Xiang Liao

[Prevention and treatment of perioperative period complication of total ankle replacement].

[Clinical efficacy of posterior percutaneous endoscopic cervical discectomy for single level cervical spondylopathy with intraspinal ossification].

Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma.

Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study.

Commentary: Radiomics in oncology: A 10-year bibliometric analysis.

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT.

Diagnostic Performance of Transient Elastography Versus Two-Dimensional Shear Wave Elastography for Liver Fibrosis in Chronic Viral Hepatitis: Direct Comparison and a Meta-Analysis.

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database.

Research trends and hotspots on connectomes from 2005 to 2021: A bibliometric and latent Dirichlet allocation application study.

Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database.

Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning.

Novel Nomograms and Web-Based Tools Predicting Overall Survival and Cancer-specific Survival of Solitary Plasmacytoma of the Spine.

Research hotspots and trends on spinal cord stimulation for pain treatment: a two-decade bibliometric analysis.

Early prognostication of critical patients with spinal cord injury: A machine learning study with 1485 cases.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study.

Mapping the hotspots and future trends of electrical stimulation for peripheral nerve injury: A bibliometric analysis from 2002 to 2023.

Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.