AIMC Topic: Observer Variation

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A deep cascaded segmentation of obstructive sleep apnea-relevant organs from sagittal spine MRI.

International journal of computer assisted radiology and surgery Mar 26, 2021
PURPOSE: The main purpose of this work was to develop an efficient approach for segmentation of structures that are relevant for diagnosis and treatment of obstructive sleep apnea syndrome (OSAS), namely pharynx, tongue, and soft palate, from mid-sag...
Germany Palate, Soft Sleep Apnea, Obstructive Pharynx Tongue Observer Variation Humans Algorithms Female Risk Factors Male Deep Learning Magnetic Resonance Imaging
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Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study.

Radiology Mar 23, 2021
Background Previous studies assessing the effects of computer-aided detection on observer performance in the reading of chest radiographs used a sequential reading design that may have biased the results because of reading order or recall bias. Purpo...
Humans Retrospective Studies Deep Learning Female Republic of Korea Lung Diseases Sensitivity and Specificity Cross-Over Studies Male Observer Variation Middle Aged Radiography, Thoracic
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Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.

Radiation oncology (London, England) Feb 25, 2021
BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentat...
Radiometry Mastectomy, Segmental Adult Breast Neoplasms Middle Aged Deep Learning Feasibility Studies Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Radiotherapy, Intensity-Modulated Humans Tomography, X-Ray Computed Female Aged
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Searching Images for Consensus: Can AI Remove Observer Variability in Pathology?

The American journal of pathology Feb 24, 2021
One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imagin...
Pathology Artificial Intelligence Image Processing, Computer-Assisted Natural Language Processing Observer Variation Humans Neural Networks, Computer
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Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

European journal of cancer (Oxford, England : 1990) Feb 24, 2021
PURPOSE: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatme...
Humans Retrospective Studies Clinical Decision-Making Neoplasm Invasiveness Predictive Value of Tests Ultrasonography, Mammary Image Interpretation, Computer-Assisted Middle Aged Mastectomy Female Observer Variation Deep Learning Chemotherapy, Adjuvant Reproducibility of Results Nomograms Adult Treatment Outcome Breast Neoplasms Neoadjuvant Therapy
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Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

European journal of nuclear medicine and molecular imaging Feb 9, 2021
PURPOSE: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV...
Head and Neck Neoplasms Positron Emission Tomography Computed Tomography Tumor Burden Observer Variation Humans Deep Learning
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Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence.

Communications biology Feb 5, 2021
Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image seg...
Humans Retrospective Studies Female Reproducibility of Results Adult Animals Neural Networks, Computer Artificial Intelligence Machine Learning Algorithms Retinal Diseases Macaca fascicularis Multimodal Imaging Tomography, Optical Coherence Retina Image Interpretation, Computer-Assisted Male Observer Variation Middle Aged Deep Learning Clinical Competence
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Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Journal of cutaneous pathology Jan 26, 2021
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...
Dermatology Diagnostic Tests, Routine Observer Variation Pathology Humans Nevus Neural Networks, Computer Keratosis, Seborrheic Melanoma Artificial Intelligence Skin Neoplasms Machine Learning Carcinoma, Basal Cell Algorithms Deep Learning Research Diagnosis, Differential
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Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation.

The British journal of radiology Dec 17, 2020
OBJECTIVES: To investigate the impact of deep learning (DL) on radiologists' detection accuracy and reading efficiency of rib fractures on CT.
Reproducibility of Results Adolescent Retrospective Studies Humans Sensitivity and Specificity Tomography, X-Ray Computed Aged Rib Fractures Radiographic Image Interpretation, Computer-Assisted Ribs Male Aged, 80 and over Observer Variation Female Middle Aged Young Adult Adult Deep Learning
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Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.

PloS one Dec 16, 2020
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. S...
Video Recording Male Social Behavior Humans Data Interpretation, Statistical Observer Variation Choice Behavior Research Design Research Personnel Supervised Machine Learning Behavior Observation Techniques Female Drosophila Sex Factors Animals
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