Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Césped natural “versus” césped híbrido

Cada vez se da una mayor importancia a la influencia que ejercen las superficies para la práctica deportiva sobre aspectos como el rendimiento, el confort y la prevención de lesiones. El fútbol no es una excepción, y se están desarrollando nuevos tipos de césped que tratan de obtener las mejores prestaciones. Un ejemplo es el césped híbrido, formado por una capa de césped artificial porosa y una capa elástica. Ambas capas permiten el paso de las raíces para un correcto crecimiento del césped natural. Para caracterizar este tipo de césped, el Instituto de Biomecánica (IBV) ha realizado un estudio comparativo de un campo de fútbol de césped híbrido con un campo de césped natural de alta calidad

La Alcarria es un hermoso país

Resumen basado en el de la publicaciónEl Colegio Rural Agrupado (CRA) "Santa Lucía", se encuentra ubicado en una zona rural alejada de la capital de provincia (Guadalajara) y distante a su vez de otras poblaciones con mayor posibilidad de recursos tanto de ocio como educativos. Desde el equipo docente del CRA Santa Lucía, se entiende que para que el alumnado tenga un desarrollo competencial adecuado es importante compensar esa situación. Además, las características de los centros rurales, principalmente las referidas a la presencia de alumnos de distintas edades en el aula y la separación kilométrica existente entre las secciones, hace necesario el desarrollo de propuestas educativas coordinadas que permitan la participación activa del alumnado, la implicación de las familias, ayuntamientos y otras entidades del entorno y el trabajo conjunto de todo el equipo docente. Pero si el enclave donde se sitúa el CRA ofrece limitaciones, también ofrece una inagotable fuente de recursos educativos que se pueden aprovechar. Entre ellos uno de los ejes esenciales de este proyecto: La Alcarria.Consejería de Educación, Cultura y Deportes de Castilla-La ManchaCastilla La ManchaES

Actitudes desarrolladas por estudiantes de enfermería en su participación en actividades de voluntariado en salud mental

Es sistema universitario público español considera que debe de asumir un papel protagonista en los procesos de desarrollo humano, a través de experiencias y prácticas destinadas a construir una sociedad más justa y participativa. Es por ello que la Universidad de Alicante tiene la voluntad institucional de promover y ofrecer programas de voluntariado como herramienta que permita la participación de miembros de la comunidad universitaria en dichos procesos. De este modo desde la Universidad no sólo puede darse respuesta a las necesidades del entorno social, sino que además se ofrece al alumnado y a la comunidad universitaria en general la excelente posibilidad de formación complementaria y de desarrollo personal. En este contexto surgió desde la asignatura de Intervención Comunitaria, Salud Mental, Psiquiatría Y Ética, impartida por la Facultad de Ciencias de la Salud la iniciativa del Voluntariado en Salud Mental, formado por estudiantes, profesores y egresados, del Grado en Enfermería, que constituye un recurso a disposición de las posibles necesidades de la Unidad de Hospitalización Psiquiátrica (UHP) del Hospital Universitario de San Juan. En el presente trabajo se describe el desarrollo del voluntariado y sobre la utilidad social del mismo

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus

Adopting transfer learning for neuroimaging : a comparative analysis with a custom 3D convolution neural network model

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)