With the previous experiences in performing laparoscopic for over a period of 15 years and da Vinci colorectal surgeries from 2010 to 2013, we started operating using the Cambridge Medical Robotics (CMR) Versius Surgical Robot System. The aim of the ...
International journal of urology : official journal of the Japanese Urological Association
Jul 19, 2021
OBJECTIVES: To compare functional and surgical outcomes of robot-assisted partial nephrectomy for complex tumors with RENAL scores ≥10 and non-complex tumors at a single academic institution.
BACKGROUND: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning...
BACKGROUND: Surgical treatment of benign liver diseases (BLD) remains a field of conflict, due to increased risk and high complication rate. However, the introduction of minimally invasive surgery has led to increased number of patients with BLD bein...
Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal fore...
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Jul 17, 2021
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particul...
OBJECTIVES: To report the functional outcomes of robot-assisted laparoscopic artificial urinary sphincter implantation (R-AUS) in men with neurogenic stress urinary incontinence (SUI).
Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here w...
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.
Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study...
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