagnóstico con la prueba de diagnóstico rápido y PCR. Conclusión. Este escenario puede ser un camino útil para que los países de la región con plataformas tecnológicas para el diagnóstico por PCR débiles o ausentes puedan maximizar los recursos existentes, estimar el peso epidemiológico de la COVID-19 (infección, morbilidad, mortalidad y letalidad) en sus territorios y definir planes de contención, mitigación y control acordes a sus necesidades.


[ABSTRACT]. Objective. To propose a health care model that integrates point-of-care technologies and artificial intelligence for the management of the COVID-19 pandemic. Methods. A theoretical model was used in which one million people accessed the mobile application CoronApp-Colombia, which collects personal data, signs, symptoms and epidemiological links compatible with COVID-19. With the information from the app artificial intelligence techniques (data science) were applied in a virtual situation room. Results. Users compatible with COVID-19 were prioritized and subjected to a rapid diagnostic test for anti-SARS-CoV-2 antibodies. Screening with the rapid diagnostic test would allow detection of sero-reactive individuals, for whom diagnostic confirmation would be carried out using molecular biology (PCR). Information from positive cases confirmed by PCR would be re-screened using artificial intelligence and spatial statistical techniques to identify geographical foci of infection. These foci could be actively searched for contacts with positive index cases and the diagnostic route would be followed again using the rapid diagnostic test and PCR. Conclusion. This model may be useful for countries in the region with weak or absent technological platforms for PCR diagnosis to maximize existing resources, estimate the epidemiological burden of COVID-19 (infection, morbidity, mortality and lethality) and implement containment, mitigation and control plans according to their needs.


Assuntos
Pandemias , Infecções por Coronavirus , Coronavirus , Saúde Pública , Inteligência Artificial , Monitoramento Epidemiológico , Testes Sorológicos , Colômbia , Pandemias , Infecções por Coronavirus , Saúde Pública , Inteligência Artificial , Monitoramento Epidemiológico , Testes Sorológicos , Infecções por Coronavirus
6.
Nat Commun ; 11(1): 4080, 2020 08 14.
Artigo em Inglês | MEDLINE | ID: mdl-32796848

RESUMO

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.


Assuntos
Inteligência Artificial , Técnicas de Laboratório Clínico/métodos , Infecções por Coronavirus/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Betacoronavirus/isolamento & purificação , Criança , Pré-Escolar , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/virologia , Aprendizado Profundo , Feminino , Humanos , Imageamento Tridimensional/métodos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/virologia , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Adulto Jovem
7.
J Med Internet Res ; 22(8): e20007, 2020 08 17.
Artigo em Inglês | MEDLINE | ID: mdl-32804086

RESUMO

BACKGROUND: Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. OBJECTIVE: We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. METHODS: The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. RESULTS: By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. CONCLUSIONS: We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis.


Assuntos
Inteligência Artificial , Infecções por Coronavirus/tratamento farmacológico , Oftalmopatias/etiologia , Hidroxicloroquina/efeitos adversos , Hidroxicloroquina/uso terapêutico , Metanálise como Assunto , Pneumonia Viral/tratamento farmacológico , Olho/efeitos dos fármacos , Olho/patologia , Humanos , Pandemias , Fatores de Tempo
8.
J Med Internet Res ; 22(8): e20259, 2020 08 25.
Artigo em Inglês | MEDLINE | ID: mdl-32735549

RESUMO

BACKGROUND: The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2. OBJECTIVE: We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN). METHODS: We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2. RESULTS: Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%. CONCLUSIONS: This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.


Assuntos
Betacoronavirus , Infecções por Coronavirus , Pandemias , Pneumonia Viral , Idoso , Idoso de 80 Anos ou mais , Inteligência Artificial , Comorbidade , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Feminino , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Prognóstico , Curva ROC , Reino Unido
9.
Nat Commun ; 11(1): 3852, 2020 07 31.
Artigo em Inglês | MEDLINE | ID: mdl-32737308

RESUMO

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as early warning scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on electronic health records (EHR) trained artificial intelligence (AI) systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. Here, we present an explainable AI early warning score (xAI-EWS) system for early detection of acute critical illness. xAI-EWS potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.


