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1.
Eur. j. psychiatry ; 38(1): [100227], Jan.-Mar. 2024. graf
Artículo en Inglés | IBECS | ID: ibc-229233

RESUMEN

Background and objectives Suicide is a major public health concern, media can influence its awareness, contagion, and prevention. In this study, we evaluated the relationship between the COVID-19 pandemic and suicide in media coverage through Natural Language Processing analysis (NPL). Methods To study how suicide is depicted in news media, Artificial Intelligence and Big Data techniques were used to analyze news and tweets, to extract or classify the topic to which they belonged. Results A granger causality analysis showed with significant p-value that an increase in covid news at the beginning of the pandemic explains a later rise in suicide-related news. An analysis based on correlation and structural causal models show a strong relationship between the appearance of subjects “health” and “covid”, and also between “covid” and “suicide”. Conclusions Our analysis also uncovers that the inclusion of suicide-related news in the category health has grown since the outbreak of the pandemic. The COVID-19 pandemic has posed an inflection point in the way suicide-related news are reported. Our study found that the increased media attention on suicide during the COVID-19 pandemic may indicate rising social awareness of suicide and mental health, which could lead to the development of new prevention tools. (AU)


Asunto(s)
Humanos , Salud Pública , Suicidio , Macrodatos , Inteligencia Artificial , Aprendizaje Automático , Medios de Comunicación , Red Social , Procesamiento Automatizado de Datos
2.
Rev. esp. med. legal ; 50(1): 29-39, Ene.-Mar. 2024. tab, graf
Artículo en Inglés, Español | IBECS | ID: ibc-229295

RESUMEN

Introducción/objetivos la violencia contra la mujer sigue siendo un grave problema social y de salud a pesar de las medidas puestas en marcha en los últimos años. La exploración de las víctimas por el médico forense en los juzgados es de gran interés puesto que recibe información relacionada no solo con la agresión, sino también de su entorno social, familiar y económico. El objetivo es utilizar dicha información para identificar grupos de riesgo y mejorar/obtener las medidas necesarias. Material y métodos en este trabajo, el forense ha recogido, durante 8 años, una toma abundante de datos sobre las víctimas exploradas en L’Hospitalet de Llobregat. La muestra incluye 1.622 casos de mujeres víctimas de violencia de género. Se realiza un estudio descriptivo poblacional y de las lesiones. Resultados se exponen las principales variables estudiadas tanto socioeconómicas como referentes a la agresión en sí. Se trabaja también con base en la reentrada de las víctimas o repetición de las agresiones (revictimización), que son el 10,9% de la muestra. Finalmente, se presentan los resultados obtenidos tras aplicar técnicas de inteligencia artificial, en este caso, árboles de clasificación CaRT. Conclusiones con los resultados obtenidos concluimos que el tratamiento de la información recogida y sistematizada de la intervención médico-forense permite una mejor comprensión de la violencia sobre la mujer, de la que podemos extraer sugerencias sobre la adopción de medidas de atención y soporte a las víctimas y a los colectivos más vulnerables, así como sobre los recursos administrativos y la optimización de programas de prevención. (AU)


Introduction/objectives Violence against women is still a serious social and health problem, despite the measures implemented in recent years. The examination of the victims by the forensic doctor in the courts is of great interest since it provides information related not only to the aggression, but also to their social, family and economic environment. The objective is to use this information to identify groups at risk and improve/implement the necessary measures. Material and methods In this work, the forensic has collected, for eight years, abundant data on the victims examined in L'Hospitalet de Llobregat. The sample includes 1,622 cases of women who have been victims of gender violence. A descriptive study of the population and of the lesions has been carried out. Results The paper presents the main variables studied, both socioeconomic and referring to the aggression itself. This study also analyzes the reentry of the victims, the repetition of aggressions (revictimization), which are 10.9% of the sample. Finally, the results obtained after applying artificial intelligence techniques -in this case, CaRT classification trees- are presented. Conclusions With the results obtained, we conclude that the treatment of the information collected and systematized from the medical-forensic intervention allows a better understanding of Violence Against Women, from which we can extract suggestions on the adoption of care and support measures for the victims and the most vulnerable groups, as well as administrative resources and the optimization of prevention programs. (AU)


