AIMC Journal:
Academic radiology

Showing 271 to 280 of 317 articles

Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.

Academic radiology Dec 7, 2019
RATIONALE AND OBJECTIVES: To evaluate the noninvasive predictive performance of deep learning features based on staging CT for sentinel lymph node (SLN) metastasis of breast cancer.
Humans Tomography, X-Ray Computed Lymph Nodes Lymphatic Metastasis Sentinel Lymph Node Biopsy Breast Neoplasms Deep Learning
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Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.

Academic radiology Nov 25, 2019
RATIONALE AND OBJECTIVES: We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate i...
Humans Tomography, X-Ray Computed Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Deep Learning Lung Neoplasms
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Artificial Intelligence, Radiology, and Tuberculosis: A Review.

Academic radiology Nov 20, 2019
Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advance...
Humans Diagnosis, Computer-Assisted Radiology Artificial Intelligence Radiography Tuberculosis
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Exploring Large-scale Public Medical Image Datasets.

Academic radiology Nov 6, 2019
RATIONALE AND OBJECTIVES: Medical artificial intelligence systems are dependent on well characterized large-scale datasets. Recently released public datasets have been of great interest to the field, but pose specific challenges due to the disconnect...
Documentation Radiology Radiologists Artificial Intelligence Sensitivity and Specificity Humans
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The Importance of Imaging Informatics and Informaticists in the Implementation of AI.

Academic radiology Oct 19, 2019
Imaging informatics is critical to the success of AI implementation in radiology. An imaging informaticist is a unique individual who sits at the intersection of clinical radiology, data science, and information technology. With the ability to unders...
Radiology Information Systems Radiography Radiology Artificial Intelligence
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Artificial Intelligence in Medicine: Where Are We Now?

Academic radiology Oct 19, 2019
Artificial intelligence in medicine has made dramatic progress in recent years. However, much of this progress is seemingly scattered, lacking a cohesive structure for the discerning observer. In this article, we will provide an up-to-date review of ...
Pathology Ophthalmology Dermatology Artificial Intelligence Radiography Radiology Forecasting
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Essential Elements of Natural Language Processing: What the Radiologist Should Know.

Academic radiology Sep 17, 2019
Natural language is ubiquitous in the workflow of medical imaging. Radiologists create and consume free text in their daily work, some of which can be amenable to enhancements through automatic processing. Recent advancements in deep learning and "ar...
Radiologists Neural Networks, Computer Artificial Intelligence Natural Language Processing Radiology Humans Machine Learning
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Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT.

Academic radiology Sep 16, 2019
PURPOSE: Accurate pancreas segmentation has application in surgical planning, assessment of diabetes, and detection and analysis of pancreatic tumors. Factors that affect pancreas segmentation accuracy have not been previously reported. The purpose o...
Humans Image Processing, Computer-Assisted Pancreas Tomography, X-Ray Computed Female Neural Networks, Computer Male Deep Learning
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Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method.

Academic radiology Jul 17, 2019
BACKGROUND: The purpose of this study was to develop a deep learning classification approach to distinguish cancerous from noncancerous regions within optical coherence tomography (OCT) images of breast tissue for potential use in an intraoperative s...
Mastectomy, Segmental Postoperative Period Margins of Excision Tomography, Optical Coherence Breast Breast Neoplasms Humans Algorithms Image Processing, Computer-Assisted Neural Networks, Computer Female Magnetic Resonance Imaging
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Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.

Academic radiology Jul 17, 2019
RATIONALE AND OBJECTIVES: To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia.
Female Radiation Dosage Algorithms Middle Aged Tomography, X-Ray Computed Humans Male Aged Paraspinal Muscles Sarcopenia Machine Learning
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