AIMC Topic: False Positive Reactions

Clear Filters Showing 41 to 50 of 157 articles

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

Computer methods and programs in biomedicine Jun 5, 2020
BACKGROUND AND OBJECTIVE: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As...
Humans Neural Networks, Computer Software Pandemics COVID-19 Radiographic Image Interpretation, Computer-Assisted Coronavirus Infections False Positive Reactions Radiography, Thoracic Betacoronavirus Pneumonia, Viral Algorithms Databases, Factual Reproducibility of Results SARS-CoV-2 Deep Learning
View on PubMed DOI

Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

The British journal of ophthalmology May 6, 2020
BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an A...
Diabetic Retinopathy Artificial Intelligence Algorithms Female Reproducibility of Results Humans Middle Aged Image Processing, Computer-Assisted Microscopy, Confocal Predictive Value of Tests Diagnostic Imaging Aged Adult Sensitivity and Specificity Cross-Sectional Studies Male Retina Diagnostic Tests, Routine False Positive Reactions Reference Standards Slit Lamp Microscopy
View on PubMed DOI

Radiomics and deep learning in lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] May 4, 2020
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...
Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Humans Genotype False Positive Reactions Computational Biology Forecasting Radiosurgery Treatment Outcome Phenotype Radiotherapy Neoplasm Staging Databases, Factual Radiation Injuries Neoplasm Recurrence, Local Prognosis Imaging Genomics Radiotherapy Planning, Computer-Assisted
View on PubMed DOI

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology Apr 27, 2020
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more tr...
False Positive Reactions Clinical Laboratory Techniques Betacoronavirus Pneumonia, Viral Tomography, X-Ray Computed SARS-CoV-2 Sensitivity and Specificity Lung Humans Deep Learning Neural Networks, Computer Reverse Transcriptase Polymerase Chain Reaction Pandemics Coronavirus Infections COVID-19 COVID-19 Testing Image Interpretation, Computer-Assisted False Negative Reactions
View on PubMed DOI

Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

The Journal of clinical psychiatry Apr 21, 2020
OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implement...
False Positive Reactions Male Risk Assessment Attitude of Health Personnel Suicide Prevention False Negative Reactions Humans Machine Learning Algorithms Health Personnel Physicians Electronic Health Records Female Adult
View on PubMed DOI

Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans.

Medical physics Mar 18, 2020
PURPOSE: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative mo...
False Positive Reactions Radiography, Thoracic Neural Networks, Computer Tomography, X-Ray Computed Sensitivity and Specificity Image Processing, Computer-Assisted Lung Neoplasms Humans
View on PubMed DOI

A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Scientific reports Feb 20, 2020
Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and pre...
Aged, 80 and over Adult Pulmonary Disease, Chronic Obstructive Male Middle Aged Disease Progression Comorbidity ROC Curve Decision Support Systems, Clinical Inflammation Prognosis Models, Statistical Female Decision Trees Reproducibility of Results Humans False Positive Reactions Hospitalization Machine Learning Risk Aged Algorithms
View on PubMed DOI

Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.

The Lancet. Digital health Feb 6, 2020
BACKGROUND: Mammography is the current standard for breast cancer screening. This study aimed to develop an artificial intelligence (AI) algorithm for diagnosis of breast cancer in mammography, and explore whether it could benefit radiologists by imp...
Observer Variation False Positive Reactions Breast Neoplasms Middle Aged Humans Early Detection of Cancer Artificial Intelligence Mammography Retrospective Studies Radiology Female Adult
View on PubMed DOI

Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.

PloS one Jan 10, 2020
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multi- modal biosignals. Most of the current work in the literature are eithe...
Monitoring, Physiologic Humans Supervised Machine Learning Neural Networks, Computer Arrhythmias, Cardiac Algorithms False Positive Reactions Intensive Care Units Clinical Alarms Sensitivity and Specificity
View on PubMed DOI

Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.

Journal of critical care Jan 9, 2020
PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibility of a search based on natural language processing (NLP) on the text sections of electronic health r...
Sensitivity and Specificity Electronic Health Records Natural Language Processing Humans Female Middle Aged Software Hospitalization Epidemiologic Studies Incidence Databases, Factual Young Adult Pattern Recognition, Automated Shock, Septic Prospective Studies Adult False Positive Reactions Predictive Value of Tests Intensive Care Units International Classification of Diseases
View on PubMed DOI
Popular Topics
Recent Journals