AIMC Topic: Observer Variation

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Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer.

Diagnostic pathology Jan 30, 2022
BACKGROUND: Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers.
Breast Neoplasms Observer Variation Ki-67 Antigen Artificial Intelligence Female Reproducibility of Results Humans
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Automatic Assessment of Pectus Excavatum Severity From CT Images Using Deep Learning.

IEEE journal of biomedical and health informatics Jan 17, 2022
Pectus excavatum (PE) is the most common abnormality of the thoracic cage, whose severity is evaluated by extracting three indices (Haller, correction and asymmetry) from computed tomography (CT) images. To date, this analysis is performed manually, ...
Deep Learning Observer Variation Funnel Chest Humans Tomography, X-Ray Computed Reproducibility of Results
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Machine Learning for Auto-Segmentation in Radiotherapy Planning.

Clinical oncology (Royal College of Radiologists (Great Britain)) Jan 5, 2022
Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability...
Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Deep Learning Image Processing, Computer-Assisted Humans Machine Learning
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Artificial intelligence for the assessment of bowel preparation.

Gastrointestinal endoscopy Dec 8, 2021
BACKGROUND AND AIMS: A reliable assessment of bowel preparation is important to ensure high-quality colonoscopy. Current bowel preparation scoring systems are limited by interobserver variability. This study aimed to demonstrate objective assessment ...
Cathartics Colonoscopy Observer Variation Humans Neural Networks, Computer Artificial Intelligence
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Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.

Medical physics Dec 1, 2021
PURPOSE: Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict hu...
Humans Algorithms Image Processing, Computer-Assisted Observer Variation Radiation Dosage Tomography, X-Ray Computed Deep Learning Liver Neoplasms Phantoms, Imaging
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Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 19, 2021
BACKGROUND AND PURPOSE: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. D...
Radiotherapy Planning, Computer-Assisted Observer Variation Deep Learning Sarcoma Soft Tissue Neoplasms Humans Reproducibility of Results
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Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Oct 26, 2021
The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicate...
Radiotherapy Planning, Computer-Assisted Observer Variation Radiation Oncology Organs at Risk Humans Deep Learning
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Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging.

Computers in biology and medicine Oct 21, 2021
Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep l...
Image Processing, Computer-Assisted Breast Reproducibility of Results Observer Variation Deep Learning Ultrasonography Humans Female
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Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.

Radiation oncology (London, England) Oct 14, 2021
PURPOSE: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts.
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiation Oncologists Observer Variation Radiotherapy, Adjuvant Humans Image Processing, Computer-Assisted Organs at Risk Female Radiotherapy Planning, Computer-Assisted Breast Neoplasms Deep Learning
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Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network.

Scientific reports Oct 8, 2021
To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined s...
Humans Neural Networks, Computer Prognosis Retrospective Studies Female Follow-Up Studies Cytological Techniques Cytodiagnosis Observer Variation Biopsy, Fine-Needle Adenocarcinoma, Follicular Male Thyroid Neoplasms Middle Aged Ultrasonography Thyroid Nodule
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