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Pneumothorax-II

Pneumothorax-II

Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:

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Related Experiment Video

Updated: Jun 29, 2025

Author Spotlight: A Non-Intubated Video-Assisted Thoracoscopic Surgery with Multimodal Analgesia and Sevoflurane Inhalation Anesthesia

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A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm.

Cheng-Mao Zhou^1,2, Qiong Xue³, HuiJuan Li³

¹Big Data and Artificial Intelligence Research Group, Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China. zhouchengmao187@foxmail.com.

Scientific Reports

|March 26, 2024

Summary

Machine learning models predict pulmonary complications after thoracoscopic surgery. The pruning Bayesian neural network shows promise for identifying high-risk patients before their procedure.

Keywords:

Deep learning LGBM Machine learning PPCs Prediction

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Area of Science:

Medical Informatics
Thoracic Surgery
Artificial Intelligence in Medicine

Background:

Postoperative pulmonary complications (PPCs) are a significant concern after thoracoscopic surgery.
Accurate prediction of PPCs can aid in risk stratification and patient management.

Purpose of the Study:

To develop and evaluate machine learning and deep learning models for early prediction of PPCs after thoracoscopic surgery.
To identify key factors associated with PPCs.

Main Methods:

Construction of artificial intelligence (AI) prediction models using Python, incorporating machine learning and deep learning algorithms.
Correlation analysis to identify factors associated with PPCs.
Evaluation of various AI algorithms including Logistic Regression, Light Gradient Boosting Machine (LGBM), and Pruning Bayesian Neural Network (PBNN).

Main Results:

Age, surgery duration, and serum albumin were correlated with PPCs.
Key predictive factors identified by LGBM included single lung ventilation duration, smoking history, surgery duration, ASA score, and blood glucose.
Pruning Bayesian Neural Network (PBNN) demonstrated strong performance in AUC (0.869) and F1 score (0.566).

Conclusions:

AI models, particularly PBNN, can effectively predict the likelihood of PPCs after thoracoscopy.
These models can assist in identifying high-risk individuals preoperatively, enabling timely interventions.