Objective: To demonstrate the performance benefit of the Automatic Scene Classifier (SCAN) algorithm available in the Nucleus 6 (CP900 series) sound processor over the default processing algorithms of the previous generation Nucleus 5 (CP810) and Freedom Hybrid™ sound processors.
Methods: Eighty-two cochlear implant recipients (40 Nucleus 5 processor users and 42 Freedom Hybrid processor users) listened to and repeated AzBio sentences in noise with their current processor and with the Nucleus 6 processor.
Results: The SCAN algorithm when enabled yielded statistically significant non-inferior and superior performance when compared to the Nucleus 5 and Freedom Hybrid sound processors programmed with ASC + ADRO.
Conclusion: The results of these studies demonstrate the superior performance and clinical utility of the SCAN algorithm in the Nucleus 6 processor over the Nucleus 5 and Freedom Hybrid processors.
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http://dx.doi.org/10.1016/j.joto.2015.09.001 | DOI Listing |
Sci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFRadiography (Lond)
January 2025
Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany.
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.
Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.
PLoS One
January 2025
Department of Computer Science, National Textile University, Faisalabad, Pakistan.
Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Korea University, Sejong, Sejong, Korea, Republic of (South).
Background: Amyloid-β accumulation is a pivotal factor in Alzheimer's disease (AD) progression. As treatment for AD has not been successful yet, the most effective approach lies in early diagnosis and the subsequent delay of disease progression. Hence, this study introduces a deep learning model to predict amyloid-β accumulation in the brain.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
New York University, New York, NY, USA.
Background: Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. We previously identified a genetic heterogeneity across two levels.
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