AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Esophageal Neoplasms

Showing 51 to 60 of 250 articles

Clear Filters

Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer.

Thoracic cancer
BACKGROUND: The accuracy of artificial intelligence (AI) and experts in diagnosing early esophageal cancer (EC) and its infiltration depth was summarized and analyzed, thus identifying the advantages of AI over traditional manual diagnosis, with a vi...

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Science translational medicine
Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) fo...

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most...

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular for...

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

Langenbeck's archives of surgery
PURPOSE: Due to improved survival of esophageal cancer patients, long-term quality of life (QoL) is increasingly gaining importance. The aim of this study is to compare QoL outcomes between open Ivor Lewis esophagectomy (Open-E) and a hybrid approach...

Deep-learning-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics.

Journal of biomedical optics
SIGNIFICANCE: Endoscopic screening for esophageal cancer (EC) may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving specificity, the limited field of view () significantly reduces the...

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial.

Endoscopy
BACKGROUND: This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE).

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

World journal of surgical oncology
BACKGROUND: Recently, robot-assisted minimally invasive esophagectomy (RAMIE) has gained popularity worldwide. Some studies have compared the long-term results of RAMIE and minimally invasive esophagectomy (MIE). However, there are no reports on the ...

Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology.

Nature communications
Timely detection of Barrett's esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal me...

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

International journal of radiation oncology, biology, physics
PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation ther...