AIMC Topic: Retrospective Studies

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Identification of optimal portal pressure decrease to control ascites while minimizing HE after TIPS: A multicenter study.

Hepatology (Baltimore, Md.)
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...

Predicting intermediate-risk prostate cancer using machine learning.

International urology and nephrology
PURPOSES: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and asse...

Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and ca...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Pharmacotherapy
BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given th...

Intelligent measurement of adolescent idiopathic scoliosis x-ray coronal imaging parameters based on VB-Net neural network: a retrospective analysis of 2092 cases.

Journal of orthopaedic surgery and research
BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional deformity, and up to now, there has been no literature reporting the analysis of a large sample of X-ray imaging parameters based on artificial intelligence (AI) for it....

Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.

BMC cancer
OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained...

Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach.

Scientific reports
To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation ...

Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.

Abdominal radiology (New York)
OBJECTIVE: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assess...

Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups.

European radiology
OBJECTIVES: Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-netw...

Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.

Orthodontics & craniofacial research
OBJECTIVE: To assess the accuracy of three commercially available and one open-source deep learning (DL) solutions for automatic tooth segmentation in cone beam computed tomography (CBCT) images of patients with multiple dental impactions.