Artificial intelligence, applications and challenges in simulation-based education

The rapid advancement of Artificial Intelligence (AI) has taken the world by "surprise" due to the lack of regulation over this technological innovation which, while promising application opportunities in different fields of knowledge, including education, simultaneously generates concern, rejection and even fear. In the field of Health Sciences Education, clinical simulation has transformed educational practice; however, its formal insertion is still heterogeneous, and we are now facing a new technological revolution where AI has the potential to transform the way we conceive its application.

El rápido avance de la inteligencia artificial (IA) ha tomado al mundo por "sorpresa" debido a la falta de regulación sobre esta innovación tecnológica, que si bien promete oportunidades de aplicación en diferentes campos del conocimiento, incluido el educativo, también genera preocupación e incluso miedo y rechazo. En el campo de la Educación en Ciencias de la Salud la Simulación Clínica ha transformado la práctica educativa; sin embargo, aún es heterogénea su inserción formal, y ahora nos enfrentamos a una nueva revolución tecnológica, en la que las IA tienen el potencial de transformar la manera en que concebimos su aplicación.

Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population

ABSTRACT Introduction: Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher risk of needing this procedure is crucial. Widely used risk scores to predict the need for blood transfusions have yielded unsatisfactory results when validated for the Brazilian population. Methods: In this retrospective study, machine learning (ML) algorithms were compared to predict the need for blood transfusions in a cohort of 495 cardiac surgery patients treated at a Brazilian reference service between 2019 and 2021. The performance of the models was evaluated using various metrics, including the area under the curve (AUC), and compared to the commonly used Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) scoring systems. Results: The study found that the model had the highest performance, achieving an AUC of 0.7350 (confidence interval [CI]: 0.7203 to 0.7497). Importantly, all ML algorithms performed significantly better than the commonly used TRACK and TRUST scoring systems. TRACK had an AUC of 0.6757 (CI: 0.6609 to 0.6906), while TRUST had an AUC of 0.6622 (CI: 0.6473 to 0.6906). Conclusion: The findings of this study suggest that ML algorithms may offer a more accurate prediction of the need for blood transfusions than the traditional scoring systems and could enhance the accuracy of predicting blood transfusion requirements in cardiac surgery patients. Further research could focus on optimizing and refining ML algorithms to improve their accuracy and make them more suitable for clinical use.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience / Predição do influxo de atendimentos e casos de trauma em um serviço de urgência oftalmológico brasileiro realizado por um oftalmologista sem experiência em programação

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

RESUMO Objetivo: Esse estudo tem como objetivo criar um modelo de Machine Learning por um oftalmologista sem experiência em programação utilizando auto Machine Learning predizendo influxo de pacientes em serviço de emergência e casos de trauma. Métodos: Um dataset de 366,610 visitas em Hospital Universitário da Universidade Federal de São Paulo de 01 de janeiro de 2014 até 31 de dezembro de 2019 foi incluído no treinamento do modelo, incluindo visitas/dia e código internacional de doenças. O treinamento e predição foram realizados com o Amazon Forecast por dois oftalmologistas sem experiência com programação. Resultados: O período de previsão estimou um volume de 206,37 pacientes/dia em p90, 180,75 em p50, 140,35 em p10 e média de 7,42 casos de trauma/dia em p90, 3,99 em p50 e 0,56 em p10. Janeiro de 2020 teve um total de 6.604 pacientes e média de 206,37 pacientes/dia, 13,5% menos do que a predição em p50. O período teve um total de 199 casos de trauma e média de 6,21 casos/dia, 55,77% mais casos do que a predição em p50. Conclusão: O desenvolvimento de modelos era restrito a cientistas de dados com experiencia em programação, porém a transferência de ensino com a tecnologia de auto Machine Learning permite o desenvolvimento de algoritmos por qualquer pessoa sem experiencia em programação. Esse estudo mostra um modelo com valores preditos próximos ao que ocorreram em janeiro de 2020. Fatores que podem ter influenciados no resultado foram feriados e tamanho do banco de dados. Esse é o primeiro estudo que aplicada auto Machine Learning em predição de visitas hospitalares com resultados próximos aos que ocorreram.

Machine learning in predicting severe acute respiratory infection outbreaks / Aprendizado de máquina na previsão de surtos de síndrome respiratória aguda grave / Aprendizaje automático en la predicción de brotes de síndrome respiratorio agudo grave

Abstract: Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Resumo: Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.

