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1.
Rev. otorrinolaringol. cir. cabeza cuello ; 82(2): 244-257, jun. 2022. ilus, tab
Article in Spanish | LILACS-Express | LILACS | ID: biblio-1389845

ABSTRACT

La inteligencia artificial posee una larga historia, llena de innovaciones que han dado como resultado diferentes recursos diagnósticos de alto rendimiento, que se encuentran disponibles actualmente. En este artículo se presenta una revisión sobre la inteligencia artificial y sus aplicaciones en medicina. El trabajo se centra en la especialidad de otorrinolaringología con el objetivo de informar a la comunidad médica la importancia y las aplicaciones más destacadas en los diferentes procesos diagnósticos dentro de la especialidad. Incluimos una sección para el análisis del estado actual de la inteligencia artificial en otorrinolaringología en Chile, así como los desafíos a enfrentar a futuro para utilizar la inteligencia artificial en la práctica médica diaria.


Artificial intelligence has a long history full of innovations that have resulted in different high-performance diagnostic resources currently available. This work has reviewed the artificial intelligence definition and its applications to medicine. We focused our review on otolaryngology's specialty to inform the medical community of the importance and the most relevant applications in the different diagnostic processes. We include an analysis of the current state of artificial intelligence in otolaryngology in Chile, and the challenges to be faced in the future to use artificial intelligence into daily medical practice.


Subject(s)
Humans , Otolaryngology , Otorhinolaryngologic Diseases/diagnosis , Otorhinolaryngologic Diseases/therapy , Artificial Intelligence , Chile , Machine Learning , Head and Neck Neoplasms/diagnosis
2.
Rev. Hosp. Ital. B. Aires (2004) ; 42(1): 56-58, mar. 2022.
Article in Spanish | LILACS, BINACIS, UNISALUD | ID: biblio-1369565

ABSTRACT

En el artículo anterior se introdujo el tema y se desarrolló cómo es la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje automático supervisados y los métodos de validación interna que permiten corroborar si el modelo arroja resultados similares a los de otros conjuntos de entrenamiento y de prueba. En este artículo continuaremos con la descripción de la evaluación del rendimiento, la selección del modelo más adecuado para identificar la característica que se va a evaluar y la validación externa del modelo. Además, el artículo resume los desafíos existentes en la implementación del Machine Learning desde la investigación al uso clínico. (AU)


In the previous article, we introduced topics such as data collection and analysis, selection and training of supervised machine learning models and methods of internal validation that allow to corroborate whether the model yields similar results to other training and test sets.In this article, we will continue with the description of the performance evaluation, selecting the most appropriate model to identify the characteristic to evaluate and the external validation of the model. In addition, the article summarizes the actual challenges in the implementation of machine learning from research to clinical use. (AU)


Subject(s)
Humans , Models, Educational , Benchmarking/methods , Machine Learning , Biomedical Technology/methods , Health Sciences, Technology, and Innovation Management
4.
Rev. saúde pública (Online) ; 56: 1-13, 2022. tab, graf
Article in English | LILACS, BBO | ID: biblio-1365958

ABSTRACT

ABSTRACT OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients' mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers


Subject(s)
Humans , Adult , Middle Aged , Aged , COVID-19 Vaccines , COVID-19/prevention & control , Brazil/epidemiology , Retrospective Studies , Vaccination , Hospital Mortality , Machine Learning
5.
São Paulo; s.n; s.n; 2022. 66 p. graf, ilus.
Thesis in English | LILACS | ID: biblio-1397067