Asunto(s)
Humanos , Femenino , Adolescente , Adulto Joven , Adulto , Persona de Mediana Edad , Anciano , Violencia de Género/etnología , Violencia de Género/prevención & control , Inteligencia Artificial , Violencia contra la Mujer , Análisis de Datos , España
4.
Rev. bioét. derecho ; (60): 19-34, Mar. 2024. tab
Artículo en Español | IBECS | ID: ibc-230470

RESUMEN

La sociedad contemporánea vive la revolución digital y la necesidad de reflexionar sobre la interacción entre los seres humanos y las tecnologías digitales. El auge de las tecnologías de inteligencia artificial y la algoritmización social ha planteado interrogantes sobre la indispensabilidad de la supervisión y el análisis ético de la información y los datos en Internet. Así como la necesidad de verificar la influencia de las plataformas digitales en el ejercicio de la ciudadanía. La bioética posibilita la investigación sobre los principios que se deben respetar en una sociedad democrática y digital. Resaltamos los principios de responsabilidad social y no discriminación con la intención de que los beneficios del uso tecnológico promuevan el bienestar y la calidad de vida de los menos favorecidos. Su objetivo es garantizar la supervivencia de la especie humana y la mejora de la protección de la vida de todos los seres vivos, animales y plantas. La reflexión bioética sobre el uso de la inteligencia artificial podría establecer la brújula moral que oriente el análisis de los conflictos éticos y la defensa de que a todos los seres humanos se les debe garantizar la igualdad de oportunidades y las condiciones para realizar plenamente su proyecto de vida.(AU)


La societat contemporània viu la revolució digital i la necessitat de reflexionar sobre la interacció entre els éssers humans i les tecnologies digitals. L'auge de les tecnologies d'intel·ligència artificial i la algoritmització social ha plantejat interrogants sobre la indispensabilitat de la supervisió i l'anàlisi ètic de la informació i les dades a Internet. Així com la necessitat de verificar la influència de les plataformes digitals en l'exercici de la ciutadania. La bioètica possibilita la recerca sobre els principis que es deuen respectar en una societat democràtica i digital. Destaquem els principis de responsabilitat social i no discriminació amb la intenció que els beneficis de l'ús tecnològic promoguin el benestar i la qualitat de vida dels menys afavorits. El seu objectiu és garantir la supervivència de l'espècie humana i la millora de la protecció de la vida de tots els éssers vius, animals i plantes. La reflexió bioètica sobre l'ús de la intel·ligència artificial podria establir la brúixola moral que orienti l'anàlisi dels conflictes ètics i la defensa que a tots els éssers humans se'ls ha de garantir la igualtat d'oportunitats i les condicionsper realitzar plenament el seu projecte de vida.(AU)


Contemporary society is going through the digital revolution and the need to reflect on the interaction between human beings and digital technologies. The rise of artificial intelligence technologies and social algorithmization has raised questions about the need for ethical monitoring and analysis of information and data on the Internet. As well as the need to verify the influence of digital platforms in the exercise of citizenship. Bioethics enables research on the principles that must be respected in a democratic and digital society. We highlight the principles of social responsibility and non-discrimination with the intention that the benefits of technological use promote the well-being and quality of life of the less favored. Its objective is to guarantee the survival of the human species and the improvement of the protection of the life of all living beings, animals, and plants. Bioethical reflection on the use of artificial intelligence could establish the moral compass that guides the analysis of ethical conflicts and the defense that all human beings must be guaranteed equal opportunities and the conditions to fully carry out their project of life.(AU)


