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Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.

Radiology
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for...

Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.

International journal of environmental research and public health
With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful f...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BMC cardiovascular disorders
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthe...

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features.

Current issues in molecular biology
It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computat...

[Structured reporting and artificial intelligence].

Der Radiologe
BACKGROUND: There are a multitude of application possibilities of artificial intelligence (AI) and structured reporting (SR) in radiology. The number of scientific publications have continuously increased for many years. There is an extensive portfol...

Artificial intelligence-enhanced intraoperative neurosurgical workflow: current knowledge and future perspectives.

Journal of neurosurgical sciences
INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical as...

Integration of imaging modalities in digital dental workflows - possibilities, limitations, and potential future developments.

Dento maxillo facial radiology
The digital workflow process follows different steps for all dental specialties. However, the main ingredient for the diagnosis, treatment planning and follow-up workflow recipes is the imaging chain. The steps in the imaging chain usually include al...

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magne...

Document-Image Related Visual Sensors and Machine Learning Techniques.

Sensors (Basel, Switzerland)
Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g.,  libraries, office communication, managementof workflows, and electronic archiving [...].

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

Genome medicine
BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly...