Artificial Intelligence and Machine Learning in Cardiology - A Change of Paradigm

Mesquita, Cláudio Tinoco

doi:10.5935/2359-4802.20170027

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…A história e evolução do uso do aprendizado de máquina na medicina representam um avanço significativo na área da saúde. Como apontado por Mesquita (2017), essa evolução representa uma mudança de paradigma na cardiologia e em outras especialidades médicas.…”

Section: História E Evolução Do Uso De Aprendizado De Máquina Na Medi...unclassified

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Aprendizado De Máquina Na Medicina: Como Algoritmos De Aprendizado De Máquina Podem Ser Aplicados Em Diagnósticos Médicos, Prognósticos E Descoberta De Novos Tratamentos

Garzon,

Barbosa

2023

RECIMA21

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A aplicação de algoritmos de aprendizado de máquina na medicina representa uma revolução significativa no diagnóstico, prognóstico e descoberta de tratamentos médicos. Este resumo explora como esses algoritmos têm sido utilizados para melhorar a prática médica e promover avanços na área da saúde. O objetivo deste foi destacar a importância e as aplicações dos algoritmos de aprendizado de máquina na medicina, bem como resumir seus benefícios e desafios. A metodologia deste resumo envolveu uma revisão da literatura médica e científica, com foco nas principais pesquisas e tendências relacionadas ao uso de aprendizado de máquina na medicina. Foram analisados artigos e estudos que abordaram diagnósticos médicos, prognósticos e descoberta de tratamentos. O uso de algoritmos de aprendizado de máquina na medicina tem revolucionado a prática clínica, permitindo diagnósticos mais precisos, prognósticos personalizados e acelerado a descoberta de novos tratamentos. No entanto, desafios éticos, de privacidade e interpretação de dados continuam sendo importantes considerações. É fundamental que a comunidade médica e científica continue a explorar e aproveitar essa tecnologia de forma ética e responsável para melhorar a saúde global.

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Section: História E Evolução Do Uso De Aprendizado De Máquina Na Medi...unclassified

“…Mesquita (2017) destaca que essa abordagem representa uma mudança de paradigma na uma análise mais precisa e eficiente dos dados relacionados a essa área da medicina.…”

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Aprendizado De Máquina Na Medicina: Como Algoritmos De Aprendizado De Máquina Podem Ser Aplicados Em Diagnósticos Médicos, Prognósticos E Descoberta De Novos Tratamentos

Garzon,

Barbosa

2023

RECIMA21

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“…24,25 In cardiology, machine learning has been used in electrocardiography and echocardiography for automatic pattern recognition and classification of abnormal rhythms, interpretation of images, in nuclear cardiology to predict myocardial revascularization, among other approaches. 26 Strategies to capture signals (image and sounds) from a smartphone's source have advanced rapidly, and several applications are capable of performing these tasks. It is noted that the biggest challenges in signal processing are eliminating noise, and improving prediction accuracy.…”

Section: Is It Time To Use Artificial Intelligence?mentioning

confidence: 99%

“…Considering the advances in the use of AI, it is important to consider the ethical aspects regarding the use of data, which can generate doubts regarding the responsibilities of the doctor and about the software. 26 Nevertheless, technological tools are available to help but not replace clinical practice.…”

Section: Is It Time To Use Artificial Intelligence?mentioning

confidence: 99%

Smartphone-Based Screening for Cardiovascular Diseases: A Trend?

Mazzu-Nascimento¹,

Evangelista²,

Abubakar³

et al. 2021

International Journal of Cardiovascular Sciences

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Cardiovascular diseases are the leading cause of death in the world. People living in vulnerable and poor places such as slums, rural areas and remote locations have difficulty in accessing medical care and diagnostic tests. In addition, given the COVID-19 pandemic, we are witnessing an increase in the use of telemedicine and noninvasive tools for monitoring vital signs. These questions motivate us to write this point of view and to describe some of the main innovations used for non-invasive screening of heart diseases. Smartphones are widely used by the population and are perfect tools for screening cardiovascular diseases. They are equipped with camera, flashlight, microphone, processor, and internet connection, which allow optical, electrical, and acoustic analysis of cardiovascular phenomena. Thus, when using signal processing and artificial intelligence approaches, smartphones may have predictive power for cardiovascular diseases. Here we present different smartphone approaches to analyze signals obtained from various methods including photoplethysmography, phonocardiograph, and electrocardiography to estimate heart rate, blood pressure, oxygen saturation (SpO 2 ), heart murmurs and electrical conduction. Our objective is to present innovations in non-invasive diagnostics using the smartphone and to reflect on these trending approaches. These could help to improve health access and the screening of cardiovascular diseases for millions of people, particularly those living in needy areas.

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“…The study carried out by Mesquita (2017) discusses how AI and ML can be used in complex problems in science. With a focus on Cardiology, the author demonstrates that methods are now incorporated to find abnormalities accurately.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Applied to the Early Diagnosis of Leukemia Using Biomarkers

Ferry

Castro

Pereira

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Leukemia is a rare and lethal blood cancer. One of the factors that increase the patient's chances of better treatment results is early diagnosis. The best attempt to discover leukemia usually is the image analysis exams, but this is costly, and sometimes it is late. Thus, this paper uses attributes of a complete blood count as input to Machine Learning algorithms to predict earlier and cheaper leukemia diagnoses. In this paper, we collected actual exam results. We developed a synthetic dataset with 1000 examples based on the distribution and limits of each attribute to classify a patient in positive or negative for leukemia. We tested different classifiers (Logistic Regression, Random Forest, XGBoost, and SVM) to predict sample classes. We show that it is possible with an accuracy of 96% to predict if a patient is likely to have leukemia based on its blood count.

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Artificial Intelligence and Machine Learning in Cardiology - A Change of Paradigm

Cited by 5 publications

References 11 publications

Aprendizado De Máquina Na Medicina: Como Algoritmos De Aprendizado De Máquina Podem Ser Aplicados Em Diagnósticos Médicos, Prognósticos E Descoberta De Novos Tratamentos

Aprendizado De Máquina Na Medicina: Como Algoritmos De Aprendizado De Máquina Podem Ser Aplicados Em Diagnósticos Médicos, Prognósticos E Descoberta De Novos Tratamentos

Smartphone-Based Screening for Cardiovascular Diseases: A Trend?

Machine Learning Applied to the Early Diagnosis of Leukemia Using Biomarkers

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