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Postoperative Complications

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Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.

BMC medical informatics and decision making
OBJECTIVES: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of N...

Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.

British journal of anaesthesia
BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that ...

The usefulness of artificial intelligence in breast reconstruction: a systematic review.

Breast cancer (Tokyo, Japan)
BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to p...

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.

European journal of radiology
PURPOSE: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute unc...

Assessing the perioperative outcomes of abdominal drain omission after robot-assisted partial nephrectomy.

Scientific reports
The study aimed to evaluate the impact of abdominal drain placement (vs. omission) on perioperative outcomes of robot-assisted partial nephrectomy (RAPN), focusing on complications, time to canalization, deambulation, and pain management. A prospecti...

Machine learning for enhanced prognostication: predicting 30-day outcomes following posterior fossa decompression surgery for Chiari malformation type I in a pediatric cohort.

Journal of neurosurgery. Pediatrics
OBJECTIVE: Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- a...

Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review.

ANZ journal of surgery
Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML),...

Development and validation of machine learning models to predict postoperative infarction in moyamoya disease.

Journal of neurosurgery
OBJECTIVE: Cerebral infarction is a common complication in patients undergoing revascularization surgery for moyamoya disease (MMD). Although previous statistical evaluations have identified several risk factors for postoperative brain ischemia, the ...

Precise highlighting of the pancreas by semantic segmentation during robot-assisted gastrectomy: visual assistance with artificial intelligence for surgeons.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: A postoperative pancreatic fistula (POPF) is a critical complication of radical gastrectomy for gastric cancer, mainly because surgeons occasionally misrecognize the pancreas and fat during lymphadenectomy. Therefore, this study aimed to ...