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Observer Variation

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Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Jan 6, 2020
PURPOSE: We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.
Humans Neural Networks, Computer Tomography, X-Ray Computed Female Reproducibility of Results Image Processing, Computer-Assisted Radiation Oncology Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Adult Uterine Cervical Neoplasms Middle Aged Deep Learning
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Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Dec 16, 2019
Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided ...
Biomarkers, Tumor Observer Variation Humans Immunohistochemistry Machine Learning Keratins Colorectal Neoplasms
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On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

IEEE transactions on medical imaging Dec 13, 2019
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context o...
Humans Observer Variation Neural Networks, Computer Uncertainty Reproducibility of Results Echocardiography
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Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 5, 2019
BACKGROUND: Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for com...
Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Humans Male Deep Learning
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Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England) Nov 27, 2019
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...
Observer Variation Radiotherapy Liver Neoplasms Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Neural Networks, Computer Tomography, X-Ray Computed Republic of Korea Female Male Middle Aged Reproducibility of Results Humans Deep Learning Aged
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DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs.

Scientific reports Nov 26, 2019
In this study, a deep learning-based method for developing an automated diagnostic support system that detects periodontal bone loss in the panoramic dental radiographs is proposed. The presented method called DeNTNet not only detects lesions but als...
Retrospective Studies Deep Learning Radiography, Panoramic Radiographic Image Interpretation, Computer-Assisted Humans Dental Hygienists Algorithms Alveolar Bone Loss Datasets as Topic Observer Variation
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Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

International journal of colorectal disease Nov 6, 2019
INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpret...
Aged, 80 and over Male Middle Aged Colonoscopy Machine Learning Female Reproducibility of Results Aged Feasibility Studies Colonic Neoplasms Tumor Burden Colonic Polyps Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Clinical Competence Microscopy, Confocal Predictive Value of Tests Humans Observer Variation
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 22, 2019
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...
Neoplasm Staging Humans Organs at Risk Neural Networks, Computer Radiotherapy Planning, Computer-Assisted Algorithms Observer Variation Female Middle Aged Aged Adolescent Deep Learning Young Adult Head and Neck Neoplasms Adult Neck Male
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Endoscopic detection and differentiation of esophageal lesions using a deep neural network.

Gastrointestinal endoscopy Oct 1, 2019
BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentia...
Aged Aged, 80 and over Adult Sensitivity and Specificity Deep Learning Image Processing, Computer-Assisted Neoplasm Invasiveness Male Middle Aged Humans Neural Networks, Computer Female Reproducibility of Results Optical Imaging Esophagus Esophageal Squamous Cell Carcinoma Esophageal Diseases Esophageal Neoplasms Narrow Band Imaging Observer Variation Precancerous Conditions
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Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety.

JACC. Cardiovascular interventions Sep 25, 2019
OBJECTIVES: This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping.
Aged Observer Variation Humans Databases, Factual Male Predictive Value of Tests Neural Networks, Computer Middle Aged Arterial Pressure Female Machine Learning Europe Reproducibility of Results Patient Safety Risk Factors Aorta Cardiac Catheterization Coronary Angiography Coronary Artery Disease Heart Rate Artifacts
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