Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathematics, and several other fields. Recently, with the rapid development of machine learning (ML) and deep learning (DL), significant progress has been made in the field of AI. As one of the fastest-growing branches, DL can effectively extract features from big data and optimize the performance of various tasks. Moreover, with advancements in digital imaging technology, DL has become a key tool for processing high-dimensional medical image data and conducting medical image analysis in clinical applications. With the development of this technology, the diagnosis of orthopedic diseases has undergone significant changes. In this review, we describe recent research progress on DL in fracture diagnosis and discuss the value of DL in this field, providing a reference for better integration and development of DL technology in orthopedics.
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http://dx.doi.org/10.1007/s11596-024-2928-5 | DOI Listing |
Background: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program.
View Article and Find Full Text PDFLecanemab, a humanized IgG1 monoclonal antibody that binds with high affinity to amyloid-beta (Aβ) protofibrils, was formally evaluated as a treatment for early Alzheimer's disease in a phase 2 study (Study 201) and the phase 3 Clarity AD study. These trials both included an 18-month, randomized study (core) and an open-label extension (OLE) phase where eligible participants received open-label lecanemab for up to 30 months to date. Clinical (CDR-SB, ADAS-Cog14, and ADCS-MCI-ADL), biomarker (PET, Aβ42/40 ratio, and ptau181) and safety outcomes were evaluated.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Relecura, Bangalore, karnataka, India.
Background: Clinical Dementia Rating (CDR) and its evaluation have been important nowadays as its prevalence in older ages after 60 years. Early identification of dementia can help the world to take preventive measures as most of them are treatable. The cellular Automata (CA) framework is a powerful tool in analyzing brain dynamics and modeling the prognosis of Alzheimer's disease.
View Article and Find Full Text PDFBackground: Lecanemab is a humanized IgG1 monoclonal antibody binding with high affinity to protofibrils of amyloid-beta (Aβ) protein. In clinical studies, lecanemab has been shown to reduce markers of amyloid in early symptomatic Alzheimer's disease (AD) and slow decline on clinical endpoints of cognition and function. Herein, a modeling approach was used to correlate amyloid reduction with change in rate of AD progression.
View Article and Find Full Text PDFBackground: Lecanemab is a humanized IgG1 monoclonal antibody that binds with high affinity to Aβ soluble protofibrils. In two clinical study evaluations of lecanemab, Clarity AD (NCT03887455) and lecanemab phase 2 study (Study 201, NCT01767311), the drug showed statistically significant reduction in disease progression during 18 months of treatment relative to placebo. Anti-amyloid immunotherapy can result in higher rates of "pseudo-atrophy" (ie, whole brain volume loss or ventricular enlargement) relative to disease progression observed in placebo-treated subjects.
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