Asunto(s)
Humanos , Masculino , Femenino , Inteligencia Artificial , Bioética , Discusiones Bioéticas , Ética en Investigación
5.
Med. clín (Ed. impr.) ; 162(4): 163-169, Feb. 2024. tab, ilus, graf
Artículo en Inglés | IBECS | ID: ibc-230572

RESUMEN

Objectives: COVID-19, caused by SARS-CoV-2, has spread around the world since 2019. In severe cases, COVID-19 can lead to hospitalization and death. Systemic arterial hypertension and other comorbidities are associated with serious COVID-19 infection. Literature is unclear whether antihypertensive therapy with angiotensin receptor blockers (ARBs) and angiotensin converting enzyme (ACE) inhibitors affect COVID-19 outcomes. We aim to assess whether ACEI/ARB therapy is a risk factor for worse respiratory outcomes related to COVID-19 in hospitalized patients. Methods: Retrospective study enrolling admitted COVID-19-diagnosed patients by RT-PCR at the Hospital Geral de Fortaleza, Brazil, during 2021. Patient medical records, sociodemographic, and clinical data were analyzed. Chest CT images were analyzed using CAD4COVID-CT/Thirona™ software. Results: A total of 294 patients took part in the study. A cut-off point of 66% of pulmonary involvement was found by ROC curve, with patients having higher risk of death and intubation and lower 60-day survival. Advanced age (RR 1.025, P=0.001) and intubation (RR 16.747, P<0.001) were significantly associated with a higher risk of death. Advanced age (RR 1.023, P=0.001) and the use of noninvasive ventilation (RR 1.548, P=0.037) were associated with a higher risk of intubation. Lung involvement (>66%) increased the risk of death by almost 2.5-fold (RR 2.439, P<0.001) and by more than 2.3-fold the risk of intubation (RR 2.317, P<0.001). Conclusions: Altogether, our findings suggest that ACEI or ARB therapy does not affect the risk of death and disease course during hospitalization.(AU)


Objetivos: La COVID-19, causada por el SARS-CoV-2, se ha extendido por todo el mundo desde 2019. En casos graves, la COVID-19 puede provocar hospitalización y muerte. La hipertensión arterial sistémica y otras comorbilidades se asocian con una infección grave por COVID-19. La literatura no está clara si la terapia antihipertensiva con bloqueadores de los receptores de angiotensina (BRA) e inhibidores de la enzima convertidora de angiotensina (ECA) afecta los resultados de la COVID-19. Nuestro objetivo fue evaluar si la terapia BRA/ECA es un factor de riesgo de peores resultados respiratorios relacionados con COVID-19 en pacientes hospitalizados. Métodos: Estudio retrospectivo que incluyó pacientes ingresados con diagnóstico de COVID-19 mediante RT-PCR en el Hospital General de Fortaleza, Brasil, durante 2021. Se analizaron las historias clínicas de los pacientes, datos sociodemográficos y clínicos. Las imágenes de TC de tórax se analizaron utilizando el software CAD4COVID-CT/ThironaTM. Resultados: Participaron en el estudio un total de 294 pacientes. Mediante curva ROC se encontró un punto de corte del 66% de afectación pulmonar, teniendo los pacientes mayor riesgo de muerte e intubación y menor supervivencia a 60 días. La edad avanzada (RR 1,025; P=0,001) y la intubación (RR 16,747; P<0,001) se asociaron significativamente con un mayor riesgo de muerte. La edad avanzada (RR 1,023; P=0,001) y el uso de ventilación no invasiva (RR 1,548; P=0,037) se asociaron con un mayor riesgo de intubación. La afectación pulmonar (>66%) aumentó el riesgo de muerte casi 2,5 veces (RR 2,439; P<0,001) y más de 2,3 veces el riesgo de intubación (RR 2,317, P<0,001). Conclusiones: Se concluyó que el tratamiento con BRA o ECA no afecta el riesgo de muerte y el curso de la enfermedad durante la hospitalización.(AU)


Asunto(s)
Humanos , Masculino , Femenino , /diagnóstico , Bloqueadores del Receptor Tipo 1 de Angiotensina II/uso terapéutico , Inhibidores de la Enzima Convertidora de Angiotensina/uso terapéutico , Hipertensión , Comorbilidad , /epidemiología , Medicina Clínica , Estudios Retrospectivos , Brasil , Antihipertensivos/efectos adversos , Inteligencia Artificial
6.
J Korean Med Sci ; 39(5): e56, 2024 Feb 05.
Artículo en Inglés | MEDLINE | ID: mdl-38317452

RESUMEN

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR. METHODS: We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy. RESULTS: ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68. CONCLUSION: The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.


