AIMC Topic: Humans

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A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery.

The international journal of medical robotics + computer assisted surgery : MRCAS
INTRODUCTION: The current study presents a deep learning framework to determine, in real-time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real cou...

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.

Sensors (Basel, Switzerland)
Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-...

Incremental Ant-Miner Classifier for Online Big Data Analytics.

Sensors (Basel, Switzerland)
Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records ...

Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.

Current oncology (Toronto, Ont.)
:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feat...

Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL).

Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with F-FDG,  Ga-DOTATATE, and F-Fluciclovine.

European journal of nuclear medicine and molecular imaging
UNLABELLED: A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using F-FDG,  Ga-DOTATATE, and F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps e...

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Genomics, proteomics & bioinformatics
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcript...

A machine learning approach to identify stochastic resonance in human perceptual thresholds.

Journal of neuroscience methods
BACKGROUND: Stochastic resonance (SR) is achieved when a faint signal is improved with the addition of the appropriate amount of white noise. Perceptual thresholds are expected to follow a characteristic performance improvement curve as a function of...

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification.

Medical image analysis
Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landma...

Charting the potential of brain computed tomography deep learning systems.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality i...