AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image.

BioMed research international Dec 16, 2020
Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when m...
Lung Humans Algorithms Deep Learning Tomography, X-Ray Computed Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted
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A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions.

The British journal of radiology Dec 11, 2020
OBJECTIVE: To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT.
Aged, 80 and over Adult Male Middle Aged Female Humans Reproducibility of Results Prospective Studies Aged Tomography, X-Ray Computed Liver Radiographic Image Interpretation, Computer-Assisted Deep Learning Radiation Dosage Liver Neoplasms Radiographic Image Enhancement Contrast Media
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Deep Learning CT Image Reconstruction in Clinical Practice.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin Dec 10, 2020
BACKGROUND:  Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomica...
Reproducibility of Results Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Humans Algorithms Tomography, X-Ray Computed
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Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation.

Journal of vascular surgery Dec 9, 2020
OBJECTIVE: Imaging software has become critical tools in the diagnosis and decision making for the treatment of abdominal aortic aneurysms (AAA). However, the interobserver reproducibility of the maximum cross-section diameter is poor. This study aim...
Humans Retrospective Studies Reproducibility of Results Deep Learning Radiographic Image Interpretation, Computer-Assisted Workflow Observer Variation Automation Predictive Value of Tests Aortic Aneurysm, Abdominal Anatomic Landmarks Aortography Multidetector Computed Tomography Computed Tomography Angiography Software Design
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Iterative reconstruction and deep learning algorithms for enabling low-dose computed tomography in midfacial trauma.

Oral surgery, oral medicine, oral pathology and oral radiology Dec 8, 2020
OBJECTIVES: The objective of this study was to quantitatively assess the image quality of Advanced Modeled Iterative Reconstruction (ADMIRE) and the PixelShine (PS) deep learning algorithm for the optimization of low-dose computed tomography protocol...
Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Signal-To-Noise Ratio Humans Algorithms Tomography, X-Ray Computed Deep Learning
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Hypergraph learning for identification of COVID-19 with CT imaging.

Medical image analysis Nov 26, 2020
The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-1...
Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Community-Acquired Infections Pneumonia, Viral Tomography, X-Ray Computed SARS-CoV-2 Diagnosis, Computer-Assisted Humans China Machine Learning COVID-19
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The Potential of Artificial Intelligence to Analyze Chest Radiographs for Signs of COVID-19 Pneumonia.

Radiology Nov 24, 2020
Algorithms SARS-CoV-2 Radiographic Image Interpretation, Computer-Assisted Humans Artificial Intelligence COVID-19
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DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.

Radiology Nov 24, 2020
Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital s...
Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Radiography, Thoracic Male Humans Middle Aged Artificial Intelligence Lung COVID-19 United States Algorithms Female SARS-CoV-2 Sensitivity and Specificity
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Artificial intelligence in image reconstruction: The change is here.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Nov 24, 2020
Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and f...
Artificial Intelligence Algorithms Deep Learning Radiation Dosage Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted
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Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study.

PloS one Nov 24, 2020
The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians i...
Adult Humans Radiologists Sensitivity and Specificity COVID-19 Tomography, X-Ray Computed SARS-CoV-2 Deep Learning Male Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Aged Reverse Transcriptase Polymerase Chain Reaction Republic of Korea Data Accuracy Middle Aged Aged, 80 and over Triage Female Radiography, Thoracic Reproducibility of Results
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