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Postoperative Period

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Active robotic technologies for total knee arthroplasty.

Archives of orthopaedic and trauma surgery
INTRODUCTION: When active robotic technologies for Total Knee Arthroplasty (TKA) were introduced over 20 years ago, broad usage of robotic technology was not felt to be needed as early data suggested no clear improvement in clinical outcomes compared...

Predicting post-operative right ventricular failure using video-based deep learning.

Nature communications
Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. A...

Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items.

Journal of the American College of Surgeons
BACKGROUND: Retained surgical items are a serious human error. Surgical sponges account for 70% of retained surgical items. To prevent retained surgical sponges, it is important to establish a system that can identify errors and avoid the occurrence ...

Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data.

Studies in health technology and informatics
This article describes the results of feature extraction from unstructured medical records and prediction of postoperative complications for patients with thoracic aortic aneurysm operations using machine learning algorithms. The datasets from two di...

Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (...

Machine learning for classification of postoperative patient status using standardized medical data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from real-world data analyses. Standardization and structuring of data are necessary to analy...

Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentatio...

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment.

Clinical orthopaedics and related research
BACKGROUND: The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on inte...