Asunto(s)
Fibrilación Atrial , Aprendizaje Profundo , Humanos , Fibrilación Atrial/diagnóstico , Inteligencia Artificial , Redes Neurales de la Computación , Electrocardiografía/métodos
7.
PLoS One ; 19(2): e0297578, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38319912

RESUMEN

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.


Asunto(s)
Enfermedades Transmisibles , Aprendizaje Profundo , Neumonía , Humanos , Inteligencia Artificial , Tomografía Computarizada por Rayos X/métodos , Computadores , Comunicación
8.
PLoS One ; 19(2): e0292272, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38319939

RESUMEN

Satellite survey is widely used for archaeological site discovery, but the efficacy of the method has received little systematic investigation. In this analysis, twelve study participants of different experience levels performed an unstructured remote survey of 197 km2 in the Sama and Moquegua valleys of south central Peru where previous pedestrian surveys recorded 546 archaeological sites. Results indicate an average site discovery rate of 9.3% (0-18%, 95% range). The most experienced participants detect up to 20% (17-22%) of known archaeological sites. These detection rates can be used to derive reliable site frequency estimates on the Andean coast, which can be used in planning and budgeting for field efforts and estimating demographic patterns at large spatial scales that are difficult to achieve through pedestrian survey. More generally, this analysis offers a method for deriving correction terms specific to other parts of the world. Additionally, the results can serve as a baseline for evaluating the effectiveness of emerging artificial intelligence routines for archaeological site detection.


Asunto(s)
Arqueología , Inteligencia Artificial , Humanos , Perú , Arqueología/métodos
9.
Sci Rep ; 14(1): 3009, 2024 02 06.
Artículo en Inglés | MEDLINE | ID: mdl-38321110

RESUMEN

Currently, the classification of bone mineral density (BMD) in many research studies remains rather broad, often neglecting localized changes in BMD. This study aims to explore the correlation between peri-implant BMD and primary implant stability using a new artificial intelligence (AI)-based BMD grading system. 49 patients who received dental implant treatment at the Affiliated Hospital of Stomatology of Fujian Medical University were included. Recorded the implant stability quotient (ISQ) after implantation and the insertion torque value (ITV). A new AI-based BMD grading system was used to obtain the distribution of BMD in implant site, and the bone mineral density coefficients (BMDC) of the coronal, middle, apical, and total of the 1 mm site outside the implant were calculated by model overlap and image overlap technology. Our objective was to investigate the relationship between primary implant stability and BMDC values obtained from the new AI-based BMD grading system. There was a significant positive correlation between BMDC and ISQ value in the coronal, middle, and total of the implant (P < 0.05). However, there was no significant correlation between BMDC and ISQ values in the apical (P > 0.05). Furthermore, BMDC was notably higher at implant sites with greater ITV (P < 0.05). BMDC calculated from the new AI-based BMD grading system could more accurately present the BMD distribution in the intended implant site, thereby providing a dependable benchmark for predicting primary implant stability.


Asunto(s)
Densidad Ósea , Implantes Dentales , Humanos , Inteligencia Artificial , Prótesis e Implantes , Torque , Benchmarking
10.
Eur Radiol Exp ; 8(1): 17, 2024 Feb 07.
Artículo en Inglés | MEDLINE | ID: mdl-38321340

RESUMEN

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.


