AIMC Topic: Retrospective Studies

Clear Filters Showing 6371 to 6380 of 9989 articles

Performance of a deep learning-based identification system for esophageal cancer from CT images.

Esophagus : official journal of the Japan Esophageal Society
BACKGROUND: Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep le...

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study.

Journal of medical Internet research
BACKGROUND: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have ty...

Peri-operative blood management of Jehovah's Witnesses undergoing cytoreductive surgery for advanced ovarian cancer.

Blood transfusion = Trasfusione del sangue
BACKGROUND: The aim of this study was to evaluate the efficacy and feasibility of a peri-operative bloodless medicine and surgery (BMS) protocol in reducing severe post-operative anaemia (haemoglobin [Hb] <7 g/dL) in Jehovah's Witnesses undergoing cy...

Using Deep Learning to Emulate the Use of an External Contrast Agent in Cardiovascular 4D Flow MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Although contrast agents would be beneficial, they are seldom used in four-dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications.

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer.

Korean journal of radiology
OBJECTIVE: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists.

Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.

Journal of digital imaging
To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subje...

Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network.

Chinese medical journal
BACKGROUND: Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic resonance imaging (MRI) played an important role in the preoperative examination of patients with recta...

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm.

Critical care (London, England)
BACKGROUND: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some pr...

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.

Korean journal of radiology
OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.