Publications by authors named "Sigmund Dick"
Background: Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis.
Objective: This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM.
Article Synopsis
- Deep learning models were developed to automate the detection and classification of gastric neoplasms during endoscopic procedures, using a dataset of 5,017 endoscopic images.
- The study validated the clinical decision support system (CDSS) through various tests, finding a high lesion detection rate (95.6%) and good classification accuracy (around 89%) in identifying types of gastric cancer and invasion depth.
- Results showed CDSS-assisted endoscopies had a slightly higher detection rate compared to traditional methods, suggesting promising real-world applications, though further statistical significance was not established.
Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA).
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