Assuntos
Lesão Renal Aguda/diagnóstico , Lesão Pulmonar Aguda/diagnóstico , Inteligência Artificial , Registros Eletrônicos de Saúde/estatística & dados numéricos , Sepse/diagnóstico , Doença Aguda , Lesão Renal Aguda/sangue , Lesão Renal Aguda/patologia , Lesão Pulmonar Aguda/sangue , Lesão Pulmonar Aguda/patologia , Área Sob a Curva , Pressão Sanguínea , Estado Terminal , Diagnóstico Precoce , Frequência Cardíaca , Humanos , Prognóstico , Curva ROC , Sepse/sangue , Sepse/patologia
10.
J Affect Disord ; 276: 797-803, 2020 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-32738664

RESUMO

BACKGROUND: The outbreak of the new coronavirus pneumonia (NCP) in Wuhan, Hubei, has caused very serious consequences and severely affected people's lives and mental health. The outbreak will cause bad emotions such as tension, anxiety, fear, and so on. College students who have returned home from school face infection, isolation, and delay in starting school, and thus, their emotional stress should be observed. METHODS: This study used self-designed questionnaires and artificial intelligence (AI) to assess and analyze the emotional state of over 30,000 college students during the outbreak period in January (T1) and home quarantine in February (T2). This survey used online questionnaire (www.wjx.cn) to investigate the emotion information of college students. RESULTS: In the T1 survey, the "Typhoon Eye Effect" appeared. College students in Hubei are calmer than those outside Hubei in T1. However, in T2, an emotional "infection point" appeared, there was an "Exposure Effect", the negative emotions of students in Hubei largely increased and became higher than students outside Hubei. CONCLUSION: This survey found that there is an emotional "infection point" in February among college students, especially in the Hubei area. College students in Hubei are calmer than those outside Hubei in T1. In contrast, college students in Hubei were more nervous and scared than those outside Hubei in T2. This epidemic has caused the students to experience significant pressure and negative emotions. Therefore, universities and society should pay attention to their emotional adjustment, there are some suggestions such as establish the mental health organizations, test students' emotion status regularly.


Assuntos
Betacoronavirus , Infecções por Coronavirus , Emoções , Pandemias , Pneumonia Viral , Saúde Pública , Adolescente , Ansiedade , Inteligência Artificial , China/epidemiologia , Surtos de Doenças , Emergências , Feminino , Humanos , Masculino , Saúde Mental , Estudantes/psicologia , Inquéritos e Questionários , Adulto Jovem
12.
Radiol Clin North Am ; 58(5): 995-1008, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32792129

RESUMO

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.


Assuntos
Inteligência Artificial , Carcinoma de Células Renais/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Neoplasias Renais/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Tomografia Computadorizada por Raios X/métodos , Diagnóstico Diferencial , Humanos , Rim/diagnóstico por imagem
13.
IEEE Pulse ; 11(4): 2-7, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32804639

RESUMO

Qualitative interpretation is a good thing when it comes to reading lung images in the fight against coronavirus 2019 disease (COVID-19), but quantitative analysis makes radiology reporting much more comprehensive. To that end, several research groups have begun looking to artificial intelligence (AI) as a tool for reading and analyzing X-rays and computed tomography (CT) scans, and helping to diagnose and monitor COVID-19.


Assuntos
Inteligência Artificial , Infecções por Coronavirus/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador , Tomografia Computadorizada por Raios X , Algoritmos , Betacoronavirus , Humanos , Pandemias
14.
J Med Syst ; 44(9): 156, 2020 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-32740678

RESUMO

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.