Resumen: Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.

Food patterns associated with overweight in 7-11-year old children: machine-learning approach / Padrões alimentares associados ao excesso de peso em crianças de 7 a 11 anos: abordagem de aprendizado de máquina

Abstract Longitudinal study, whose objective was to present a better strategy and statistical methods, and demonstrate its use with the data across the 2013-2015 period in schoolchildren aged 7 to 11 years, covered with the same food questionnaire (WebCAAFE) survey in Florianopolis, southern Brazil. Six meals/snacks and 32 foods/beverages yielded 192 possible combinations denominated meal/snack-Specific Food/beverage item (MSFIs). LASSO algorithm (LASSO-logistic regression) was used to determine the MSFIs predictive of overweight/obesity, and then binary (logistic) regression was used to further analyze a subset of these variables. Late breakfast, lunch and dinner were all associated with increased overweight/obesity risk, as was an anticipated lunch. Time-of-day or meal-tagged food/beverage intake result in large number of variables whose predictive patterns regarding weight status can be analyzed by machine learning such as LASSO, which in turn may identify the patterns not amenable to other popular statistical methods such as binary logistic regression.

Resumo Estudo longitudinal cujo objetivo foi apresentar melhores estratégia e métodos estatísticos e demonstrar sua utilização com os dados do período 2013-2015 em escolares de 7 a 11 anos, contemplados com o mesmo questionário alimentar (WebCAAFE) em Florianópolis, Sul do Brasil. Seis refeições/lanches e 32 alimentos/bebidas resultaram em 192 combinações possíveis denominadas item refeição/lanche-alimentos/bebidas específicos (MSFIs). O algoritmo LASSO (LASSO-regressão logística) foi usado para determinar os MSFIs preditivos de sobrepeso/obesidade e, em seguida, a regressão binária (logística) foi usada para analisar um subconjunto dessas variáveis. Café da manhã, almoço e jantar tardios foram todos associados ao aumento do risco de sobrepeso/obesidade, assim como um almoço antecipado. O consumo de alimentos/bebidas marcados na hora do dia ou na refeição resulta em um grande número de variáveis ​​cujos padrões preditivos em relação ao status do peso podem ser analisados ​​por LASSO. Essa análise pode identificar os padrões não passíveis de outros métodos estatísticos populares, como a regressão logística binária.

Research progress of artificial intelligence in pathological subtypes classification and gene expression analysis of lung adenocarcinoma / 中国胸心血管外科临床杂志

@#Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.

Construction of a machine learning model for identifying clinical high-risk carotid plaques based on radiomics / 中国胸心血管外科临床杂志

@#Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.

Development and prospects of predicting drug polymorphs technology / 药学学报

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.

Métodos de aprendizaje automático para predecir el comportamiento epidemiológico de enfermedades arbovirales: revisión estructurada de literatura

Introducción: Los métodos de aprendizaje automático permiten manejar datos estructurados y no estructurados para construir modelos predictivos y apoyar la toma de decisiones. Objetivo: Identificar los métodos de aprendizaje automático aplicados para predecir el comportamiento epidemiológico de enfermedades arbovirales utilizando datos de vigilancia epidemiológica. Metodología: Se realizó búsqueda en EMBASE y PubMed, análisis bibliométrico y síntesis de la información. Resultados: Se seleccionaron 41 documentos, todos publicados en la última década. La palabra clave más frecuente fue dengue. La mayoría de los autores (88,3 %) participó en un artículo de investigación. Se encontraron 16 métodos de aprendizaje automático, el más frecuente fue Red Neuronal Artificial, seguido de Máquinas de Vectores de Soporte. Conclusiones: En la última década se incrementó la publicación de trabajos que pretenden predecir el comportamiento epidemiológico de arbovirosis por medio de diversos métodos de aprendizaje automático que incorporan series de tiempo de los casos, variables climatológicas, y otras fuentes de información de datos abiertos.

Introduction: Machine learning methods allow to manipulate structured and unstructured data to build predictive models and support decision-making. Objective: To identify machine learning methods applied to predict the epidemiological behavior of vector-borne diseases using epidemiological surveillance data. Methodology: A literature search in EMBASE and PubMed, bibliometric analysis, and information synthesis were performed. Results: A total of 41 papers were selected, all of them were published in the last decade. The most frequent keyword was dengue. Most authors (88.3 %) participated in a research article. Sixteen machine learning methods were found, the most frequent being Artificial Neural Network, followed by Support Vector Machines. Conclusions: In the last decade there has been an increase in the number of articles that aim to predict the epidemiological behavior of vector-borne diseases using by means of various machine learning methods that incorporate time series of cases, climatological variables, and other sources of open data information.