ABSTRACT

Neutrophils are polymorphonuclear leukocytes that play a key role in the organism defense. These cells enroll in a range of actions to ensure pathogen elimination and orchestrate both innate and adaptative immune responses. The main physiological structures of neutrophils are their storage organelles that are essential since the cells activation and participate in all their functions. The storage organelles are divided into 2 types: granules and secretory vesicles. The granules are subdivided into azurophilic, specific and gelatinase. The granules are distinguished by their protein content, and since they play an important role on the neutrophil function, the knowledge of the proteins stored in these organelles can help to better understand these cells. Some proteins are present in high abundance and are used as markers for each storage organelle. These proteins are myeloperoxidase (MPO) for azurophil granules, neutrophil gelatinase associated with lipocalin-2 (NGAL) and lactoferrin (LTF) for specific granules, matrix metalloproteinase-9 (MMP9) for gelatinase granules and alkaline phosphatase (AP) for secretory vesicles. The isolation of neutrophils granules, however, is challenging and the existing procedures rely on large sample volumes, about 400 mL of peripheral blood or 3 x 108 neutrophils, not allowing for multiple biological and technical replicates. Therefore, the aim of this study was to develop a miniaturized neutrophil granules isolation method and to use biochemical assays, mass spectrometry-based proteomics and a machine learning approach to investigate the protein content of the neutrophils storage organelles. With that in mind, 40 mL of the peripheral blood of three apparently healthy volunteers were collected. The neutrophils were isolated, disrupted using nitrogen cavitation and organelles were fractionated with a discontinuous 3-layer Percoll density gradient. The presence of granules markers in each fraction was assessed using western blot , gelatin zymography and enzymatic assays. The isolation was proven successful and allowed for a reasonable separation of all neutrophils storage organelles in a gradient of less than 1 mL, about 37 times smaller than the methodsdescribed in the literature. Moreover, mass spectrometry-based proteomics identified 369 proteins in at least 3 of the 5 samples, and using a machine learning strategy, the localization of 140 proteins was predicted with confidence. Furthermore, this study was the first to investigate the proteome of neutrophil granules using technical and biological replicates, creating a reliable database for further studies. In conclusion, the developed miniaturized method is reproducible, cheaper, and reliable. In addition, it provides a resource for further studies exploring neutrophil granules protein content and mobilization during activation with different stimuli


Neutrófilos são leucócitos polimorfonucleares que possuem papel fundamental na defesa do organismo. Essas células desempenham diversas ações a fim de assegurar a eliminação de um patógeno e, além disso, orquestram a resposta imune inata e adaptativa. O conjunto composto pelos grânulos de armazenamento e as vesículas secretórias compõe a principal estrutura fisiológica dos neutrófilos. Estes componentes são essenciais desde a ativação celular, participando de todas as funcionalidades desta célula. Os grânulos são subdivididos em azurófilos, específicos e gelatinase. Eles podem ser distinguidos por meio de seu conteúdo proteico e, como são importantes na funcionalidade dos neutrófilos, identificar quais proteínas são armazenadas nestas organelas é imprescindível para entender melhor essa célula como um todo. Algumas proteínas, estão presentes de forma abundante e, portanto, são utilizadas como marcadores dos grânulos. Tais proteínas são mieloperoxidase (MPO) para os grânulos azurófilos, gelatinase de neutrófilo associada a lipocalina (NGAL) e lactoferrina (LTF) para os específicos, metaloproteinase de matrix 9 (MMP9) para os grânulos de gelatinase e fosfatase alcalina (AP) para as vesículas secretórias. Isolar estas estruturas, no entanto, é desafiador visto que os protocolos existentes na literatura utilizam grandes volumes de amostra, cerca de 400 mL de sangue ou 3 x 108 neutrófilos, para apenas um isolamento, impedindo a realização de replicatas técnicas e biológicas. Desta forma, o objetivo do presente estudo foi desenvolver um protocolo miniaturizado de isolamento dos grânulos neutrofílicos e utilizar métodos bioquímicos, de proteômica e machine learning para investigar o conteúdo proteico destas estruturas celulares. Para isto, 40 mL de sangue periférico de três voluntários aparentemente saudáveis foi coletado. Os neutrófilos foram então isolados, lisados com cavitação de nitrogênio e o fracionamento subcelular foi realizado baseado em um gradiente descontínuo de 3 camadas de Percoll. O método de isolamento foi avaliado através da investigação dos marcadores utilizando western blotting (WB), zimografia de gelatina e ensaios enzimáticos em cada fração coletada. O isolamento demonstrou-se eficiente e permitiu uma ótima separação dos grânulosem um gradiente menor que 1 mL, cerca de 37 vezes menor que os métodos atualmente descritos na literatura. Além disso, a análise proteômica foi capaz de identificar 369 proteínas presentes em pelo menos 3 das 5 réplicas investigadas e, utilizando ferramentas de machine learning, 140 proteínas foram classificadas como pertencentes a um dos tipos de grânulos ou vesícula secretória com alto nível de confiabilidade. Por fim, o presente estudo foi o primeiro a investigar o proteoma dos grânulos utilizando replicatas técnicas e biológicas, criando e fornecendo uma base de dados robusta que poderá ser utilizada em estudos futuros. Conclui-se, portanto, que a metodologia miniaturizada desenvolvida é eficaz, reprodutível e mais barata, além de permitir estudos mais complexos e profundos sobre o proteoma dos grânulos dos neutrófilos em diferentes momentos celulares, tais como quando ativados via estímulos distintos


