AIMC Topic: Reference Standards

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REPLY: THE STANDARDIZATION AND AUTOMATION OF MACHINE LEARNING FOR BIOMEDICAL DATA.

The Journal of thoracic and cardiovascular surgery Aug 28, 2020
Machine Learning Patient Readmission Automation Reference Standards Humans
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Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Nature communications Aug 7, 2020
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accurac...
Humans Machine Learning Algorithms Female Electrocardiography Acute Coronary Syndrome Databases as Topic Reference Standards Male Middle Aged Hospitals ROC Curve
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Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard.

Journal of magnetic resonance imaging : JMRI Jul 26, 2020
BACKGROUND: MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection ...
Magnetic Resonance Imaging Arthroscopy Reference Standards Anterior Cruciate Ligament Injuries Retrospective Studies Reproducibility of Results Sensitivity and Specificity Deep Learning Anterior Cruciate Ligament Humans
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Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration.

European radiology Jul 14, 2020
OBJECTIVES: To evaluate the calibration of a deep learning (DL) model in a diagnostic cohort and to improve model's calibration through recalibration procedures.
Female Reproducibility of Results Aged Adult Humans Algorithms Retrospective Studies Predictive Value of Tests Area Under Curve Bayes Theorem Pattern Recognition, Automated Diagnosis, Computer-Assisted Deep Learning ROC Curve Image Processing, Computer-Assisted Calibration Male Reference Standards Middle Aged Radiography, Thoracic Lung
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Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.

European radiology Jun 30, 2020
OBJECTIVES: The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI.
ROC Curve Magnetic Resonance Imaging Area Under Curve Prostatic Neoplasms Biopsy Reference Standards Prostatectomy Algorithms Male Prevalence Diagnosis, Computer-Assisted Humans Machine Learning
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.

Proceedings of the National Academy of Sciences of the United States of America May 26, 2020
Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research...
Sex Factors Datasets as Topic Reference Standards Radiography, Thoracic Male Humans Deep Learning Female Bias Radiographic Image Interpretation, Computer-Assisted
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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...
Humans Diabetic Retinopathy Artificial Intelligence Algorithms Slit Lamp Microscopy Image Processing, Computer-Assisted Microscopy, Confocal Predictive Value of Tests Diagnostic Imaging Retina Diagnostic Tests, Routine Sensitivity and Specificity False Positive Reactions Female Cross-Sectional Studies Reference Standards Reproducibility of Results Male Aged Middle Aged Adult
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Artificial intelligence versus clinicians.

BMJ (Clinical research ed.) Apr 3, 2020
Reference Standards Artificial Intelligence Deep Learning
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Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference.

Skeletal radiology Mar 13, 2020
OBJECTIVE: To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears.
Sensitivity and Specificity Male Middle Aged Clinical Competence Female Aged Humans Adult Neural Networks, Computer Reference Standards Retrospective Studies Tibial Meniscus Injuries Adolescent Magnetic Resonance Imaging Radiologists Arthroscopy
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Ionic Liquid-Based Ultrasonic-Assisted Extraction Coupled with HPLC and Artificial Neural Network Analysis for .

Molecules (Basel, Switzerland) Mar 13, 2020
is widely used in traditional Chinese medicine (TCM). Ganoderic acid A and D are the main bioactive components with anticancer effects in . To obtain the maximum content of two compounds from , a novel extraction method, an ionic liquid-based ultras...
Ultrasonics Reference Standards Rotation Reishi Anions Neural Networks, Computer Solutions Chromatography, High Pressure Liquid Reproducibility of Results Ionic Liquids Time Factors Solvents
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