Tomografía fotoacústica y Deep Learning en aplicacionesmédicas

En las últimas décadas, las imágenes fotoacústicas han demostrado su eficacia en el apoyo al diagnóstico de algunas enfermedades, así como en la investigación médica, ya que a través de ellas es posible obtener información del cuerpo humano con características específicas y profundidad de penetración, desde 1 cm hasta 6 cm dependiendo en gran medida del tejido estudiado, además de una buena resolución. Las imágenes fotoacústicas son comparativamente jóvenes y emergentes y prometen mediciones en tiempo real, con procedimientos no invasivos y libres de radiación. Por otro lado, aplicar Deep Learning a imágenes fotoacústicas permite gestionar datos y transformarlos en información útil que genere conocimiento. Estas aplicaciones poseen ventajas únicas que facilitan la aplicación clínica. Se considera que con estas técnicas se pueden proporcionar diagnósticos médicos confiables. Es por eso que el objetivo de este artículo es proporcionar un panorama general de los casos donde se combina el Deep Learning con técnicas fotoacústicas.

In recent decades, photoacoustic imaging has proven its effectiveness in supporting the diagnosis of some diseases as well as in medical research, since through them it is possible to obtain information of the human body with specific characteristics and depth of penetration, from 1 cm to 6 cm depending largely on the tissue studied, in addition to a good resolution. Photoacoustic imaging is comparatively young and emerging and promises real-time measurements, with non-invasive and radiation-free procedures. On the other hand, applying Deep Learning to photoacoustic images allows managing data and transforming them into useful information that generates knowledge. These applications have unique advantages that facilitate clinical application. It may be possible with these techniques to provide reliable medical diagnoses. That is why the aim of this article is to provide an overview of cases combining Deep Learning with photoacoustic techniques.

Advances in diffuse glial tumors diagnosis / Avanços no diagnóstico dos gliomas difusos

Abstract In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

Resumo Nas últimas décadas, houve avanços significativos no diagnóstico de gliomas difusos, impulsionados pela integração de novas tecnologias. Esses avanços aprofundaram nossa compreensão da oncogênese tumoral, permitindo uma estratificação mais refinada do comportamento biológico dessas neoplasias. Esse progresso culminou na quinta edição da classificação da OMS de tumores do sistema nervoso central (SNC) em 2021. Esta revisão abrangente tem como objetivo elucidar esses avanços de forma multidisciplinar, no contexto da nova classificação. Este artigo irá explorar a patologia morfológica e as técnicas moleculares/genéticas (imuno-histoquímica, sequenciamento genético e perfil de metilação), que são fundamentais no diagnóstico, além da correlação dos radiofenótipos da neuroimagem estrutural com a patologia e a genética. Aborda sucintamente a utilidade da tractografia e da neuroimagem funcional no planejamento cirúrgico. Destacaremos o valor de outras técnicas de imagem funcional, como ressonância magnética de perfusão, espectroscopia e medicina nuclear, na distinção entre a progressão do tumor e as alterações relacionadas ao tratamento. Discutiremos as vantagens das diferentes técnicas de diagnóstico na classificação desses tumores, bem como suas limitações em termos de disponibilidade e utilização. Além disso, os crescentes avanços no processamento de dados, inteligência artificial, radiômica e radiogenômica têm grande potencial e podem em breve exercer uma influência substancial no diagnóstico de gliomas. Essas tecnologias inovadoras têm o potencial de revolucionar nossa abordagem a esses tumores. Em última análise, esta revisão destaca a importância fundamental da colaboração multidisciplinar na utilização dos recentes avanços diagnósticos, com a esperança de traduzi-los em uma melhor qualidade de vida e uma maior sobrevida.