Asunto(s)
Aprendizaje Profundo , Radiología , Humanos , Inteligencia Artificial , Tomografía de Emisión de Positrones , Poder Psicológico
11.
BMC Bioinformatics ; 25(1): 61, 2024 Feb 07.
Artículo en Inglés | MEDLINE | ID: mdl-38321434

RESUMEN

BACKGROUND: The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. RESULTS: We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. CONCLUSIONS: This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.


Asunto(s)
Arabidopsis , Inteligencia Artificial , Teorema de Bayes , Escherichia coli , Staphylococcus aureus , Programas Informáticos , Algoritmos , Secuenciación de Nucleótidos de Alto Rendimiento , Aprendizaje Automático , Análisis de Secuencia de ADN
12.
J Int Med Res ; 52(2): 3000605241230033, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-38321885

RESUMEN

OBJECTIVES: To apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method. METHODS: For this retrospective, observational study, patients with pulmonary nodules 5-30 mm in diameter on computed tomography (CT) and who had at least six months follow-up were identified. Two radiologists defined a 'correct' cuboid circumscribing each nodule which was used to judge the success/failure of nodule tracking. An AI algorithm was applied in which a U-net type neural network model was trained to predict the deformation vector field between two examinations. When the estimated position was within a defined cuboid, the AI algorithm was judged a success. RESULTS: In total, 49 lung nodules in 40 patients, with a total of 368 follow-up CT examinations were examined. The success rate for each time evaluation was 94% (345/368) and for 'nodule-by-nodule evaluation' was 78% (38/49). Reasons for a decrease in success rate were related to small nodules and those that decreased in size. CONCLUSION: Automatic tracking of lung nodules is highly feasible.


Asunto(s)
Neoplasias Pulmonares , Nódulo Pulmonar Solitario , Humanos , Inteligencia Artificial , Estudios Retrospectivos , Algoritmos , Tomografía Computarizada por Rayos X/métodos
13.
Nat Commun ; 15(1): 1136, 2024 Feb 07.
Artículo en Inglés | MEDLINE | ID: mdl-38326316

RESUMEN

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.


Asunto(s)
Inteligencia Artificial , Lípidos de la Membrana , Membrana Celular , Simulación de Dinámica Molecular , Aprendizaje Automático
14.
Nat Commun ; 15(1): 1131, 2024 Feb 07.
Artículo en Inglés | MEDLINE | ID: mdl-38326351

RESUMEN

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.


Asunto(s)
Inteligencia Artificial , Neoplasias Hepáticas , Humanos , Estudios Retrospectivos , Radiólogos , Neoplasias Hepáticas/diagnóstico por imagen
15.
Sci Rep ; 14(1): 3123, 2024 02 07.
Artículo en Inglés | MEDLINE | ID: mdl-38326488

RESUMEN

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model's performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.


Asunto(s)
Enfermedades Cardiovasculares , Cardiopatías , Ruidos Cardíacos , Humanos , Inteligencia Artificial , Redes Neurales de la Computación , Cardiopatías/diagnóstico , Aprendizaje Automático
16.
Eur Radiol Exp ; 8(1): 10, 2024 Feb 08.
Artículo en Inglés | MEDLINE | ID: mdl-38326501

