Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Nov 3, 2020
PURPOSE: The application of robotics in the operating theatre for total knee arthroplasty (TKA) remains controversial. As with all new technology, the introduction of new systems is associated with a learning curve and potentially associated with ext...
OBJECTIVE: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms.
International journal of urology : official journal of the Japanese Urological Association
Nov 3, 2020
OBJECTIVE: To evaluate the quality of recovery in patients who underwent robot-assisted partial nephrectomy and to compare the outcomes of the transperitoneal or retroperitoneal approach.
Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral a...
INTRODUCTION: The learning curve associated with robotic pancreatoduodenectomy (RPD) is a hurdle for new programs to achieve optimal results. Since early analysis, robotic training has recently expanded, and the RPD approach has been refined. The pur...
IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.
IMPORTANCE: Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotio...
IMPORTANCE: Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored.
IMPORTANCE: Machine-learning algorithms offer better predictive accuracy than traditional prognostic models but are too complex and opaque for clinical use.
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