Desarrollo de un modelo por inteligencia artificial con hemodinamia no invasiva para predecir preeclampsia en embarazos de alto riesgo / Artificial Intelligence Modeling with Non-Invasive Hemodynamics to Predict Preeclampsia in High-Risk Pregnancy

RESUMEN Introducción: la preeclampsia (PE) es la principal causa de morbimortalidad materno-fetal en nuestro país. Alteraciones hemodinámicas precoces durante el embarazo podrían predecir la evolución a PE. El machine learning (ML) permite el hallazgo de patrones ocultos que podrían detectar precozmente el desarrollo de PE. Objetivos: desarrollar un árbol de clasificación con variables de hemodinamia no invasiva para predecir precozmente desarrollo de PE. Material y métodos: estudio observacional prospectivo con embarazadas de alto riesgo (n=1155) derivadas del servicio de Obstetricia desde enero 2016 a octubre 2022 para el muestreo de entrenamiento por ML con árbol de clasificación j48. Se seleccionaron 112 embarazadas entre semanas 10 a 16, sin tratamiento farmacológico y que completaron el seguimiento con el término de su embarazo con evento final combinado (PE): preeclampsia, eclampsia y síndrome HELLP. Se evaluaron simultáneamente con cardiografía de impedancia y velocidad de onda del pulso y con monitoreo ambulatorio de presión arterial de 24 hs (MAPA). Resultados: presentaron PE 17 pacientes (15,18%). Se generó un árbol de clasificación predictivo con las siguientes variables: índice de complacencia arterial (ICA), índice cardíaco (IC), índice de trabajo sistólico (ITS), cociente de tiempos eyectivos (CTE), índice de Heather (IH). Se clasificaron correctamente el 93,75%; coeficiente Kappa 0,70, valor predictivo positivo (VPP) 0,94 y negativo (VPN) 0,35. Precisión 0,94, área bajo la curva ROC 0,93. Conclusión: las variables ICA, IC, ITS, CTE e IH predijeron en nuestra muestra el desarrollo de PE con excelente discriminación y precisión, de forma precoz, no invasiva, segura y con bajo costo.

ABSTRACT Background: Preeclampsia (PE) is the main cause of maternal-fetal morbidity and mortality in our country. Early hemodynamic changes during pregnancy could predict progression to PE. Machine learning (ML) enables the discovery of hidden patterns that could early detect PE development. Objectives: The aim of this study was to build a classification tree with non-invasive hemodynamic variables for the early prediction of PE occurrence. Results: Seventeen patients (15.18%) presented PE. A predictive classification tree was generated with arterial compliance index (ACI), cardiac index (CI), cardiac work index (CWI), ejective time ratio (ETR), and Heather index (HI). A total of 93.75% patients were correctly classified (Kappa 0.70, positive predictive value 0.94 and negative predictive value 0.35; accuracy 0.94, and area under the ROC curve 0.93). Conclusion: ACI, CI, CWI, ETR and HI variables predicted the early development of PE in our sample with excellent discrimination and accuracy, non-invasively, safely and at low cost.

Herramientas de corte para optimizar parámetros de clasificación de especies maderables con redes neuronales convolucionales / Cutting tools to optimize classification parameters of timber species with convolutional neural networks

Introducción: La gran diversidad de especies maderables tropicales demanda el desarrollo de nuevas tecnologías de identificación con base en sus patrones o características anatómicas. La aplicación de redes neuronales convolucionales (CNN) para el reconocimiento de especies maderables tropicales se ha incrementado en los últimos años por sus resultados prometedores. Objetivo: Evaluamos la calidad de las imágenes macroscópicas con tres herramientas de corte para mejorar la visualización y distinción de las características anatómicas en el entrenamiento del modelo CNN. Métodos: Recolectamos las muestras entre el 2020 y 2021 en áreas de explotación forestal y aserraderos de Selva Central, Perú. Luego, las dimensionamos y, previo a la identificación botánica y anatómica, las cortamos en secciones transversales. Generamos una base de datos de imágenes macroscópicas de la sección transversal de la madera, a través del corte, con tres herramientas para ver su rendimiento en el laboratorio, campo y puesto de control. Resultados: Usamos tres herramientas de corte para obtener una alta calidad de imágenes transversales de la madera; obtuvimos 3 750 imágenes macroscópicas con un microscopio portátil que corresponden a 25 especies maderables. El cuchillo ''Tramontina'' es duradero, pero pierde el filo con facilidad y se necesita una herramienta para afilar, el cúter retráctil ''Pretul'' es adecuado para madera suave y dura en muestras pequeñas de laboratorio; el cuchillo ''Ubermann'' es apropiado para el campo, laboratorio y puesto de control, porque tiene una envoltura duradera y láminas intercambiables en caso de pérdida de filo. Conclusiones: La calidad de las imágenes es decisiva en la clasificación de especies maderables, porque permite una mejor visualización y distinción de las características anatómicas en el entrenamiento con los modelos de red neuronal convolucional EfficientNet B0 y Custom Vision, lo cual se evidenció en las métricas de precisión.

Introduction: The great diversity of tropical timber species demands the development of new technologies capable of identifying them based on their patterns or anatomical characteristics. The application of convolutional neural networks (CNN) for the recognition of tropical timber species has increased in recent years due to the promising results of CNNs. Objective: To evaluate the quality of macroscopic images with three cutting tools to improve the visualization and distinction of anatomical features in the CNN model training. Methods: Samples were collected from 2020 to 2021 in areas of logging and sawmills in the Central Jungle, Peru. They were later sized and, after botanical and anatomical identification, cut in cross sections. A database of macroscopic images of the cross-section of wood was generated through cutting with three different tools and observing its performance in the laboratory, field, and checkpoint. Results: Using three cutting tools, we obtained high quality images of the cross section of wood; 3 750 macroscopic images were obtained with a portable microscope and correspond to 25 timber species. We found the ''Tramontina'' knife to be durable, however, it loses its edge easily and requires a sharpening tool, the ''Pretul'' retractable cutter is suitable for cutting soft and hard wood in small laboratory samples and finally the ''Ubermann'' knife is suitable for use in the field, laboratory, and checkpoint, because it has a durable sheath and interchangeable blades in case of dullness. Conclusion: The quality of the images is decisive in the classification of timber species, because it allows a better visualization and distinction of the anatomical characteristics in training with the EfficientNet B0 and Custom Vision convolutional neural network models, which was evidenced in the precision metrics.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal / Desenvolvimento de rede neural convolucional para o diagnóstico radiográfico de osteoartrite dos joelhos no ELSA-Brasil Musculoesquelético

Abstract Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results: The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion: The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.

Resumo Objetivo: Desenvolver um modelo computacional - rede neural convolucional (RNC) - treinado com radiografias da linha de base do Estudo Longitudinal de Saúde do Adulto Musculoesquelético (ELSA-Brasil Musculoesquelético), para a classificação automática de osteoartrite dos joelhos. Materiais e Métodos: Trata-se de um estudo transversal abrangendo todos os exames da linha de base do ELSA-Brasil Musculoesquelético (5.660 radiografias dos joelhos em incidência posteroanterior). Os exames foram interpretados por médico radiologista com treinamento específico e calibração previamente publicada. Resultados: A RNC desenvolvida apresentou área sob a curva característica de operação do receptor de 0,866 (IC 95%: 0,842-0,882). O modelo pode ser calibrado para alcançar, não simultaneamente, valores máximos de 0,907 para acurácia, 0,938 para sensibilidade e 0,994 para especificidade. Conclusão: A RNC desenvolvida pode ser utilizada como ferramenta de triagem, reduzindo o número total de exames avaliados pelos radiologistas do estudo, e/ou como ferramenta de segunda leitura, contribuindo com a redução de possíveis erros de interpretação.

Validation of a deep learning algorithm for bone age estimation among patients in the city of São Paulo, Brazil / Validação de algoritmo de aprendizado profundo para detecção da idade óssea em pacientes de São Paulo, Brasil

Abstract Objective: To validate a deep learning (DL) model for bone age estimation in individuals in the city of São Paulo, comparing it with the Greulich and Pyle method. Materials and Methods: This was a cross-sectional study of hand and wrist radiographs obtained for the determination of bone age. The manual analysis was performed by an experienced radiologist. The model used was based on a convolutional neural network that placed third in the 2017 Radiological Society of North America challenge. The mean absolute error (MAE) and the root-mean-square error (RMSE) were calculated for the model versus the radiologist, with comparisons by sex, race, and age. Results: The sample comprised 714 examinations. There was a correlation between the two methods, with a coefficient of determination of 0.94. The MAE of the predictions was 7.68 months, and the RMSE was 10.27 months. There were no statistically significant differences between sexes or among races (p > 0.05). The algorithm overestimated bone age in younger individuals (p = 0.001). Conclusion: Our DL algorithm demonstrated potential for estimating bone age in individuals in the city of São Paulo, regardless of sex and race. However, improvements are needed, particularly in relation to its use in younger patients.