Assuntos
Algoritmos , Inteligência Artificial , Betacoronavirus , Infecções por Coronavirus , Pandemias , Pneumonia Viral , Humanos , Aprendizado de Máquina
15.
Urologe A ; 59(9): 1026-1034, 2020 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-32821957

RESUMO

In the past 10 years, the methods of artificial intelligence (AI) have experienced breakthroughs that have opened up a multitude of new fields of application for information technology. AI is particularly strong in those areas where patterns have to be recognized and conclusions and forecasts based on large, multiparametric data sets have to be drawn. Computers are superior to us in terms of precision and speed in these problems. These advances in information technology reach us at a time when innovations in diagnostics and sensor technology enable more precise patient stratification and confront medical personnel with an increasing quantity and quality of patient data. Urology is symbolic of this new complexity of medicine, in which multi-layered diagnostic cascades require a high degree of interdisciplinarity and, especially in uro-oncology, therapeutic strategies are becoming more differentiated and require the interpretation of multiple clinical and diagnostic data. Here, methods of Artificial Intelligence will in future support medical personnel in diagnostics and therapy decisions and thus come closer to the goal of precision medicine. A prerequisite for the success of AI-based support tools will be the transparent development and validation of the software, as well as the population-based visualization of decision parameters.


Assuntos
Inteligência Artificial , Urologistas , Urologia/tendências , Humanos , Oncologia/tendências , Medicina de Precisão , Software
16.
BMC Ecol ; 20(1): 48, 2020 08 29.
Artigo em Inglês | MEDLINE | ID: mdl-32861248

RESUMO

BACKGROUND: Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, -2, -4, -6, -8, -10 and -12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development. RESULTS: Comparing to the MLR, the MLP model represents the significant value of R2 in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds. CONCLUSIONS: Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands.


Assuntos
Germinação , Salvia , Inteligência Artificial , Ecossistema , Sementes , Temperatura
17.
Hautarzt ; 71(9): 669-676, 2020 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-32747996

RESUMO

BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relevance of AI in current dermatological practice. OBJECTIVES: This article summarizes promising study results of skin cancer diagnosis by computer-based diagnostic systems and discusses their significance for daily practice. We hereby focus on the analysis of dermoscopic images of pigmented and unpigmented skin lesions. MATERIALS AND METHODS: A selective literature search for recent relevant trials was conducted. The included studies used machine learning, and in particular "convolutional neural networks", which have been shown to be particularly effective for the classification of image data. RESULTS AND CONCLUSIONS: In numerous studies, computer algorithms were able to detect pigmented and nonpigmented neoplasms of the skin with high precision, comparable to that of dermatologists. The combination of the physician's assessment and AI showed the best results. Computer-based diagnostic systems are widely accepted among patients and physicians. However, they are still not applicable in daily practice, since computer-based diagnostic systems have only been tested in an experimental environment. In addition, many digital diagnostic criteria that help AI to classify skin lesions remain unclear. This lack of transparency still needs to be addressed. Moreover, clinical studies on the use of AI-based assistance systems are needed in order to prove its applicability in daily dermatologic practice.


Assuntos
Inteligência Artificial , Diagnóstico por Computador/métodos , Programas de Rastreamento/métodos , Melanoma/diagnóstico , Redes Neurais de Computação , Neoplasias Cutâneas/diagnóstico , Algoritmos , Dermoscopia , Humanos , Processamento de Imagem Assistida por Computador/métodos
18.
Hautarzt ; 71(9): 686-690, 2020 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-32761386

RESUMO

Telemedicine has been used in the daily routine of dermatologists for decades. The potential advantages are especially obvious in African countries having limited medical care, long geographical distances, and a meanwhile relatively well-developed telecommunication sector. National and international working groups support the establishment of teledermatological projects and in recent years have increasingly been using artificial intelligence (AI)-based technologies to support the local physicians. Ethnic variations represent a challenge in the development of automated algorithms. To further improve the accuracy of the systems and to be able to globalize, it is important to increase the amount of available clinical data. This can only be achieved with the active participation of local health care providers as well as the dermatological community and must always be in the interest of the individual patient.


