Cirrhosis care is inherently complex, marked by a high risk of acute decompensation and significant morbidity and mortality. Traditional episodic care models provide static snapshots of a patient's condition, limiting the ability to address dynamic c...
Conventional ascitic fluid cytology for detecting ovarian cancer is limited by its low sensitivity. To address this issue, this multicenter study developed patch image (PI)-based fully supervised convolutional neural network (CNN) models and clusteri...
BACKGROUND AND AIMS: Clinically significant portal hypertension in patients with liver cirrhosis can lead to refractory ascites. A TIPS treats clinically significant portal hypertension but may cause overt hepatic encephalopathy (oHE). Our aim was to...
BACKGROUND AND AIMS: Diagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing using large language models (...
Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we develope...
Journal of applied clinical medical physics
Jun 20, 2022
PURPOSE: The detection of abdominal free fluid or hemoperitoneum can provide critical information for clinical diagnosis and treatment, particularly in emergencies. This study investigates the use of deep learning (DL) for identifying peritoneal free...
Computational and mathematical methods in medicine
May 4, 2022
The study was aimed at exploring the diagnostic value of artificial intelligence reconstruction algorithm combined with CT image parameters on hepatic ascites, expected to provide a reference for the etiological evaluation of clinical abdominal effus...
BACKGROUND: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and...
OBJECTIVE: Mortality following surgical resection of spinal tumors is a devastating outcome. Naïve Bayes machine learning algorithms may be leveraged in surgical planning to predict mortality. In this investigation, we use a Naïve Bayes classificatio...
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