AIMC Topic: Postoperative Period

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Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.

Medicina (Kaunas, Lithuania)
: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it diff...

A spatio-temporal network for video semantic segmentation in surgical videos.

International journal of computer assisted radiology and surgery
PURPOSE: Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Models need to provide accurate predictions since temporally inconsistent identification of anatomy can h...

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single de...

Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system.

Scientific reports
Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for earl...

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...

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 (...

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 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...