AIMC Topic: Reference Standards

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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.
Adult Male Middle Aged Lung Diagnosis, Computer-Assisted Algorithms Retrospective Studies Female Reproducibility of Results Aged Bayes Theorem Pattern Recognition, Automated Calibration Reference Standards Radiography, Thoracic Humans Deep Learning ROC Curve Image Processing, Computer-Assisted Predictive Value of Tests Area Under Curve
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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.
Prevalence Diagnosis, Computer-Assisted Humans Prostatic Neoplasms ROC Curve Machine Learning Magnetic Resonance Imaging Biopsy Algorithms Area Under Curve Reference Standards Male Prostatectomy
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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 Humans Female Male Deep Learning 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...
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 Slit Lamp Microscopy Diagnostic Tests, Routine False Positive Reactions Reference Standards
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Artificial intelligence versus clinicians.

BMJ (Clinical research ed.) Apr 3, 2020
Deep Learning Reference Standards Artificial Intelligence
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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.
Radiologists Humans Retrospective Studies Neural Networks, Computer Arthroscopy Male Reference Standards Female Middle Aged Tibial Meniscus Injuries Aged Clinical Competence Adult Adolescent Sensitivity and Specificity Magnetic Resonance Imaging
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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...
Reishi Solutions Anions Neural Networks, Computer Chromatography, High Pressure Liquid Reproducibility of Results Ionic Liquids Time Factors Solvents Rotation Ultrasonics Reference Standards
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Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

BMC musculoskeletal disorders Mar 12, 2020
BACKGROUND: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiolog...
Humans Japan Machine Learning Spinal Stenosis Adult Female Outcome Assessment, Health Care Prospective Studies Male Reproducibility of Results Observer Variation Middle Aged Aged Reference Standards Magnetic Resonance Imaging Aged, 80 and over Lumbar Vertebrae Young Adult
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Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Nature communications Mar 6, 2020
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Her...
Mastectomy Reference Standards Preoperative Period Elasticity Imaging Techniques Lymphatic Metastasis Lymph Node Excision Axilla Neoplasm Staging Breast Ultrasonography ROC Curve Image Processing, Computer-Assisted Lymph Nodes Humans Prospective Studies Prognosis Female Aged Aged, 80 and over Adult Breast Neoplasms Middle Aged Deep Learning
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Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women.

Breast cancer (Tokyo, Japan) Feb 12, 2020
BACKGROUND: To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
Radiographic Image Interpretation, Computer-Assisted Breast Decision Support Systems, Clinical Japan Humans Early Detection of Cancer Young Adult Reference Standards Artificial Intelligence Mammography Adult Retrospective Studies Breast Neoplasms Radiologists Female Middle Aged Aged ROC Curve Aged, 80 and over
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