RESUMEN

BACKGROUND: Pretraining labeled datasets, like ImageNet, have become a technical standard in advanced medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive labeling process. In this study, we explored if SSL for pretraining on non-medical images can be applied to chest radiographs and how it compares to supervised pretraining on non-medical images and on medical images. METHODS: We utilized a vision transformer and initialized its weights based on the following: (i) SSL pretraining on non-medical images (DINOv2), (ii) supervised learning (SL) pretraining on non-medical images (ImageNet dataset), and (iii) SL pretraining on chest radiographs from the MIMIC-CXR database, the largest labeled public dataset of chest radiographs to date. We tested our approach on over 800,000 chest radiographs from 6 large global datasets, diagnosing more than 20 different imaging findings. Performance was quantified using the area under the receiver operating characteristic curve and evaluated for statistical significance using bootstrapping. RESULTS: SSL pretraining on non-medical images not only outperformed ImageNet-based pretraining (p < 0.001 for all datasets) but, in certain cases, also exceeded SL on the MIMIC-CXR dataset. Our findings suggest that selecting the right pretraining strategy, especially with SSL, can be pivotal for improving diagnostic accuracy of artificial intelligence in medical imaging. CONCLUSIONS: By demonstrating the promise of SSL in chest radiograph analysis, we underline a transformative shift towards more efficient and accurate AI models in medical imaging. RELEVANCE STATEMENT: Self-supervised learning highlights a paradigm shift towards the enhancement of AI-driven accuracy and efficiency in medical imaging. Given its promise, the broader application of self-supervised learning in medical imaging calls for deeper exploration, particularly in contexts where comprehensive annotated datasets are limited.


Asunto(s)
Inteligencia Artificial , Aprendizaje Profundo , Bases de Datos Factuales
17.
Radiology ; 310(2): e230793, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-38319162

RESUMEN

Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.


Asunto(s)
Neoplasias Encefálicas , Glioma , Neoplasias Meníngeas , Meningioma , Adulto , Humanos , Niño , Medios de Contraste , Gadolinio , Fantasía , Inteligencia Artificial , Imagen por Resonancia Magnética , Neoplasias Encefálicas/diagnóstico por imagen , Glioma/diagnóstico por imagen
18.
Ned Tijdschr Geneeskd ; 1682024 Jan 22.
Artículo en Holandés | MEDLINE | ID: mdl-38319310

RESUMEN

In advising the preferred therapy for the individual patient the expected results of the proposed intervention and possible side effects are the most relevant considerations. However, predicting the results of an intervention is difficult, especially when well designed randomized clinical trials (RCT's) are lacking or not conclusive. Artificial intelligence (AI) algorithms based on routine clinical data (real world data) can support clinical decision making, but in daily practice AI is still scarcely used. In this article one large radiotherapy facility and two health insurers describe their joint opinion on the possible role of AI based on real world data as an aid in clinical decision making when evidence from RCT's is not available. The introduction of proton radiotherapy in The Netherlands is being used as case model for AI model based clinical decision making.


Asunto(s)
Algoritmos , Inteligencia Artificial , Humanos , Toma de Decisiones Clínicas , Aseguradoras , Países Bajos
20.
Transl Vis Sci Technol ; 13(2): 1, 2024 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-38300623

RESUMEN

Purpose: Artificial intelligence (AI)-assisted ultra-widefield (UWF) fundus photographic interpretation is beneficial to improve the screening of fundus abnormalities. Therefore we constructed an AI machine-learning approach and performed preliminary training and validation. Methods: We proposed a two-stage deep learning-based framework to detect early retinal peripheral degeneration using UWF images from the Chinese Air Force cadets' medical selection between February 2016 and June 2022. We developed a detection model for the localization of optic disc and macula, which are used to find the peripheral areas. Then we developed six classification models for the screening of various retinal cases. We also compared our proposed framework with two baseline models reported in the literature. The performance of the screening models was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval. Results: A total of 3911 UWF fundus images were used to develop the deep learning model. The external validation included 760 UWF fundus images. The results of comparison study revealed that our proposed framework achieved competitive performance compared to existing baselines while also demonstrating significantly faster inference time. The developed classification models achieved an average AUC of 0.879 on six different retinal cases in the external validation dataset. Conclusions: Our two-stage deep learning-based framework improved the machine learning efficiency of the AI model for fundus images with high resolution and many interference factors by maximizing the retention of valid information and compressing the image file size. Translational Relevance: This machine learning model may become a new paradigm for developing UWF fundus photography AI-assisted diagnosis.


Asunto(s)
Aprendizaje Profundo , Degeneración Retiniana , Adulto Joven , Humanos , Inteligencia Artificial , Retina/diagnóstico por imagen , Fondo de Ojo
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