Resumo Objetivo: Validar em indivíduos paulistas um modelo de aprendizado profundo (deep learning - DL) para estimativa da idade óssea, comparando-o com o método de Greulich e Pyle. Materiais e Métodos: Estudo transversal com radiografias de mão e punho para idade óssea. A análise manual foi feita por um radiologista experiente. Foi usado um modelo baseado em uma rede neural convolucional que ficou em terceiro lugar no desafio de 2017 da Radiological Society of North America. Calcularam-se o erro médio absoluto (mean absolute error - MAE) e a raiz do erro médio quadrado (root mean-square error - RMSE) do modelo contra o radiologista, com comparações entre sexo, etnia e idade. Resultados: A amostra compreendia 714 exames. Houve correlação entre ambos os métodos com coeficiente de determinação de 0,94. O MAE das predições foi 7,68 meses e a RMSE foi 10,27 meses. Não houve diferenças estatisticamente significantes entre sexos ou raças (p > 0,05). O algoritmo superestimou a idade óssea nos mais jovens (p = 0,001). Conclusão: O nosso algoritmo de DL demonstrou potencial para estimar a idade óssea em indivíduos paulistas, independentemente do sexo e da raça. Entretanto, há necessidade de aprimoramentos, particularmente em pacientes mais jovens.

Aprendizaje automático aplicado a la predicción de diabetes mellitus, utilizando información socioeconómica y ambiental de usuarios del sistema de salud

Objetivo: Se propuso aplicar modelos basados en técnicas de aprendizaje automático como apoyo para el diagnóstico temprano de la diabetes mellitus, utilizando variables de datos ambientales, sociales, económicos y sanitarios, sin la dependencia de la toma de muestras clínicas. Metodología: Se utilizaron datos de 10 889 usuarios afiliados al régimen subsidiado de salud de la zona suroccidental en Colombia, diagnosticados con hipertensión y agrupados en usuarios sin (74,3 %) y con (25,7 %) diabetes mellitus. Se entrenaron modelos supervisados utilizando k vecinos más cercanos, árboles de decisión y bosques aleatorios, así como modelos basados en ensambles, aplicados a la base de datos antes y después de balancear el número de casos en cada grupo de diagnóstico. Se evalúo el rendimiento de los algoritmos mediante la división de la base de datos en datos de entreno y de prueba (70/30, respectivamente), y se utilizaron métricas de exactitud, sensibilidad, especificidad y área bajo la curva. Resultados: Los valores de sensibilidad aumentaron considerablemente al utilizar datos balanceados, pasando de valores máximos del 17,1 % (datos sin balancear) a valores de hasta 57,4 % (datos balanceados). El valor más alto de área bajo la curva (0,61) fue obtenido con los modelos de ensambles, al aplicar un balance en el número de datos por cada grupo y al codificar las variables categóricas. Las variables de mayor peso estuvieron asociadas con aspectos hereditarios (24,65 %) y con el grupo étnico (5.59 %), además de la dificultad visual, el bajo consumo de agua, una dieta baja en frutas y verduras, y el consumo de sal y azúcar. Conclusiones: Aunque los modelos predictivos, utilizando información socioeconómica y ambiental de las personas, surgen como una herramienta para el diagnóstico temprano de la diabetes mellitus, estos aún deben ser mejorados en su capacidad predictiva.

Objective: The objective was to apply models based on machine learning techniques to support the early diagnosis of diabetes mellitus, using environmental, social, economic and health data variables, without dependence on clinical sample collection. Methodology: Data from 10,889 users affiliated with the subsidized health system in the southwestern area of Colombia, diagnosed with hypertension and grouped into users without (74.3%) and with (25.7%) diabetes mellitus, were used. Supervised models were trained using k-nearest neighbors, decision trees, and random forests, as well as ensemble-based models, applied to the database before and after balancing the number of cases in each diagnostic group. The performance of the algorithms was evaluated by dividing the database into training and test data (70/30, respectively), and metrics of accuracy, sensitivity, specificity, and area under the curve were used. Results: Sensitivity values increased significantly when using balanced data, going from maximum values of 17.1% (unbalanced data) to values as high as 57.4% (balanced data). The highest value of area under the curve (0.61) was obtained with the ensemble models, by applying a balance in the amount of data for each group and by coding the categorical variables. The variables with the greatest weight were associated with hereditary aspects (24.65%) and with the ethnic group (5.59%), in addition to visual difficulty, low water consumption, a diet low in fruits and vegetables, and the consumption of salt and sugar. Conclusions: Although predictive models, using people's socioeconomic and environmental information, emerge as a tool for the early diagnosis of diabetes mellitus, their predictive capacity still needs to be improved.

