PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of...
PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the developme...
PURPOSE: Most robot-assisted thoracoscopic surgery (RATS) is performed from the vertical view. This study evaluates the initial outcomes of our novel confronting RATS technique, in which the patient was viewed horizontally, as in open thoracotomy.
Recent advances in optical and robotic technologies have given surgeons high-definition eyes and precision hands that perform beyond human capabilities. This has expanded the scope of minimally invasive surgery and increased opportunities for surgery...
PURPOSE: The accuracy of lymph node (LN) dissection in robotic surgery for lung cancer remains controversial. We compared the accuracy of LN dissection in robot-assisted thoracic surgery (RATS) vs. video-assisted thoracic surgery (VATS).
A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction ...