View clinical trials related to Deep Learning.
Filter by:Early assessment of pancreatic exocrine insufficiency (PEI) is crucial for determining appropriate chronic pancreatitis (CP) treatment plans, thereby avoiding unnecessary suffering and further complications in patients. A total of 504 patients with CP who underwent fecal elastase-1 test and contrast-enhanced CT at Changhai Hospital between January 2018 and April 2023 were enrolled in this study. The investigators aim to establish a fully automated workflow to establish a PEI classification model based on radiomic features, semantic features and deep learning features on enhanced CT images for evaluating the severity of PEI.
Here, this study aimed to develop an automated system for detecting and diagnosing lesion DRGs in PHN patients based on deep learning. This study retrospectively analyzed the DRG images of all patients with postherpetic neuralgia who underwent magnetic resonance neuroimaging examinations in our radiology department from January 2021 to February 2022. After image post-processing, the You Only Look Once (YOLO) version 8 was selected as the target algorithm model. Model performance was evaluated using metrics such as precision, recall, Average Precision, mean average precision and F1 score.
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Radiomics provides internal features of lesions for precise diagnosis, prognosis prediction, and personalized treatment planning. Early and accurate diagnosis of renal tumors is crucial, but it's challenging due to morphological and pathological overlap between benign and malignant lesions. The accurate diagnosis of RCC, especially for small tumors, remains a significant challenge. Recent studies have shown a relationship between body composition, obesity, and renal tumors. Common indicators like body weight and BMI fail to reflect body composition accurately. Research on the role of body composition, including adipose tissue, in tumor pathology could improve clinical diagnosis and treatment planning.
In this study, the AI-assisted system EndoAngel has the functions of reminding the ileocecal junction, withdrawal time, withdrawal speed, sliding lens, polyps in the field of vision, etc. These functions can assist novice endoscopists in performing colonoscopy and improve the quality.
Artificial Intelligence (AI) to predict the histology of polyps per colonoscopy, offers a promising solution to reduce variation in colonoscopy performance. This new and innovative non-invasive technology will improve the quality of screening colonoscopies, and reduce the costs of colorectal cancer screening. The aim of the study is to performed a cross-sectional, multi-center study evaluating the diagnostic performance of the CAD EYE automatic characterization system for the histology of colonic polyps in colorectal cancer screening colonoscopy.
Primary objective of this study is the development and validation of a system of deep neural networks which automatically detects and classifies blinks as "complete" or "incomplete" in image sequences.