Objetivo: Propôs-se aplicar modelos baseados em técnicas de aprendizagem automática como apoio para o diagnóstico precoce da diabetes mellitus, utilizando variáveis de dados ambientais, sociais, econômicos e sanitários, sem a dependência da coleta de amostras clínicas. Metodologia: Usaram-se dados de 10.889 usuários filiados ao regime subsidiado de saúde da zona sudoeste da Colômbia, diagnosticados com hipertensão e agrupados em usuários sem (74,3%) e com (25,7%) diabetes mellitus. Foram treinados modelos supervisionados utilizando k vizinhos mais próximos, árvores de decisão e florestas aleatórias, assim como modelos baseados em montagens, aplicados à base de dados antes de depois de equilibrar o número de casos em cada grupo de diagnóstico. Avaliou-se o desempenho dos algoritmos por meio da divisão da base de dados de treino e teste (70/30, respectivamente), e utilizaram-se métricas de exatidão, sensibilidade, especificidade e área sob a curva. Resultados: Os valores de sensibilidade aumentaram de maneira significativa ao utilizar dados equilibrados, passando de valores máximos de 17,1% (dados sem equilibrar) a valores de até 57,4% (dados equilibrados). O valor mais elevado de área sob a curva (0,61) foi obtido com os modelos de montagens, ao aplicar um balanço no número de dados por cada grupo e codificar as variáveis categóricas. As variáveis de maior peso estiveram associadas com fatores hereditários (24,65%) e com o grupo étnico (5,59%), além da dificuldade visual, o baixo consumo de água, um regime baixo em frutas e vegetais e o consumo de sal e açúcar. Conclusões: Embora os modelos preditivos, utilizando informação socioeconômica e ambiental das pessoas, surgem como uma ferramenta para o diagnóstico precoce da diabetes mellitus, ainda devem ser melhorados em sua capacidade preditiva.

Sex Determination by the Machine Learning Algorithms Through Using Morphometric Measurements of the Carpal, Metacarpal, and Phalangeal Bones / Determinación del Sexo Mediante Algoritmos de Aprendizaje Automático Mediante el Uso de Mediciones Morfométricas de los Huesos del Carpo, el Metacarpo y las Falanges

SUMMARY: In the study, it was aimed to predict sex from hand measurements using machine learning algorithms (MLA). Measurements were made on MR images of 60 men and 60 women. Determined parameters; hand length (HL), palm length (PL), hand width (HW), wrist width (EBG), metacarpal I length (MIL), metacarpal I width (MIW), metacarpal II length (MIIL), metacarpal II width (MIIW), metacarpal III length (MIIL), metacarpal III width (MIIIW), metacarpal IV length (MIVL), metacarpal IV width (MIVW), metacarpal V length (MVL), metacarpal V width (MVW), phalanx I length (PILL), measured as phalanx II length (PIIL), phalanx III length (PIIL), phalanx IV length (PIVL), phalanx V length (PVL). In addition, the hand index (HI) was calculated. Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN) and Naive Bayes (NB) were used as MLAs. In the study, the KNN algorithm's Accuracy, SEN, F1 and Specificity ratios were determined as 88 %. In this study using MLA, it is understood that the highest accuracy belongs to the KNN algorithm. Except for the hand's MIIW, MIIIW, MIVW, MVW, HI variables, other variables were statistically significant in terms of sex difference.

En el estudio, el objetivo era predecir el sexo a partir de mediciones manuales utilizando algoritmos de aprendizaje automático (MLA). Las mediciones se realizaron en imágenes de RM de 60 hombres y 60 mujeres. Parámetros determinados; longitud de la mano (HL), longitud de la palma (PL), ancho de la mano (HW), ancho de la muñeca (EBG), longitud del metacarpiano I (MIL), ancho del metacarpiano I (MIW), longitud del metacarpiano II (MIIL), ancho del metacarpiano II (MIIW), longitud del metacarpiano III (MIIL), ancho del metacarpiano III (MIIIW), longitud del metacarpiano IV (MIVL), ancho del metacarpiano IV (MIVW), longitud del metacarpiano V (MVL), ancho del metacarpiano V (MVW), longitud de la falange I (PILL), medido como longitud de la falange II (PIIL), longitud de la falange III (PIIL), longitud de la falange IV (PIVL), longitud de la falange V (PVL). Además, se calculó el índice de la mano (HI). Regresión logística (LR), Random Forest (RF), Análisis discriminante lineal (LDA), K-vecino más cercano (KNN) y Naive Bayes (NB) se utilizaron como MLA. En el estudio, las proporciones de precisión, SEN, F1 y especificidad del algoritmo KNN se determinaron en un 88 %. En este estudio que utiliza MLA, se entiende que la mayor precisión pertenece al algoritmo KNN. Excepto por las variables MIIW, MIIIW, MIVW, MVW, HI de la mano, otras variables fueron estadísticamente significativas en términos de diferencia de sexo.

