AIMC Topic: Observer Variation

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Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Aug 5, 2020
The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gle...
Observer Variation Biopsy Pathologists Microscopy Image Interpretation, Computer-Assisted Humans Reproducibility of Results Male Diagnosis, Computer-Assisted Deep Learning Predictive Value of Tests Prostatic Neoplasms Neoplasm Grading
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Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI.

AJR. American journal of roentgenology Aug 5, 2020
The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cance...
Humans Artificial Intelligence Algorithms Retrospective Studies Aged Sensitivity and Specificity Male Middle Aged Adenocarcinoma Diagnosis, Computer-Assisted Prostatic Neoplasms Observer Variation Random Allocation Multiparametric Magnetic Resonance Imaging
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Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.

Upsala journal of medical sciences Jul 22, 2020
BACKGROUND: The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to en...
Automation Pattern Recognition, Automated Smoking False Positive Reactions Tobacco Use Disorder Humans Natural Language Processing Machine Learning Bayes Theorem Software Data Mining Algorithms Reproducibility of Results Observer Variation Research Design Sweden Medical Informatics Electronic Health Records ROC Curve Support Vector Machine
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Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.

Radiation oncology (London, England) Jul 20, 2020
BACKGROUND: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed...
Neck Observer Variation Radiation Dosage Atlases as Topic Humans Tomography, X-Ray Computed Masticatory Muscles Deep Learning Head and Neck Neoplasms
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Fast interactive medical image segmentation with weakly supervised deep learning method.

International journal of computer assisted radiology and surgery Jul 11, 2020
PURPOSE: To achieve accurate image segmentation, which is the first critical step in medical image analysis and interventions, using deep neural networks seems a promising approach provided sufficiently large and diverse annotated data from experts. ...
Humans Neural Networks, Computer Databases, Factual Tomography, X-Ray Computed Reproducibility of Results Image Processing, Computer-Assisted Prostatic Neoplasms Prostate Observer Variation Pattern Recognition, Automated Male Echocardiography Diagnosis, Computer-Assisted Deep Learning Ultrasonography Brachytherapy
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Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

Scientific reports Jul 6, 2020
The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the ...
Neuroendocrine Tumors Ki-67 Antigen Neoplasm Grading Gastrointestinal Neoplasms Humans Observer Variation Reproducibility of Results Immunohistochemistry Deep Learning Synaptophysin
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Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

European radiology Jun 27, 2020
OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (...
Diagnostic Tests, Routine Observer Variation Pattern Recognition, Automated Calibration Adenocarcinoma of Lung Diagnosis, Differential Granuloma Tuberculosis Sex Factors Deep Learning Humans Female Logistic Models ROC Curve Algorithms Image Processing, Computer-Assisted Adult Nomograms Retrospective Studies Lung Neoplasms Male Age Factors Tomography, X-Ray Computed Middle Aged Solitary Pulmonary Nodule Diagnosis, Computer-Assisted Regression Analysis
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Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment.

European radiology Jun 26, 2020
OBJECTIVES: To benchmark the performance of a calibrated 3D convolutional neural network (CNN) applied to multiparametric MRI (mpMRI) for risk assessment of clinically significant prostate cancer (csPCa) using decision curve analysis (DCA).
Risk Assessment Deep Learning Image Processing, Computer-Assisted Calibration Prostate-Specific Antigen Magnetic Resonance Imaging Normal Distribution Biopsy Prostatic Neoplasms Benchmarking Humans Observer Variation Machine Learning Algorithms Retrospective Studies Male
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Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.

European radiology Jun 26, 2020
OBJECTIVE: To compare the CT texture feature reproducibility of 2D and 3D segmentations and their machine learning (ML)-based classifications for predicting human papilloma virus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).
Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Male Imaging, Three-Dimensional Image Processing, Computer-Assisted Papillomavirus Infections Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Humans Machine Learning Algorithms Regression Analysis Diagnosis, Differential Area Under Curve Observer Variation Alphapapillomavirus
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Simplified PADUA Renal (SPARE) Nephrometry Scoring System: External Validation, Interobserver Variability, and Comparison with RENAL and PADUA in a Single-center Robotic Partial Nephrectomy Series.

European urology focus Jun 23, 2020
BACKGROUND: The RENAL (radius [R], exophytic/endophytic [E], nearness to collecting system/sinus [N], anterior/posterior [A], and location relative to polar lines [L]) and the PADUA (preoperative aspects and dimensions used for an anatomical classifi...
Robotic Surgical Procedures Observer Variation Kidney Neoplasms Humans Nephrectomy Retrospective Studies Reproducibility of Results Robotics
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