Subject(s)
Proteomics/instrumentation , Methodology as a Subject , Neutrophils/classification , Mass Spectrometry/methods , Cavitation , Blotting, Western/instrumentation , Gelatinases/analysis , Alkaline Phosphatase/adverse effects , Machine Learning/classification
6.
J. health inform ; 14(1): 26-34, 20220000.
Article in English | LILACS | ID: biblio-1370952

ABSTRACT

Objective: Identify the risk of patients with Chronic Chagas Cardiomyopathy (CCC) to prevent them from having Sudden Cardiac Death (SCD). Methods: We developed an SCD prediction system using a heterogeneous dataset of chagasic patients evaluated in 9 state-of-the-art machine learning algorithms to select the most critical clinical variables and predict SCD in chagasic patients even when the interval between the most recent exams and the SCD event is months or years. Results: 310 patients were analyzed, being 81 (14,7%) suffering from SCD. In the study, Balanced Random Forest showed the best performance, with AUC:80.03 and F1:75.12. Due to their high weights in the machine learning classifiers, we suggest Holter - Non-Sustained Ventricular Tachycardia, Total Ventricular Extrasystoles, Left Ventricular Systolic Diameter, Syncope, and Left Ventricular Diastolic Diameter as essential features to identify SCD. Conclusion: The high-risk pattern of SCD in patients with CCC can be identified and prevented based on clinical and laboratory variables.


Objetivo: Identificar o risco de pacientes com Cardiomiopatia Chagásica Crônica (CCC) para prevenir a Morte Súbita Cardíaca (MSC). Métodos: Desenvolvemos um sistema de MSC usando um conjunto de dados heterogêneo de pacientes chagásicos avaliados em 9 algoritmos de aprendizado de máquina de última geração para selecionar as variáveis clínicas mais críticas e prever MSC em pacientes chagásicos mesmo quando o intervalo mais recente entre os mais recentes exames e o evento MSC é meses ou anos. Resultados: Foram analisados 310 pacientes, sendo 81 (14,7%) portadores de CCC. No estudo, o algoritmo Balanced Random Forest apresentou o melhor desempenho, com AUC:80,03 e F1:75,12. Devido ao seu alto peso nos classificadores de aprendizado de máquina, sugerimos Holter - Taquicardia Ventricular Não Sustentada, Extrassístoles Ventriculares Totais, Diâmetro Sistólico do Ventrículo Esquerdo, Síncope e Diâmetro Diastólico do Ventrículo Esquerdo como características essenciais para identificar a CCC. Conclusão: O padrão de alto risco de MSC em pacientes com CCC pode ser identificado e prevenido com base em variáveis clínicas e laboratoriais.


Objetivo: Identificar el riesgo de los pacientes con Miocardiopatía Chagásica Crónica (MCC) para evitar que presenten Muerte Cardíaca Súbita (MCS). Métodos: Desarrollamos un sistema MCS utilizando un conjunto de datos heterogéneo de pacientes chagásicos evaluados en 9 algoritmos de aprendizaje automático de última generación para seleccionar las variables clínicas más críticas y predecir MCS en pacientes chagásicos incluso cuando el intervalo más reciente entre los más recientes exámenes y el evento MCS es meses o años. Resultados: Se analizaron 310 pacientes, siendo 81 (14,7%) con MSC. En el estudio, Balanced Random Forest mostró el mejor desempeño, con AUC:80.03 y F1:75.12. Debido a su alto peso en los clasificadores de aprendizaje automático, sugerimos Holter - Taquicardia ventricular no sostenida, Extrasístoles ventriculares totales, Diámetro sistólico del ventrículo izquierdo, Síncope y Diámetro diastólico del ventrículo izquierdo como características esenciales para identificar la MSC. Conclusión: El patrón de alto riesgo de MSC en pacientes con MCC se puede identificar y prevenir con base en variables clínicas y de laboratorio.