THE NEW ERA OF DENTISTRY: MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Innovative computer techniques are now being utilized not only in academic research but also in commercial dental practice, revolutionizing various areas of dentistry. This digitalization trend is driven by the increasing demands for treatment and diagnosis in the eld. Accurate diagnosis is crucial in dentistry, whether it be in orthodontics, maxillofacial surgery, periodontics, or prosthetics, as it forms the basis for creating effective treatment plans and restoring patients' oral health. While a specialist's expertise plays a vital role in diagnosis and treatment planning, it is susceptible to the inherent risks of human error, given the multifactorial nature of dental conditions. Consequently, there is growing interest in leveraging multi-parametric pattern recognition methods, including statistics, machine learning, and articial intelligence (AI), to enhance clinical decision-making. The introduction of clinical decision support systems (CDSS) and genetic algorithms (GAs) in dental research and clinical practice holds great promise for both healthcare professionals and patients. Extensive work has been undertaken to develop CDSS in dentistry, and this article reviews the latest advancements in this eld.

Systematic Analysis of Artificial Intelligence in Pathology

Introduction: Artificial intelligence (AI) is an emerging modern technology within the health care sectors in the current era and it is the ability of computer software to mimic human judgment. Artificial intelligence (AI)-based modern image analysis methods have significant promise for enhancing the accuracy and efficacy of pathology diagnostic processes as well as for the discovery of new biomarkers. Objectives: In this article, we will discuss Artificial Intelligence, its usage in pathology in various ways such as for screening of various diseases, detection of prognostic markers or biomarkers, and various treatment modalities. Materials and Methods: Data were collected and analyzed from the recently published literature and electronic database searches of Cochrane and included the articles the year 2017 to 2021 by reading the title and the abstract. Artificial intelligence (AI), has a lot of potential for aiding in diagnosis with the advancement of information technology. For this purpose, few machine learning algorithms have been created to date. Given their capacity to evaluate complicated data in a quantitative and standardised manner to further improve the precision and scope of diagnoses, artificial intelligence (AI) or machine learning technologies hold great promise for the field of pathology. Conclusion: The application of Artificial Intelligence tools in pathology has sharply increased in this era and it is anticipated to revolutionize the pathology field in the years ahead and can change the way the field of pathology is managed and make them not only more systematic but also effective in meeting the needs of the current age of precision medicine.

The Effectiveness of Machine Learning Systems' Accuracy in Predicting Heart Stroke Using Socio-Demographic and Risk Factors - A Comparative Analysis of Various Models

Background: Cardiologists can more appropriately classify patients' cardiovascular diseases by executing ac-curate diagnoses and prognoses, enabling them to administer the most appropriate care. Due to machine learning's ability to identify patterns in data, its applications in the medical sector have grown. Diagnosticians can avoid making mistakes by classifying the incidence of cardiovascular illness using machine learning. To lower the fatality rate brought on by cardiovascular disorders, our research developed a model that can cor-rectly forecast these conditions.Methods: This study emphasized a model that can correctly forecast cardiovascular illnesses to lower the death rate brought on by these conditions. We deployed four well-known classification machine learning al-gorithms like K nearest Neighbour, Logistic Regression, Artificial Neural network, and Decision tree. Results: The proposed models were evaluated by their performance matrices. However logistic regression performed high accuracy concerning AUC (0.955) 95% CI (0.872-0.965) followed by the artificial neural net-work. AUC (0.864) 95% CI (0.826-0.912). Conclusion: Individuals' risk of having a cardiac event may be predicted using machine learning, and those who are most at risk can be identified. Predictive models may be developed via machine learning to pinpoint those who have a high chance of suffering a heart attack