Thoracic Surgery

AI-based measurement of cardiothoracic ratio in chest X-rays and prediction of echocardiographic congestive heart failure.

BACKGROUND: This study presents an artificial intelligence (AI) model for automated cardiothoracic ratio (CTR) measurement from chest X-rays (CXRs) and evaluates its association with severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) diagnosed by echocardiography. The study also assesses CTR's prognostic value for predicting future SLVH/DLV development.

The Year in Perioperative Echocardiography: Selected Highlights from 2024.

This article is the ninth of an annual series reviewing the research highlights of the year pertaining to the subspecialty of perioperative echocardiography for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the editor-in-chief, Dr. Kaplan, and the editorial board for the opportunity to continue this series. In most cases, these will be research articles that are targeted...

Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.

BACKGROUND: Robotic-assisted minimally invasive esophagectomy (RAMIE) is a complex surgical procedure for treating esophageal cancer. Artificial intel...

Letter to the editor regarding the article "learning curve for robot-assisted Mckeown esophagectomy in patients with thoracic esophageal cancer".

Early prediction of 30-day mortality in patients with surgical wound infections following cardiothoracic surgery: Development and validation of the SWICS-30 score utilizing conventional logistic regression and artificial neural network.

INTRODUCTION: We aimed to create and validate the 30-day prognostic score for mortality in patients with surgical wound infection (SWICS-30) after car...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred for a week's history of dysphagia. Upper gastrointes...

Prognostic Impact of Tumor Cell Nuclear Size Assessed by Artificial Intelligence in Esophageal Squamous Cell Carcinoma.

Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma ...

Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.

The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlar...

Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy.

BACKGROUND: The purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative ...

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.

BACKGROUND AND OBJECTIVE: Diagnosis of pathology in the mediastinum has proven quite challenging, given the wide variability of both benign and malign...

Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning.

Echocardiography is the gold standard of diagnosis and evaluation of patent ductus arteriosus (PDA), a common condition among preterm infants that can...

Preoperative markers for identifying CT ≤2 cm solid nodules of lung adenocarcinoma based on image deep learning.

BACKGROUND: The solid pattern is a highly malignant subtype of lung adenocarcinoma. In the current era of transitioning from lobectomy to sublobar res...

Usefulness of an Artificial Intelligence Model in Recognizing Recurrent Laryngeal Nerves During Robot-Assisted Minimally Invasive Esophagectomy.

BACKGROUND: Recurrent laryngeal nerve (RLN) palsy is a common complication in esophagectomy and its main risk factor is reportedly intraoperative proc...

Deep learning prediction of survival in patients with heart failure using chest radiographs.

Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiogr...

Prediction of Anastomotic Leakage in Esophageal Cancer Surgery: A Multimodal Machine Learning Model Integrating Imaging and Clinical Data.

RATIONALE AND OBJECTIVES: Surgery in combination with chemo/radiotherapy is the standard treatment for locally advanced esophageal cancer. Even after ...

Postoperative leukocyte counts as a surrogate for surgical stress response in matched robot- and video-assisted thoracoscopic surgery cohorts of patients: A preliminary report.

The objective is to preliminary evaluated postoperative leukocyte counts as a surrogate for the surgical stress response in NSCLC patients who underwe...

Long-term quality of life after hybrid robot-assisted and open Ivor Lewis esophagectomy for esophageal cancer in a single center: a comparative analysis.

PURPOSE: Due to improved survival of esophageal cancer patients, long-term quality of life (QoL) is increasingly gaining importance. The aim of this s...

Long-term outcomes of robot-assisted versus minimally invasive esophagectomy in patients with thoracic esophageal cancer: a propensity score-matched study.

BACKGROUND: Recently, robot-assisted minimally invasive esophagectomy (RAMIE) has gained popularity worldwide. Some studies have compared the long-ter...

A Pilot Study Using Machine-learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery.

OBJECTIVE: To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complicat...

Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio.

The successful integration of neural networks in a clinical setting is still uncommon despite major successes achieved by artificial intelligence in o...