Subject(s)
Humans , Male , Female , Chagas Cardiomyopathy/complications , Death, Sudden, Cardiac/prevention & control , Machine Learning , Algorithms , Chronic Disease , Probability , Risk Assessment , Electrocardiography
7.
Rev. bras. oftalmol ; 81: e0056, 2022. tab, graf
Article in English | LILACS | ID: biblio-1394863

ABSTRACT

ABSTRACT It is part of the omic sciences to search for an understanding of how the cellular system of organisms works as well as studying their biological changes. As part of the omic sciences, we can highlight the genomics whose function is the study of genes, the transcriptomics that studies the changes in the transcripts, the proteomics responsible for understanding the changes that occur in proteins, and the metabolomics that studies all the metabolic changes that occur in a certain system when it is submitted to different types of stimuli. Metabolomics is the science that studies the endogenous and exogenous metabolites in biological systems, which aims to provide comparative quantitative or semi-quantitative information about all metabolites in the system. This review aims to describe the main applications of metabolomics science in ophthalmolog. We searched the literature on main applications of metabolomics science in ophthalmology, using the MEDLINE and LILACS databases, with the keywords "metabolomics" and "ophthalmology", from January 1, 2009, to April 5, 2021. We retrieved 216 references, of which 58 were considered eligible for intensive review and critical analysis. The study of the metabolome allows a better understanding of the metabolism of ocular tissues. The results are important to aid diagnosis and as predictors of the progression of many eye and systemic diseases.


RESUMO Faz parte das ciências ômicas buscar entender como funciona o sistema celular dos organismos e estudar suas alterações biológicas. Como parte das ciências ômicas, destacam-se a genômica, cuja função é o estudo dos genes; a transcriptômica, que estuda as mudanças nos transcritos; a proteômica, responsável por entender as mudanças que ocorrem nas proteínas, e a metabolômica, que estuda todo o metabolismo das alterações que ocorrem em um determinado sistema quando ele é submetido a diferentes tipos de estímulos. A metabolômica é a ciência que estuda os metabólitos endógenos e exógenos em sistemas biológicos, visando fornecer informações comparativas quantitativas ou semiquantitativas sobre todos os metabólitos do sistema. Esta revisão teve como objetivo descrever as principais aplicações da ciência metabolômica na oftalmologia. Trata-se de revisão narrativa desenvolvida por um grupo de pesquisa da Universidade Federal de São Paulo, em São Paulo (SP). Buscaram-se, na literatura, as principais aplicações da ciência metabolômica em oftalmologia, utilizando as bases de dados Medline® e Lilacs, com as palavras-chave "metabolomics" e "oftalmologia", de 1º de janeiro de 2009 a 5 de abril de 2021. Foram recuperadas 216 referências, das quais 58 foram consideradas elegíveis para revisão intensiva e análise crítica. O estudo do metaboloma permite um melhor entendimento do metabolismo dos tecidos oculares. Os resultados são importantes para auxiliar no diagnóstico e como preditores da progressão de muitas doenças oculares e sistêmicas.


Subject(s)
Humans , Eye Diseases/metabolism , Metabolome/physiology , Retina/metabolism , Artificial Intelligence , Biomarkers/metabolism , Cornea/metabolism , Eye Diseases/diagnosis , Metabolomics/methods , Machine Learning
8.
Article in English | LILACS, BBO | ID: biblio-1395085

ABSTRACT

ABSTRACT Artificial intelligence develops rapidly and health is one of the areas where new technologies in this field are most promising. The use of artificial intelligence can modify the way health care and self-care are provided, besides influencing the organization of health systems. Therefore, the regulation of artificial intelligence in healthcare is an emerging and essential topic. Specific laws and regulations are being developed around the world. In Brazil, the starting point of this regulation is the Lei Geral de Proteção de Dados Pessoais (LGPD - General Personal Data Protection Law), which recognizes the right to explanation and review of automated decisions. Discussing the scope of this right is needed, considering the necessary instrumentalization of transparency in the use of artificial intelligence for health and the currently existing limits, such as the black-box system inherent to algorithms and the trade-off between explainability and accuracy of automated systems.


RESUMO A inteligência artificial se desenvolve rapidamente e a saúde é uma das áreas em que as novas tecnologias desse campo são mais promissoras. O uso de inteligência artificial tem potencial para modificar a forma de prestação da assistência à saúde e do autocuidado, além de influenciar a organização dos sistemas de saúde. Por isso, a regulação da inteligência artificial na saúde é um tema emergente e essencial. Leis e normas específicas são elaboradas em todo o mundo. No Brasil, o marco inicial dessa regulação é a Lei Geral de Proteção de Dados Pessoais, a partir do reconhecimento do direito à explicação e à revisão de decisões automatizadas. É preciso debater a abrangência desse direito, considerando a necessária instrumentalização da transparência no uso da inteligência artificial na saúde e os limites atualmente existentes, como a dimensão caixa-preta inerente aos algoritmos e o trade-off existente entre explicabilidade e precisão dos sistemas automatizados.


Subject(s)
Brazil , Health Systems/organization & administration , Artificial Intelligence/legislation & jurisprudence , Comprehensive Health Care , Privacy , Health Law , Machine Learning , Health Services Research
9.
Chinese Journal of Stomatology ; (12): 540-546, 2022.
Article in Chinese | WPRIM | ID: wpr-935899

ABSTRACT

With the advent of the era of big data, artificial intelligence based on machine learning, especially artificial neural network has rapidly developed and applicated in the field of stomatology, owning huge potential in segmentation and labelling of three-dimensional intraoral anatomical features. Automatic segmentation, labelling and diagnosis can assist dentists and technicians to complete heavy and repeat work, change stomatology from subjective perception to objective science, and help to make diagnosis and treatment plan efficiently and accurately. This review briefly summarized related knowledge and development of machine learning and artificial neural network, its application status and existing problems in the field of segmentation and labelling of three-dimensional intraoral anatomical features, and provided an outlook of its future development.


Subject(s)
Artificial Intelligence , Machine Learning , Neural Networks, Computer
10.
Article in Chinese | WPRIM | ID: wpr-935298

ABSTRACT

The micronucleomics test can comprehensively display a variety of harmful endpoints, such as DNA damage and repair, chromosome breakage or loss and cell growth inhibition, with fast, simple and economical feature. Micronucleomics is not only widely used in the comprehensive assessment of the types and modes of genetic action of exogenous chemicals (such as drugs, food additives, cosmetics, environmental pollutants, etc.), but also plays an important role in the screening and risk assessment of cancer population at high risk. However, the traditional micronucleomics image counting method has the characteristics of time-consuming, low accuracy, and high cost, which cannot meet the current analysis requirements of large-scale, multi-index, rapidity, high precision and visualization. In recent years, with the rapid development of the era of precision medicine based on big data, visualized analysis of new micronucleomics based on machine learning and detection strategies based on deep learning have shown a good application prospect. This review, based on the application value of micronucleomics, systematically compares the traditional and new artificial intelligence counting of micronucleus images, and discusses the future direction of micronucleus image detection.


Subject(s)
Artificial Intelligence , Big Data , Humans , Machine Learning , Precision Medicine
11.
Article in Chinese | WPRIM | ID: wpr-928204

ABSTRACT

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.


Subject(s)
Algorithms , Gait , Humans , Machine Learning , Neural Networks, Computer , Walking
12.
Article in Chinese | WPRIM | ID: wpr-928196

ABSTRACT

Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.


Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography/methods , Humans , Imagination , Machine Learning
13.
Article in Chinese | WPRIM | ID: wpr-928130

ABSTRACT

A high-throughput screening machine learning model for mitochondrial function was constructed, and compounds of Aco-niti Lateralis Radix Praeparata were predicted. Deoxyaconitine with the highest score and benzoylmesaconine with the lowest score among the compounds screened by the model were selected for mitochondrial mechanism analysis. Mitochondrial function data were collected from PubChem and Tox21 databases. Random forest and gradient boosted decision tree algorithms were separately used for mo-deling, and ECFP4(extended connectivity fingerprint, up to four bonds) and Mordred descriptors were employed for training, respectively. Cross-validation test was carried out, and balanced accuracy(BA) and overall accuracy were determined to evaluate the performance of different combinations of models and obtain the optimal algorithm and hyperparameters for modeling. The data of Aconiti Lateralis Radix Praeparata compounds in TCMSP database were collected, and after prediction and screening by the constructed high-throughput screening machine learning model, deoxyaconitine and benzoylmesaconine were selected to measure mitochondrial membrane potential, reactive oxygen species(ROS) level and protein expression of B-cell lymphoma 2(Bcl-2), Bcl-2-associated X protein(Bax) and peroxisome proliferator-activated receptor-γ-coactivator 1α(PGC-1α). The results showed that the model constructed using gradient boosted decision tree+Mordred algorithm performed better, with a cross-validation BA of 0.825 and a test set accuracy of 0.811. Deoxyaconitine and benzoylmesaconine changed the ROS level(P<0.001), mitochondrial membrane potential(P<0.001), and protein expression of Bcl-2(P<0.001, P<0.01) and Bax(P<0.001), and deoxyaconitine increased the expression of PGC-1α protein(P<0.01). The high-throughput screening model for mitochondrial function constructed by gradient boosted decision tree+Mordred algorithm was more accurate than that by random forest+ECFP4 algorithm, which could be used to build an algorithm model for subsequent research. Deoxyaconitine and benzoylmesaconine affected mitochondrial function. However, deoxyaconitine with higher score also affected mitochondrial biosynthesis by regulating PGC-1α protein.


