Publications by authors named "Li-Wen Xiao"
Article Synopsis
- The study aims to create a machine learning model that combines clinical data and ultrasound radiomic analysis to predict whether axillary lymph nodes are affected in early-stage breast cancer patients.
- Using retrospective data from 321 patients, the researchers identified significant risk factors and developed both clinical and ultrasound radiomics models, comparing multiple machine learning algorithms to find the most effective for diagnosis.
- The results showed that the joint prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm performed best, achieving high AUC scores, indicating strong accuracy in predicting lymph node status.
Background: Explore the feasibility of using the multimodal ultrasound (US) radiomics technology to diagnose American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 thyroid nodules.
Method: This study prospectively collected the clinical characteristics, conventional, and US elastography images of 100 patients diagnosed with ACR TI-RADS 4-5 nodules from May 2022 to 2023. Independent risk factors for malignant thyroid nodules were extracted and screened using methods such as the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model, and a multimodal US radiomics combined diagnostic model was established.
Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images.
View Article and Find Full Text PDFObjective: This study compared the diagnostic value of various diagnostic methods for lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) through network meta-analysis.
Methods: In this experiment, databases such as CNKI, Wanfang, PubMed, and Web of Science were retrieved according to the Cochrane database, Prisma, and NMAP command manual. A meta-analysis was performed using STATA 15.
Article Synopsis
- The study evaluates how accurately the VGGNet deep learning model can differentiate between benign and malignant thyroid nodules using ultrasound images.
- A total of 11 research studies were analyzed, revealing high sensitivity (0.87) and specificity (0.85) for the model, indicating it performed well in diagnosis.
- Overall, the VGGNet model showed strong diagnostic efficacy with an area under the curve of 0.93, confirming its effectiveness in this medical application.
[Effects of orthodontic force on tumor necrosis factor alpha protein expression in the inflammatory periodontal tissues].
Hua Xi Kou Qiang Yi Xue Za Zhi
June 2005
Objective: To explore the combined effects of orthodontic force and inflammation on the remodelling of periodontal tissues.
Methods: The upper first molars underwent mesial orthodontic force on 48 rats suffering experimental periodontitis and 48 rats injected lipopolysaccharide, respectively.
Results: The TNF-alpha protein expression in the compressed periodontal tissues fluctuated during 0, 2, 12 hours and 2, 7, 14 days stages, the OD value got to the peak in the compressed periodontal tissues in 2 days.