OBJECTIVE: The aim of this study was to develop a multimodal fusion model for accurate risk prediction and clinical decision support for ductal carcinoma in-situ (DCIS).
BACKGROUND: To develop and validate a deep learning tool for the automatic segmentation of pancreatic solid neoplasms and to establish a radiomics model for diagnosing these solid neoplasms in MRI.
BACKGROUND: Individuals with metabolic syndrome (MetS) are more prone to depression, which is a significant complication impacting quality of life. This research seeks to create and validate predictive models for assessing depression risk in patients...
BACKGROUND: Accurate preoperative risk stratification for patients with head and neck (H&N) cancer remained a critical challenge, as long-term survival rates are poor despite aggressive multimodality treatment. While deep learning models showed promi...
OBJECTIVE: To establish and validate a machine learning model using preoperative multi-sequence MRI radiomic features and clinical data to predict pancreatic fistula after pancreaticoduodenectomy (PD).
BACKGROUND: Protein-energy wasting (PEW) is a common complication of patients on maintenance haemodialysis (MHD) and is strongly associated with poor clinical outcomes; early identification and timely nutritional interventions are essential. The aim ...
BACKGROUND: Establishing risk factors associated with severity and prognosis in the early stages of the disease is important to identify patients who need specialized care. Creating new clinical tools to improve health decisions and outcomes in the p...
INTRODUCTION: Difficult intubation is one of the most challenging scenarios to deal with due to increased morbidity and mortality. Machine learning systems can help predict this process in advance. This study aimed to predict whether patients had dif...
This study assessed whether resting-state quantitative EEG (qEEG) can differentiate tinnitus laterality under rigorous multiple-comparison control and nested, cross-validated machine learning (ML). We analyzed 210 pre-specified qEEG features-spectral...
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