Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis and emergency condition identification. To address these limitations, we develop Meta General Practiti...
OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment ...
Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brai...
Early detection of lung nodules is crucial for the prevention and treatment of lung cancer. However, current methods face challenges such as missing small nodules, variations in nodule size, and high false positive rates. To address these challenges,...
OBJECTIVES: To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connecti...
The international journal of medical robotics + computer assisted surgery : MRCAS
40209153
BACKGROUND: Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output error...
BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patien...
Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessi...
. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residu...