Assuntos
Inteligência Artificial , Telemedicina , África , Dermatologia , Humanos
19.
Hautarzt ; 71(9): 660-668, 2020 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-32789670

RESUMO

BACKGROUND: Since 2017, there have been several reports of artificial intelligence (AI) achieving comparable performance to human experts on medical image analysis tasks. With the first ratification of a computer vision algorithm as a medical device in 2018, the way was paved for these methods to eventually become an integral part of modern clinical practice. OBJECTIVES: The purpose of this article is to review the main developments that have occurred over the last few years in AI for image analysis, in relation to clinical applications and dermatology. MATERIALS AND METHODS: Following the annual ImageNet challenge, we review classical methods of machine learning for image analysis and demonstrate how these methods incorporated human expertise but failed to meet industrial requirements regarding performance and scalability. With the rise of deep learning based on artificial neural networks, these limitations could be overcome. We discuss important aspects of this technology including transfer learning and report on recent developments such as explainable AI and generative models. RESULTS: Deep learning models achieved performance on a par with human experts in a broad variety of diagnostic tasks and were shown to be suitable for industrialization. Therefore, current developments focus less on further improving accuracy but rather address open issues such as interpretability and applicability under clinical conditions. Upcoming generative models allow for entirely new applications. CONCLUSIONS: Deep learning has a history of remarkable success and has become the new technical standard for image analysis. The dramatic improvement these models brought over classical approaches enables applications in a rapidly increasing number of clinical fields. In dermatology, as in many other domains, artificial intelligence still faces considerable challenges but is undoubtedly developing into an essential tool of modern medicine.


Assuntos
Inteligência Artificial , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/tendências , Algoritmos , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
20.
J Med Internet Res ; 22(9): e19338, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32790642

RESUMO

BACKGROUND: The first case of COVID-19 in Saudi Arabia was confirmed on March 3, 2020. Saudi Arabia, like many other countries worldwide, implemented lockdown of most public and private services in response to the pandemic and established population movement restrictions nationwide. With the implementation of these strict mitigation regulations, technology and digital solutions have enabled the provision of essential services. OBJECTIVE: The aim of this paper is to highlight how Saudi Arabia has used digital technology during the COVID-19 pandemic in the domains of public health, health care services, education, telecommunication, commerce, and risk communication. METHODS: We documented the use of digital technology in Saudi Arabia during the pandemic using publicly available official announcements, press briefings and releases, news clips, published data, peer-reviewed literature, and professional discussions. RESULTS: Saudi Arabia's government and private sectors combined developed and launched approximately 19 apps and platforms that serve public health functions and provide health care services. A detailed account of each is provided. Education processes continued using an established electronic learning infrastructure with a promising direction toward wider adoption in the future. Telecommunication companies exhibited smooth collaboration as well as innovative initiatives to support ongoing efforts. Risk communication activities using social media, websites, and SMS text messaging followed best practice guides. CONCLUSIONS: The Saudi Vision 2030 framework, released in 2017, has paved the path for digital transformation. COVID-19 enabled the promotion and testing of this transition. In Saudi Arabia, the use of artificial intelligence in integrating different data sources during future outbreaks could be further explored. Also, decreasing the number of mobile apps and merging their functions could increase and facilitate their use.


Assuntos
Inteligência Artificial , Infecções por Coronavirus/epidemiologia , Educação em Saúde/métodos , Aplicativos Móveis/provisão & distribução , Pandemias , Pneumonia Viral/epidemiologia , Saúde Pública/métodos , Mídias Sociais/estatística & dados numéricos , Humanos , Arábia Saudita/epidemiologia
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