Latest AI and machine learning research in thoracic surgery for healthcare professionals.
OBJECTIVE: To develop and evaluate a suturing simulator using Artificial intelligence (AI)-driven computer vision for objective, high-resolution assessment of technical performance in cardiothoracic surgery, applied to a novel 3D-printed simulator across different levels of surgical expertise. DESIGN: Prospective study using a novel 3D-printed suturing simulator with targets positioned at multiple...
BACKGROUND: Traditional morbidity and mortality (M&M) conferences incompletely capture postoperative complications, potentially limiting quality improvement efforts. We developed and validated the Automated Surveillance of Postoperative Infectious and Non-Infectious Complications (ASPIN), a machine-learning system that estimates postoperative complication rates from electronic health record data, ...
Purpose To develop a deep learning (DL) algorithm for identification of cardiac chamber enlargement (CCE) on anteroposterior chest radiographs using s...
OBJECTIVES: This paper aims to design an explainable machine learning model capable of predicting postoperative quality of life in elderly NSCLC patie...
BACKGROUND: Anastomotic leakage remains a critical complication following esophagectomy, occurring in 8-20% of patients. While indocyanine green fluor...
Cardiomegaly screening via manual Cardiothoracic Ratio (CTR) measurement remains a clinical bottleneck, while contemporary deep learning solutions oft...
BACKGROUND: Postoperative atrial fibrillation (POAF) is a common and serious complication following video-assisted thoracoscopic surgery (VATS), which...
BACKGROUND: Curative treatment of resectable esophageal cancer comprises neoadjuvant chemoradiotherapy and esophagectomy. Robot-assisted minimally inv...
Spread through air spaces (STAS) is a recently recognized pattern of invasion in lung cancer that is strongly linked to postoperative recurrence and p...
INTRODUCTION: Conventional risk scores like EuroSCORE II and Society of Thoracic Surgeons models, derived from logistic regression, may not fully repr...
The increasing complexity of cardiovascular procedures, regulatory constraints, and heightened patient safety requirements have necessitated a fundame...
Purpose To evaluate the accuracy and time efficiency of a deep learning (DL)-based tool for automated quantification of functional small airway diseas...
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is the standard minimally invasive modality for mediastinal staging in no...
BACKGROUND: Although an artificial intelligence-driven three-dimensional reconstruction system (AI-3D) facilitates preoperative planning, its impact o...
BACKGROUND: Postoperative pulmonary complications (PPCs), including pneumonia, acute lung injury, and acute respiratory distress syndrome, are common ...
Canadian radiology continues to produce scholarship that is technically sophisticated, clinically relevant, and increasingly attentive to the wider sy...
Fluid overload is common after neonatal congenital cardiac surgery (CCS) and is frequently managed with continuous furosemide infusions requiring iter...
This special article represents the eighth installment in an annual Journal of Cardiothoracic and Vascular Anesthesia series highlighting key advances...
Existing methods of grading atelectasis are typically subjective and not scalable. We aimed to develop an automated, deep learning-based framework to ...
PURPOSE: Despite recent advances in preoperative work-up of drug resistant medial temporal lobe epilepsy (MTLE), predicting post-surgical seizure and ...