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Xpress Digital Mammography System Images For Computer Aided Detection Development PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Xpress Digital Mammography System Images For Computer Aided Detection Development articles that have been published worldwide.
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Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. It aims to decrease ov...
Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique used for detection of breast cancer in women and also to improve the breast cancer prognosis. The numbers of images need to be examined by the radiologists, the resulting may be misdiagnosis due to human errors by visual Fatigue. In order to avoid human errors, Computer Aided Diag...
The aim of this study was to evaluate the detection rate and diagnostic performance of 2-dimensional synthetic mammography (SM) as an adjunct to wide-angle digital breast tomosynthesis (WA-DBT) compared with digital mammography (DM) alone or to DM in combination with WA-DBT.
We present a comparison between full field digital mammography and synthetic mammography, performed on several mammography systems from four different manufacturers. The analysis is carried out on both the digital and synthetic images of two commercially available mammography phantoms, and focuses on a set of objective metrics that encode the geometrical appearance of imaging features of diagnostic interest. In particular, we measured sizes and contrasts of several clusters of microcalcification specks, sha...
To investigate the sensitivity and specificity of dig¬ital mammography (DM) and digital breast tomosynthesis (DBT) for the detection of breast cancer in comparison to histopathology findings.
To compare the observer agreement of microcalcification detection on synthetic 2D images to full field digital mammography (FFDM) at screening and determine if calcifications can be detected to the same degree and given the same BI-RADS assessment.
Purpose To compare the performance of two-dimensional synthetic mammography (SM) plus digital breast tomosynthesis (DBT) versus conventional full-field digital mammography (FFDM) in the detection of microcalcifications on screening mammograms. Materials and Methods In this retrospective multireader observer study, 72 consecutive screening mammograms recalled for microcalcifications from June 2015 through August 2016 were evaluated with both FFDM and DBT. The data set included 54 mammograms with benign micro...
To compare the diagnostic performance and interpretation time of digital breast tomosynthesis (DBT) for both novice and experienced readers with and without using a computer-aided detection (CAD) system for concurrent read.
The workflow digital to aid the treatment of dentofacial deformities is a reality. Associated with the virtual planning, the creation of surgical guides assists the performance of osteotomies and bone positioning, increasing the accuracy of surgical outcomes. This study aims to present a new method of surgical guide for genioplasty based on the selected osteosynthesis plate.
The purpose of this study is to provide a more accurate estimation of the radiation dose of contrast-enhanced spectral mammography (CESM) relative to that of 2D digital mammography and tomosynthesis using phantom and patient data and an accepted dosimetry protocol that eliminates vendor-specific average glandular dose (AGD) estimates while including breast density.
The purpose of this article is to compare outcomes of findings seen on one view only from screening full-field digital mammography (FFDM) and FFDM plus digital breast tomosynthesis (DBT).
The purpose of this study was to compare diagnostic accuracy and interpretation time of screening automated breast ultrasound (ABUS) for women with dense breast tissue without and with use of a recently U.S. Food and Drug Administration-approved computer-aided detection (CAD) system for concurrent read.
The aim of this study was to evaluate the ability of computer-aided detection (CAD) and human readers to detect pulmonary nodules ≥5 mm using 100 kV ultra-low-dose computed tomography (ULDCT) utilizing a tin filter.
This work proposes a new reliable Computer Aided Diagnostic (CAD) system for the diagnosis of breast cancer from Breast Ultrasound (BUS) images. The system can be useful to reduce the number of biopsies and pathological tests, which are invasive, costly, and often unnecessary.
Central nervous system myelomatosis is uncommon and is associated with a particularly poor prognosis. PET images, from a 53-year-old man referred to a fully digital F-FDG PET for relapsed multiple myeloma, revealed high F-FDG uptakes located in the cortex and sulci of the right central area and within the meningeal envelopes of the cerebellum, the trigeminal nerves, and on the spinal canal. These particular uptakes gave evidence of a central nervous system myelomatosis subsequently confirmed by plasma cells...
Early detection of cancer can increase patients' survivability and treatment options. Medical images such as Mammogram, Ultrasound, Magnetic Resonance Imaging, and microscopic images are the common method for cancer diagnosis. Recently, computer-aided diagnosis (CAD) systems have been used to help physicians in cancer diagnosis so that the diagnosis accuracy can be improved. CAD can help in decreasing missed cancer lesions due to physician fatigue, reducing the burden of workload and data overloading, and d...
Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis...
In the post-genome era, a novel research field, 'radiomics' has been developed to offer a new viewpoint for the use of genotypes in radiology and medicine research which have traditionally focused on the analysis of imaging phenotypes. The present study analyzed brain morphological changes related to the individual's genotype. Our data consisted of magnetic resonance (MR) images of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), as well as their apolipoprotein E (APOE) genotypes....
Comparison of the Cepheid Xpert Xpress Flu/RSV Assay to in-house Flu/RSV triplex real-time RT-PCR for rapid molecular detection of Influenza A, Influenza B and Respiratory Syncytial Virus in respiratory specimens.
This study compared the performance of the commercially available Xpert Xpress Flu/RSV assay to an in-house FluAB/RSV triplex real-time RT-PCR assay for the detection of influenza A/B viruses and respiratory syncitial virus (RSV) from both nasopharyngeal aspirate (NPA) and nasopharyngeal flocked swab (NPS). A total of 20 external quality assurance (EQA) samples and 172 clinical respiratory samples were tested prospectively using both the Xpert Xpress Flu/RSV assay and the in-house FluAB/RSV triplex assay. F...
To compare the efficacy of use of digital breast tomosynthesis (DBT) with standard digital mammography (DM) workup views in the breast cancer assessment clinic.
To evaluate the tumour visibility and diagnostic performance of digital breast tomosynthesis (DBT) in patients with noncalcified T breast cancer.
Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets. On the other hand, the engineered features have been widely studied.
To evaluate the diagnostic accuracy of a digital fundus photographic system that consists of taking fundus photographs by a trained technician using a RetCam® shuttle and interpreting fundus images by an expert to detect Retinotapthy of Prematurity requiring treatment (ROP-RT) which defined as type I ROP according to the Early Treatment for ROP study (ETROP).