AIMC Topic: Observer Variation

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A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images.

IEEE journal of biomedical and health informatics Oct 5, 2021
Health professionals extensively use Two-Dimensional (2D) Ultrasound (US) videos and images to visualize and measure internal organs for various purposes including evaluation of muscle architectural changes. US images can be used to measure abdominal...
Deep Learning Ultrasonography Abdominal Muscles Observer Variation Humans Reproducibility of Results
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Repeatability and reproducibility of deep-learning-based liver volume and Couinaud segment volume measurement tool.

Abdominal radiology (New York) Oct 4, 2021
PURPOSE: Volumetric and health assessment of the liver is crucial to avoid poor post-operative outcomes following liver resection surgery. No current methods allow for concurrent and accurate measurement of both Couinaud segmental volumes for future ...
Deep Learning Magnetic Resonance Imaging Liver Observer Variation Humans Reproducibility of Results Hepatectomy
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Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.

Scientific reports Sep 28, 2021
The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients ...
ROC Curve Image Processing, Computer-Assisted Treatment Outcome Follow-Up Studies Middle Aged Risk Assessment Deep Learning Observer Variation Kaplan-Meier Estimate Stomach Keratins Neoplasm Staging Carcinoma Gastrectomy Humans Female Stomach Neoplasms Male Aged Adult
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Evaluation of artificial intelligence on a reference standard based on subjective interpretation.

The Lancet. Digital health Sep 21, 2021
Models, Biological Humans Reference Standards Observer Variation Artificial Intelligence Clinical Decision-Making
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Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

Radiation oncology (London, England) Sep 9, 2021
BACKGROUND: Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the d...
Atlases as Topic Artificial Intelligence Male Head and Neck Neoplasms Observer Variation Cloud Computing Prostatic Neoplasms Organs at Risk Humans
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Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.

Scientific reports Aug 16, 2021
Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-dependent manual segmentation techniques. A convolutional neural network (CNN) model was trained to se...
Humans Female Reproducibility of Results Male Deep Learning Adiposity Young Adult Adult Datasets as Topic Automation Observer Variation Neck Muscles Cervical Vertebrae Image Processing, Computer-Assisted Adipose Tissue Anthropometry Body Mass Index Organ Size Magnetic Resonance Imaging
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Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance.

Scientific reports Aug 4, 2021
We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader stu...
Radiography, Thoracic Radiologists Radiographic Image Interpretation, Computer-Assisted Observer Variation ROC Curve Multiple Pulmonary Nodules Solitary Pulmonary Nodule Humans Neural Networks, Computer
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Clinically applicable artificial intelligence system for dental diagnosis with CBCT.

Scientific reports Jul 22, 2021
In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinica...
Stomatognathic Diseases Image Processing, Computer-Assisted Sensitivity and Specificity Disease Management Humans Cone-Beam Computed Tomography Observer Variation Artificial Intelligence
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Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.

Virchows Archiv : an international journal of pathology Jul 19, 2021
The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserve...
Observer Variation Immunohistochemistry Receptor, ErbB-2 Humans Carcinoma, Ductal, Breast Artificial Intelligence Microscopy Breast Neoplasms Automation, Laboratory In Situ Hybridization, Fluorescence Biomarkers, Tumor Image Interpretation, Computer-Assisted China Retrospective Studies Predictive Value of Tests Female Reproducibility of Results
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Interobserver variability in organ at risk delineation in head and neck cancer.

Radiation oncology (London, England) Jun 28, 2021
BACKGROUND: In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck canc...
Observer Variation Head and Neck Neoplasms Humans Radiotherapy Dosage Artificial Intelligence Radiotherapy, Intensity-Modulated Prognosis Organs at Risk Radiotherapy Planning, Computer-Assisted
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