AIMC Topic: Proof of Concept Study

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Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept.

Clinical radiology Nov 29, 2019
AIM: To investigate the feasibility of applying a deep convolutional neural network (CNN) for detection/localisation of acute proximal femoral fractures (APFFs) on hip radiographs.
Artificial Intelligence Retrospective Studies Female Aged Aged, 80 and over Humans Adolescent Feasibility Studies Hip Fractures Proof of Concept Study Adult Sensitivity and Specificity Male Middle Aged
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An interpretation algorithm for molecular diagnosis of bacterial vaginosis in a maternity hospital using machine learning: proof-of-concept study.

Diagnostic microbiology and infectious disease Nov 27, 2019
Allplex Bacterial vaginosis assay (Seegene, South Korea) is a molecular test for bacterial vaginosis (BV). A machine learning algorithm was devised on 200 samples (BV = 23, non-BV = 177) converting 7 identified bacterial strains polymerase chain reac...
Sensitivity and Specificity Molecular Diagnostic Techniques Hospitals, Maternity Cross Infection Machine Learning Algorithms Female Reproducibility of Results Polymerase Chain Reaction Proof of Concept Study Vaginosis, Bacterial Humans
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Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.

Computational intelligence and neuroscience Nov 27, 2019
In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide t...
Imagination Robotics Electroencephalography Signal Processing, Computer-Assisted Goals Event-Related Potentials, P300 Motor Activity Mental Fatigue Brain-Computer Interfaces Proof of Concept Study Young Adult Biomechanical Phenomena Male Models, Theoretical Humans Artificial Intelligence Brain Female
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The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study.

Surgery Nov 11, 2019
BACKGROUND: Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenectomy. Important in this regard is the presence of a soft pancreatic texture which is a major risk factor. Advances in machine learning and texture analy...
Pancreas Proof of Concept Study Pancreatic Fistula Pancreaticoduodenectomy Humans Machine Learning Retrospective Studies Female Middle Aged Postoperative Complications Positron Emission Tomography Computed Tomography Aged Male
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Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study.

Metabolism: clinical and experimental Nov 9, 2019
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirr...
Humans Female Male Middle Aged Case-Control Studies Polysaccharides Supervised Machine Learning Non-alcoholic Fatty Liver Disease Fatty Acids Proof of Concept Study Fatty Liver Liver Cirrhosis Metabolomics Lipids
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The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology Oct 28, 2019
Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsin...
Prognosis Female Adult Humans Machine Learning Outcome Assessment, Health Care Multiple Sclerosis Patient Reported Outcome Measures Proof of Concept Study Male Middle Aged Disease Progression Severity of Illness Index
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Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Predictive Value of Tests Signal Processing, Computer-Assisted Stroke Atrial Fibrillation Telemetry Veterans Health Services Proof of Concept Study Administrative Claims, Healthcare Diagnosis, Computer-Assisted Time Factors Logistic Models United States Aged, 80 and over Male Middle Aged Risk Assessment Humans Neural Networks, Computer Machine Learning Prognosis Female Risk Factors Aged Retrospective Studies Electronic Health Records
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Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Analytica chimica acta Sep 25, 2019
Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absen...
Irinotecan Metabolomics HCT116 Cells Proof of Concept Study Drug Resistance Biomarkers Antineoplastic Agents ROC Curve Single-Cell Analysis Mass Spectrometry Humans Neural Networks, Computer Machine Learning
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Bio-inspired robotic dog paddling: kinematic and hydro-dynamic analysis.

Bioinspiration & biomimetics Sep 13, 2019
Research on quadrupedal robots inspired by canids or felids have been widely reported and demonstrated. However, none of these legged robots can deal with difficult environments that include water, such as small lakes, streams, rain, mud, flooded ter...
Dogs Swimming Proof of Concept Study Hydrodynamics Biomechanical Phenomena Animals Robotics
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Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.

Journal of medicinal chemistry Aug 27, 2019
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
Models, Molecular Solubility Datasets as Topic Databases, Chemical Drug Discovery Deep Learning Organic Chemicals Proof of Concept Study
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