AIMC Topic: Radiography

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Development of a computer-aided quality assurance support system for identifying hand X-ray image direction using deep convolutional neural network.

Radiological physics and technology Aug 24, 2022
The convenience of imaging has improved with digitization; however, there has been no progress in the methods used to prevent human error. Therefore, radiographic incidents and accidents are not prevented. In Japan, image interpretation is conducted ...
Computers X-Rays Tomography, X-Ray Computed Neural Networks, Computer Image Processing, Computer-Assisted Humans Radiography
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Classification of Musculoskeletal Radiograph Requisition Appropriateness Using Machine Learning.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Aug 23, 2022
Poor quality imaging requisitions lower report quality and impede good patient care. Manual control of such requisitions is time consuming and can be a source of friction with referring physicians. The purpose of this study was to determine if poor ...
Radiography Bayes Theorem Machine Learning Humans
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How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

European radiology Aug 18, 2022
OBJECTIVES: How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow?
Radiography Radiology Workflow Data Collection Humans Artificial Intelligence
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Machine Learning Model Drift: Predicting Diagnostic Imaging Follow-Up as a Case Example.

Journal of the American College of Radiology : JACR Aug 16, 2022
OBJECTIVE: Address model drift in a machine learning (ML) model for predicting diagnostic imaging follow-up using data augmentation with more recent data versus retraining new predictive models.
Machine Learning Algorithms Retrospective Studies Follow-Up Studies Radiography
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Patient communication in radiology: Moving up the agenda.

European journal of radiology Aug 12, 2022
Optimised communication between patients and the imaging team is an essential component of providing patient-centred and value-based care. Communication with patients can be challenging in the setting of busy radiology departments where there is a fo...
Artificial Intelligence Radiography Humans Radiologists Radiology Communication
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Predicting Patient Demographics From Chest Radiographs With Deep Learning.

Journal of the American College of Radiology : JACR Aug 11, 2022
BACKGROUND: Deep learning models are increasingly informing medical decision making, for instance, in the detection of acute intracranial hemorrhage and pulmonary embolism. However, many models are trained on medical image databases that poorly repre...
Radiography Ethnicity Radiography, Thoracic Artificial Intelligence Deep Learning Humans
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iCVM: An Interpretable Deep Learning Model for CVM Assessment Under Label Uncertainty.

IEEE journal of biomedical and health informatics Aug 11, 2022
The Cervical Vertebral Maturation (CVM) method aims to determine the craniofacial skeletal maturational stage, which is crucial for orthodontic and orthopedic treatment. In this paper, we explore the potential of deep learning for automatic CVM asses...
Male Uncertainty Deep Learning Age Determination by Skeleton Radiography Cervical Vertebrae Humans Female
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Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.

Journal of digital imaging Aug 8, 2022
Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning ...
COVID-19 Retrospective Studies Artificial Intelligence Deep Learning Radiography Humans
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Addressing fairness in artificial intelligence for medical imaging.

Nature communications Aug 6, 2022
A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here w...
Artificial Intelligence Radiography Diagnostic Imaging
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Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett's analysis: can we trust a bot to do that?

Dento maxillo facial radiology Aug 5, 2022
OBJECTIVE: To assess the reliability of CEFBOT, an artificial intelligence (AI)-based cephalometry software, for cephalometric landmark annotation and linear and angular measurements according to Arnett's analysis.
Humans Artificial Intelligence Radiography Cephalometry Software Reproducibility of Results
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