AIMC Topic: Humans

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Study on the correlation between early three-dimensional gait analysis and clinical efficacy after robot-assisted total knee arthroplasty.

Chinese journal of traumatology = Zhonghua chuang shang za zhi
PURPOSE: Robot-assisted technology is a forefront of surgical innovation that improves the accuracy of total knee arthroplasty (TKA). But whether the accuracy of surgery can improve the clinical efficacy still needs further research. The purpose of t...

Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.

Academic radiology
RATIONALE AND OBJECTIVES: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool.

The learning curve for open and minimally-invasive kidney transplantation: a systematic review.

Minerva urology and nephrology
INTRODUCTION: There is lack of evidence on the impact of surgeons' learning curve on postoperative outcomes after open (OKT) or minimally-invasive (robot-assisted) kidney transplantation (RAKT). The aim of the review was to assess the learning curve ...

Are nephrometry scores accurate for the prediction of outcomes in patients with renal angiomyolipoma treated with robot-assisted partial nephrectomy? A multi-institutional analysis.

Minerva urology and nephrology
BACKGROUND: Prediction of complications and surgical outcomes is of outmost importance even in patients with benign renal masses. The aim of our study is to test the PADUA, SPARE and R.E.N.A.L. scores to predict nephron sparing surgery (NSS) outcomes...

Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.

Investigative radiology
OBJECTIVES: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can ...

Analysis of robot-assisted nipple-sparing mastectomy using the da Vinci SP system.

Journal of surgical oncology
BACKGROUND: As patients tend to be diagnosed with breast cancer at an early stage, the demand for better cosmetic outcomes has increased. Several studies revealed that robot-assisted nipple-sparing mastectomy (RNSM) shows favorable outcomes. The aim ...

Trivariate Linear Regression and Machine Learning Prediction of Possible Roles of Efflux Transporters in Estimated Intestinal Permeability Values of 301 Disparate Chemicals.

Biological & pharmaceutical bulletin
A system for predicting apparent bidirectional permeability (P) across Caco-2 cells of diverse chemicals has been reported. The present study aimed to investigate the relationship between in silico-generated P (from apical to basal side, P) for 301 s...

Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing.

Journal of tissue engineering and regenerative medicine
Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remain...

Robotically performed diagnostic coronary angiography.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
OBJECTIVE: This study was performed to investigate the efficacy and safety of robotic diagnostic coronary angiography.

Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

CPT: pharmacometrics & systems pharmacology
Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-lear...