Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a s...
American journal of physical medicine & rehabilitation
Mar 5, 2025
OBJECTIVE: This study investigated the effectiveness of wearable robot-assisted gait training compared to treadmill gait training for improving balance and walking ability in stroke patients.
OBJECTIVES: Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway n...
OBJECTIVES: This study evaluated and compared the effectiveness of various predictive models for forecasting occult lymph node metastasis (LNM) in tongue squamous cell carcinoma (TSCC) patients.
European journal of gastroenterology & hepatology
Mar 5, 2025
BACKGROUND AND AIMS: Posthepatectomy liver failure (PHLF) remains a severe complication after hepatectomy for hepatocellular carcinoma (HCC) and accurate preoperative evaluation and predictive measures are urgently needed. We investigated the impact ...
BACKGROUND: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare co...
BACKGROUND: Complex regional pain syndrome is a chronic pain syndrome typically affecting a limb. It is characterized by severe spontaneous and evoked pain, along with vasomotor, autonomic, and motor signs and symptoms. Although dysregulation in seve...
PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs...
PURPOSE: To assess the added value of artificial intelligence (AI) for radiologists and emergency physicians in the radiographic detection of pelvic fractures.
BACKGROUND: To evaluate the effectiveness of machine learning (ML) techniques in predicting negative remodeling in uncomplicated Stanford type B intramural hematoma (IMHB) during the acute phase.
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