Subject(s)
Aconitum/chemistry , Algorithms , Drugs, Chinese Herbal/chemistry , High-Throughput Screening Assays , Machine Learning , Mitochondria , Reactive Oxygen Species , bcl-2-Associated X Protein
14.
Article in English | WPRIM | ID: wpr-927680

ABSTRACT

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019 (COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web interface with adjustable parameters, including choice of intervention measures, intervention weights, vaccination, and viral variants, was designed for users to run the simulation. The total case number was set as the outcome. The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set. Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model. The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200, which were 25 more days and 36 fewer cases than the real situation, respectively. Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people's livelihood.


Subject(s)
COVID-19/prevention & control , China/epidemiology , Humans , Machine Learning , Pandemics/prevention & control , Policy , Reproducibility of Results , SARS-CoV-2
15.
Rev. cuba. anestesiol. reanim ; 20(3): e713, 2021.
Article in Spanish | LILACS, CUMED | ID: biblio-1351983

ABSTRACT

Introducción: La administración manual en bolo ha evolucionado desde la infusión volumétrica basada en regímenes farmacológicos estandarizados, hasta los sistemas de infusión controlada por objetivo y los más sofisticados sistemas de circuito cerrado. Objetivo: Describir los principios tecnológicos y aplicaciones clínicas extendidas de la infusión controlada por objetivo y los sistemas de circuito cerrado. Métodos: Se realizó una revisión no sistemática de la literatura, en bases de datos científicas como Cochrane Database of Systematic Reviews, Pubmed/Medline, EMBASE, Scopus, Web of Science, EBSCOhost, Science Direct, OVID y el buscador académico Google Scholar, en el mes de septiembre del año 2020. Desarrollo: La disponibilidad y portabilidad de dispositivos electrónicos con capacidad de procesamiento avanzado a precios relativamente accesibles, el perfeccionamiento del aprendizaje automático e inteligencia artificial aplicado a las decisiones médicas, y las iteraciones tecnológicas complejas incorporadas en los sistemas de circuito abierto y cerrado, desarrollados originalmente en el campo de la Anestesiología, han posibilitado su expansión a otras especialidades y entornos clínicos tan disímiles como el tratamiento de la diabetes mellitus, administración de fármacos antineoplásicos, ventilación mecánica, control de las variables hemodinámicas y la terapia antimicrobiana en pacientes críticos. Conclusiones: La infusión controlada por objetivo y los sistemas de circuito cerrado se han convertido en tecnologías maduras, seguras y viables, aplicadas clínicamente en múltiples naciones y escenarios, con un desempeño superior a los sistemas manuales tradicionales(AU)


Introduction: Manual bolus administration has evolved from volumetric infusion based on standardized pharmacological regimens to target-controlled infusion systems and the most sophisticated closed-loop systems. Objective: To describe the technological principles and extended clinical applications of target-controlled infusion and closed-loop systems. Methods: A nonsystematic review of the literature was carried out, during September 2020, in scientific databases such as Cochrane Database of Systematic Reviews, Pubmed/Medline, EMBASE, Scopus, Web of Science, EBSCOhost, Science Direct, OVID and the academic search engine Google Scholar. Development: The availability and portability of electronic devices with advanced processing capacity at relatively affordable prices, the refinement of machine learning and artificial intelligence applied to medical decisions, as well as the complex technological iterations incorporated into open and closed-loop systems, originally developed in the field of anesthesiology, have enabled their expansion to other specialties and clinical settings so diverse as treatment of diabetes mellitus, administration of antineoplastic drugs, mechanical ventilation, control of hemodynamic variables and antimicrobial therapy in critical patients. Conclusions: Target-controlled infusion and closed-loop systems have become mature, safe and viable technologies, applied clinically in multiple nations and settings, with superior performance compared to traditional manual systems(AU)


Subject(s)
Humans , Male , Female , Artificial Intelligence , Machine Learning , Anesthesiology , Anesthesia, Closed-Circuit/methods , Early Goal-Directed Therapy
16.
Rev. Hosp. Ital. B. Aires (2004) ; 41(4): 206-209, dic. 2021. ilus
Article in Spanish | LILACS, BINACIS, UNISALUD | ID: biblio-1367103

ABSTRACT

Este será el primero de dos artículos donde se tratarán los pasos necesarios para desarrollar un proyecto de aplicación de técnicas de Machine Learning en Salud, que introduce nociones sobre la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje auto-mático de tipo supervisado y los métodos de validación interna para cada modelo. (AU)


This will be the first of two articles where the steps needed to apply machine learning methods in healthcare will be discussed. It will introduce fundamental notions about data collection, selection and training of supervised ML models as well as the methods of internal validation. In a second article, we will discuss about the performance evaluation to select the most appropriate model and its external validation. (AU)


Subject(s)
Models, Educational , Health Sciences, Technology, and Innovation Management , Machine Learning , Algorithms , Data Collection/methods , Data Analysis
17.
urol. colomb. (Bogotá. En línea) ; 30(3): 153-154, 15/09/2021.
Article in English | LILACS, COLNAL | ID: biblio-1369069

ABSTRACT

The use of robotic technology in minimally invasive surgery has expanded worldwide in the last two decades. Currently being the DaVinci system of the Intuitive Surgical company, one of the most used in many surgical specialties within them, Urology. This system has obtained more than 1500 patents in the robotics area; however, some have already expired. Thus, allowing the development of new platforms as an alternative to the monopoly achieved by this system. Some robotic platforms in actual development feature a modular design, open console, smaller instruments (<8mm), and haptic feedback (tactile sensation). These new designs of robotic surgery technology highlight cost-effective systems, single port surgery, artificial intelligence for machine learning, but it should be noted that they face a long and complex process of clinical studies and approval by regulatory entities.


El uso de la tecnología robótica en cirugía mínimamente invasiva se ha extendido por todo el mundo en las dos últimas décadas. Actualmente es el sistema DaVinci de la empresa Intuitive Surgical, uno de los más utilizados en muchas especialidades quirúrgicas dentro de ellas, la Urología. Este sistema ha obtenido más de 1500 patentes en el área de la robótica; sin embargo, algunas ya han caducado. Permitiendo así el desarrollo de nuevas plataformas como alternativa al monopolio conseguido por este sistema. Algunas plataformas robóticas en desarrollo actual presentan un diseño modular, consola abierta, instrumentos más pequeños (<8 mm) y retroalimentación háptica (sensación táctil). En estos nuevos diseños de tecnología de cirugía robótica destacan los sistemas rentables, la cirugía de puerto único y la inteligencia artificial para el aprendizaje automático, pero hay que tener en cuenta que se enfrentan a un largo y complejo proceso de estudios clínicos y aprobación por parte de las entidades reguladoras.


Subject(s)
Humans , Artificial Intelligence , Robotic Surgical Procedures , Technology , Robotics , Minimally Invasive Surgical Procedures , Machine Learning , Haptic Technology
19.
Rev. cuba. oftalmol ; 34(2): e1152, 2021.
Article in Spanish | LILACS, CUMED | ID: biblio-1341465

ABSTRACT

El presente trabajo aborda el uso de la inteligencia artificial en la cirugía de catarata y la incursión de Cuba en este campo. La inteligencia artificial tiene como objetivo dotar a un agente con la capacidad de tomar decisiones correctas. Dentro de los campos de la inteligencia artificial se encuentra el aprendizaje de máquinas cuyo propósito es entrenar a las computadoras para aprender de un conjunto de datos las decisiones que han de tomar, dada una situación específica. Uno de los métodos más utilizados para el entrenamiento y el aprendizaje de máquinas es el desarrollo de redes neuronales artificiales. Desde un enfoque social, se explica cómo la influencia sobre el resultado visual que puede lograrse con esta tecnología repercute en el individuo y la sociedad, y se resaltan las ventajas y las desventajas de su utilización(AU)


The study addresses the use of artificial intelligence in cataract surgery and Cuba's incorporation into this field. The purpose of artificial intelligence is to develop agents with the ability to take appropriate decisions. One of the branches of artificial intelligence is machine learning, whose aim is to train computers to draw from a set of data the decisions to be taken in response to a specific situation. One of the most common methods in machine training and learning is the development of artificial neural networks. A social explanation is provided of the effect of the visual outcomes obtained by this technology on the individual and society, highlighting the advantages and disadvantages of its use(AU)


Subject(s)
Humans , Artificial Intelligence , Cataract Extraction/methods , Machine Learning
20.
Arq. bras. cardiol ; 116(6): 1091-1098, Jun. 2021. tab, graf
Article in English, Portuguese | LILACS | ID: biblio-1278330

ABSTRACT

Resumo Fundamento A quantificação não invasiva da reserva fracionada de fluxo miocárdico (FFR TC ) através de software baseado em inteligência artificial em versão mais atualizada e tomógrafo de última geração (384 cortes) apresenta elevada performance na detecção de isquemia coronariana. Objetivos Avaliar o desempenho diagnóstico da FFR TC na detecção de doença arterial coronariana (DAC) significativa em relação ao FFRi, em tomógrafos de gerações anteriores (128 e 256 cortes). Métodos Estudo retrospectivo com pacientes encaminhados à angiotomografia de artérias coronárias (TCC) e cateterismo (FFRi). Foram utilizados os tomógrafos Siemens Somatom Definition Flash (256 cortes) e AS+ (128 cortes). A FFR TC e a área luminal mínima (ALM) foram avaliadas em software (cFFR versão 3.0.0, Siemens Healthineers, Forchheim, Alemanha). DAC obstrutiva foi definida como TCC com redução luminal ≥50% e DAC funcionalmente obstrutiva como FFRi ≤0,8. Todos os valores de p reportados são bicaudais; e quando <0,05, foram considerados estatisticamente significativos. Resultados Noventa e três pacientes consecutivos (152 vasos) foram incluídos. Houve boa concordância entre FFR TC e FFRi, com mínima superestimação da FFR TC (viés: -0,02; limites de concordância: 0,14 a 0,09). Diferentes tomógrafos não modificaram a relação entre FFR TC e FFRi (p para interação = 0,73). A FFR TC demonstrou performance significativamente superior à classificação visual de estenose coronariana (AUC 0,93 vs. 0,61, p <0,001) e à ALM (AUC 0,93 vs. 0,75, p <0,001) reduzindo o número de casos falso-positivos. O melhor ponto de corte para a FFR TC utilizando um índice de Youden foi de 0,85 (sensiblidade, 87%; especificidade, 86%; VPP, 73%; NPV, 94%), com redução de falso-positivos. Conclusão FFR TC baseada em inteligência artificial, em tomógrafos de gerações anteriores (128 e 256 cortes), apresenta boa performance diagnóstica na detecção de DAC, podendo ser utilizada para reduzir procedimentos invasivos.


Abstract Background The non-invasive quantification of the fractional flow reserve (FFRCT) using a more recent version of an artificial intelligence-based software and latest generation CT scanner (384 slices) may show high performance to detect coronary ischemia. Objectives To evaluate the diagnostic performance of FFRCT for the detection of significant coronary artery disease (CAD) in contrast to invasive FFR (iFFR) using previous generation CT scanners (128 and 256- detector rows). Methods Retrospective study with patients referred to coronary artery CT angiography (CTA) and catheterization (iFFR) procedures. Siemens Somatom Definition Flash (256-detector rows) and AS+ (128-detector rows) CT scanners were used to acquire the images. The FFRCT and the minimal lumen area (MLA) were evaluated using a dedicated software (cFFR version 3.0.0, Siemens Healthineers, Forchheim, Germany). Obstructive CAD was defined as CTA lumen reduction ≥ 50%, and flow-limiting stenosis as iFFR ≤0.8. All reported P values are two-tailed, and when <0.05, they were considered statistically significant. Results Ninety-three consecutive patients (152 vessels) were included. There was good agreement between FFRCT and iFFR, with minimal FFRCT overestimation (bias: -0.02; limits of agreement:0.14-0.09). Different CT scanners did not modify the association between FFRCT and FFRi (p for interaction=0.73). The performance of FFRCT was significantly superior compared to the visual classification of coronary stenosis (AUC 0.93vs.0.61, p<0.001) and to MLA (AUC 0.93vs.0.75, p<0.001), reducing the number of false-positive cases. The optimal cut-off point for FFRCT using a Youden index was 0.85 (87% Sensitivity, 86% Specificity, 73% PPV, 94% NPV), with a reduction of false-positives. Conclusion Machine learning-based FFRCT using previous generation CT scanners (128 and 256-detector rows) shows good diagnostic performance for the detection of CAD, and can be used to reduce the number of invasive procedures.


Subject(s)
Humans , Coronary Artery Disease , Coronary Stenosis , Fractional Flow Reserve, Myocardial , Severity of Illness Index , Artificial Intelligence , Tomography, X-Ray Computed , Predictive Value of Tests , Retrospective Studies , Coronary Angiography , Constriction, Pathologic , Coronary Vessels , Machine Learning , Computed Tomography Angiography
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