Español Inglés
Acceso usuarios

Big Data

Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/ postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting


The first prospective clinical trial to evaluate the safety and performance of an artificial intelligence (AI) diagnostic aid for skin cancer detection and management in the real-world clinical setting. Participants are recruited on a consecutive basis from routine attendance at melanoma and skin cancer assessment clinics, forming a representative sample of patients and lesion phenotypes from which to evaluate AI algorithm performance. AI performance will be compared with teledermatologists' assessment, as well as to face-to-face assessors of varying clinical experience (registrars and consultant dermatologists), and with histopathology results for biopsied lesions.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters


The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed data from a cohort of 303 patients with reverse transcription-polymerase chain reaction (RT-PCR) confirmed COVID-19, during the first phase of the COVID-19 global pandemic from 14 March to 10 September 2020. Statistical methods and survival analysis, together with the development of machine learning classifiers, were carried out on these data, with the purpose of identifying hematochemical parameters that better reflect and contribute to the risk assessment.


Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach


Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images.

Analyzing the impact of Machine learning and Artificial intelligence and its Effect on Management of lung cancer detection in covid-19 pandemic


Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies

Towards Machine-Readable (Meta) Data and the FAIR Value for Artificial Intelligence Exploration of COVID-19 and Cancer Research Data Even before COVID-19, the bioinformatics labs and life science industry were investing extensively in ecosystems of techno


Even before COVID-19, the bioinformatics labs and life science industry were investing extensively in ecosystems of technological and analytical applications/appliances to store, curate, share, integrate, and analyze large amounts of data. With the pandemic coming at an accelerating pace, a series of global research actions are being implemented to strive against the virus and its effects and to create data-driven investigations to support more agile responses to future events. Innovative solutions in COVID-19 research require more efficient and effective data management strategies and practices. Cancer research is an excellent example of the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles on precision oncology and major cancer data repositories, such as the NIH Cancer Research Data Commons, are gradually adhering to these principles.

Artificial intelligence in oncology: current applications and future perspectives


Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice.

Scope of Artificial Intelligence in Gastrointestinal Oncology


Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis.

Machine Learning Predicts Cancer Treatment Response


The Georgia Institute of Technology and Ovarian Cancer Institute researchers are using machine learning algorithms to predict how patients will respond to cancer-fighting drugs. Advances in machine learning and artificial intelligence are allowing researchers to create more targeted precision medicine-based treatment using predictive analytics. By analyzing large amounts of complex data, clinicians can provide individualized treatments, improving patient outcomes.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas


Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

Current status and limitations of artificial intelligence in colonoscopy


Background: Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development.

Aim: This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail.

Methods: A literature search to identify important studies exploring the use of AI in colonoscopy was performed.
Results: This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.

Investigadores europeos impulsan la integración de técnicas de Big Data e IA en salud


La implantación de estas soluciones posibilitará procesos más efectivos para la salud del paciente y la sostenibilidad del sistema sanitario europeo.

Total pages: 8
Diseño y Desarrollo web Im3diA comunicación

Bienvenida/o a la información básica sobre las cookies de la página web responsabilidad de la entidad: Grupo Arán de Comunicación S.L..

Una cookie o galleta informática es un pequeño archivo de información que se guarda en tu ordenador, “smartphone” o tableta cada vez que visitas nuestra página web. Algunas cookies son nuestras y otras pertenecen a empresas externas que prestan servicios para nuestra página web.

Las cookies pueden ser de varios tipos: las cookies técnicas son necesarias para que nuestra página web pueda funcionar, no necesitan de tu autorización y son las únicas que tenemos activadas por defecto.

El resto de cookies sirven para mejorar nuestra página, para personalizarla en base a tus preferencias, o para poder mostrarte publicidad ajustada a tus búsquedas, gustos e intereses personales. Puedes aceptar todas estas cookies pulsando el botón ACEPTA TODO o configurarlas o rechazar su uso clicando en el apartado CONFIGURACIÓN DE COOKIES.

Si quires más información, consulta la “POLITICA COOKIES” de nuestra página web.