AIMC Topic: Observer Variation

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Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer.

International journal of radiation oncology, biology, physics Nov 9, 2020
PURPOSE: This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events ...
Uterine Cervical Neoplasms Deep Learning Radiotherapy Planning, Computer-Assisted Observer Variation Radiotherapy Dosage Humans Retrospective Studies Tomography, X-Ray Computed Female
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Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.

JAMA network open Nov 2, 2020
IMPORTANCE: Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and inst...
Prostatic Neoplasms Organs at Risk Quality Improvement Observer Variation Radiotherapy, Intensity-Modulated Male Middle Aged Deep Learning Head and Neck Neoplasms Radiotherapy, Image-Guided Humans Neural Networks, Computer Tomography, X-Ray Computed Reproducibility of Results Aged
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Traditional and New Methods of Bone Age Assessment-An Overview.

Journal of clinical research in pediatric endocrinology Oct 26, 2020
Bone age is one of biological indicators of maturity used in clinical practice and it is a very important parameter of a child’s assessment, especially in paediatric endocrinology. The most widely used method of bone age assessment is by performing a...
Wrist Joint Age Determination by Skeleton Adolescent Development Hand Bones Radiographic Image Interpretation, Computer-Assisted Observer Variation Automation Child Development Male Infant Diagnosis, Computer-Assisted Deep Learning Adolescent Child, Preschool Child Age Factors Predictive Value of Tests Neural Networks, Computer Female Infant, Newborn Reproducibility of Results Young Adult Humans
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Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks.

Gastroenterology Oct 21, 2020
BACKGROUND AND AIMS: Endoscopic disease activity scoring in ulcerative colitis (UC) is useful in clinical practice but done infrequently. It is required in clinical trials, where it is expensive and slow because human central readers are needed. A ma...
Antibodies, Monoclonal, Humanized Colitis, Ulcerative Intestinal Mucosa Predictive Value of Tests Feasibility Studies Severity of Illness Index Deep Learning Adolescent Treatment Outcome Video Recording Image Interpretation, Computer-Assisted Observer Variation Humans Prospective Studies Adult Female Male Aged Middle Aged Young Adult Colon Colonoscopy
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Deep learning for elective neck delineation: More consistent and time efficient.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 14, 2020
BACKGROUND/PURPOSE: Delineation of the lymph node levels of the neck for irradiation of the elective clinical target volume in head and neck cancer (HNC) patients is time consuming and prone to interobserver variability (IOV), although international ...
Neural Networks, Computer Deep Learning Head and Neck Neoplasms Neoplasm Staging Observer Variation Humans
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Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.

Gut Oct 7, 2020
OBJECTIVE: The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These li...
Image Interpretation, Computer-Assisted Observer Variation Pancreas Autoimmune Pancreatitis Diagnosis, Differential Pancreatic Neoplasms Area Under Curve Carcinoma, Pancreatic Ductal Endosonography Humans Neural Networks, Computer Machine Learning ROC Curve
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Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging.

Physics in medicine and biology Sep 18, 2020
The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enha...
Humans Neural Networks, Computer Retrospective Studies Brain Neoplasms Imaging, Three-Dimensional Contrast Media Observer Variation Perfusion Imaging
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Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma.

Scientific reports Sep 17, 2020
Our objective was to compare the diagnostic performance and diagnostic confidence of convolutional neural networks (CNN) to radiologists in characterizing small hypoattenuating hepatic nodules (SHHN) in colorectal carcinoma (CRC) on CT scans. Retrosp...
Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Female Observer Variation Aged Expert Testimony Aged, 80 and over Young Adult Humans Neural Networks, Computer Retrospective Studies Diagnosis, Computer-Assisted Liver Neoplasms Liver Neoplasm Staging Radiologists Adult Colorectal Neoplasms Male Middle Aged
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Automated detection of cribriform growth patterns in prostate histology images.

Scientific reports Sep 10, 2020
Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth pa...
Prostatic Neoplasms Neoplasm Grading Observer Variation Biopsy, Needle Neural Networks, Computer Male ROC Curve Image Processing, Computer-Assisted Adenocarcinoma Humans
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Effect of vessel wall segmentation on volumetric and radiomic parameters of coronary plaques with adverse characteristics.

Journal of cardiovascular computed tomography Aug 10, 2020
BACKGROUND: Quantitative coronary plaque parameters are increasingly being utilized as surrogate endpoints of pharmaceutical trials. However, little is known whether differences in segmentation significantly alter parameter values.
Computed Tomography Angiography Clinical Competence Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Reproducibility of Results Observer Variation Aged Coronary Vessels Male Plaque, Atherosclerotic Middle Aged Vascular Calcification Severity of Illness Index Humans Machine Learning Retrospective Studies Female
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