AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT.

Korean journal of radiology Aug 19, 2021
OBJECTIVE: The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT.
Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Humans Middle Aged Retrospective Studies Algorithms Deep Learning Tomography, X-Ray Computed Pilot Projects Female Male
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Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

AJR. American journal of roentgenology Aug 18, 2021
Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. The purpose of this article is to compare the utility of fully automated...
Humans Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Sarcopenia Spine Tomography, X-Ray Computed Muscle, Skeletal Middle Aged Female Predictive Value of Tests Risk Assessment Reproducibility of Results Deep Learning Male Follow-Up Studies
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Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning.

Journal of healthcare engineering Aug 17, 2021
Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient's survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appe...
Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Precancerous Conditions Humans Artificial Intelligence Lung Tomography, X-Ray Computed Deep Learning Lung Neoplasms
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Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study.

European radiology Aug 14, 2021
OBJECTIVES: To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning-based image reconstruction algorithm (DLR, TrueFidelity™).
Radiation Dosage Phantoms, Imaging Drug Tapering Humans Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted
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Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital.

Clinical radiology Aug 14, 2021
AIM: To evaluate a deep-learning-based computer-aided detection (DL-CAD) software system for pulmonary nodule detection on computed tomography (CT) images and assess its added value in the clinical practice of a large teaching hospital.
Tomography, X-Ray Computed Female Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Aged Hospitals, Teaching Aged, 80 and over Young Adult Adult Sensitivity and Specificity Male Humans Deep Learning Middle Aged Retrospective Studies Adolescent Lung Multiple Pulmonary Nodules Solitary Pulmonary Nodule
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Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

AJR. American journal of roentgenology Aug 11, 2021
Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conservin...
Breast Neoplasm Recurrence, Local Mastectomy, Segmental Aged Aged, 80 and over Young Adult Adult Breast Neoplasms Middle Aged Mammography Radiographic Image Interpretation, Computer-Assisted Humans Artificial Intelligence Retrospective Studies Female
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Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

Acta radiologica (Stockholm, Sweden : 1987) Aug 9, 2021
BACKGROUND: Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.
Humans Algorithms Retrospective Studies Tomography, X-Ray Computed Urolithiasis Urinary Calculi Deep Learning Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Radiation Dosage
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Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin Aug 5, 2021
PURPOSE:  Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary ...
Retrospective Studies Female Sensitivity and Specificity Male Artificial Intelligence Algorithms Computers Humans Radiographic Image Interpretation, Computer-Assisted Pulmonary Embolism
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Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray.

BMC urology Aug 5, 2021
BACKGROUND: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis usi...
Humans Neural Networks, Computer Diagnosis, Computer-Assisted Deep Learning Adolescent Radiography Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Urinary Calculi Female Aged Aged, 80 and over Adult Sensitivity and Specificity Male Middle Aged
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Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.

The British journal of radiology Aug 4, 2021
OBJECTIVE: A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lun...
Tomography, X-Ray Computed Humans Imaging, Three-Dimensional Neural Networks, Computer Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted
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