View clinical trials related to Deep Learning.
Filter by:Ultrasound (US) is a more cost-effective, accessible, and available imaging technique to assess anterior talofibular ligament (ATFL) injuries compared with magnetic resonance imaging (MRI). However, challenges in using this technique and increasing demand on qualified musculoskeletal (MSK) radiologists delay the diagnosis. The investigators have already developed a deep convolutional network (DCNN) model that automates detailed classification of ATFL injuries. The investigators hope to use the DCNN in real-world clinical setting to test its diagnostic accuracy.
Ultrasound (US) is a more cost-effective, accessible, and available imaging technique to assess anterior talofibular ligament (ATFL) injuries compared with magnetic resonance imaging (MRI). However, challenges in using this technique and increasing demand on qualified musculoskeletal (MSK) radiologists delay the diagnosis. Using datasets from multiple clinical centers, we aimed to develop and validate a deep convolutional network (DCNN) model that automates classification of ATFL injuries using US images with the goal of providing interpretable assistance to radiologists and facilitating a more accurate diagnosis of ATFL injuries. The investigators collected US images of ATFL injuries which had arthroscopic surgery results as reference standard form 13 hospitals across China;Then the investigators divided the images into training dataset, internal validation dataset, and external validation dataset in a ratio of 8:1:1; the investigators chose an optimal DCNN model to test its diagnostic performance of the model, including the diagnostic accuracy, sensitivity, specificity, F1 score. At last, the investigators compared the diagnostic performance of the model with 12 radiologists at different levels of expertise.
The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.
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.
This study (IEDAL study) intends to prospectively enroll more than 6800 patients who will undergo head CT angiography (CTA) scanning in the outpatient clinic. It will be carried out in 25 hospitals in more than 10 provinces in China. The patient's head CTA images will be randomly assigned to the True-AI and Sham-AI group with a ratio of 1:1, and the patients and radiologists are unaware of the allocation. The primary outcomes are sensitivity and specificity of detecting intracranial aneurysms. The secondary outcomes focus on the prognosis and outcomes of the patients.
The early noninvasive screening of patients suitable for neoadjuvant chemotherapy (NCT) is essential for personalized treatment in locally advanced gastric cancer (LAGC). The aim of this study was to develop and visualized a radio-clinical biomarker from pretreatment oversampled CT images to predict the response and prognosis to NCT in LAGC patients.
CT-enhanced scans are routine imaging modality for the diagnosis and follow-up of liver disease. However, this means that patients will receive more radiation dose. Therefore, it is necessary to reduce the radiation dose received by patients as much as possible. Deep learning-based reconstruction algorithms have been introduced to improve image quality recently. For many years, researchers attempt to maintain image quality using an advanced method while reducing radiation dose. Recently, a new deep-learning based iterative reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR, United Imaging Healthcare, Shanghai, China) has been introduced. In this study, we evaluate the image and diagnostic qualities of AIIR for low-dose portal vein and delayed phase liver CT with those of a KARL method normally used in standard-dose CT.
This registry has the following objectives. First, according to the guidance of 2021 WHO of CNS classification, we constructed and externally tested a multi-task DL model for simultaneous diagnosis of tumor segmentation, glioma classification and more extensive molecular subtype, including IDH mutation, ATRX deletion status, 1p19q co-deletion, TERT gene mutation status, etc. Second, based on the same ultimate purpose of liquid biopsy and radiomics, we innovatively put forward the concept and idea of combining radiomics and liquid biopsy technology to improve the diagnosis of glioma. And through our study, it will provide some clinical validation for this concept, hoping to supply some new ideas for subsequent research and supporting clinical decision-making.