AIMC Topic: Retrospective Studies

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Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms.

IEEE journal of biomedical and health informatics
Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed tomography (CT) images, which is time-consuming and subjective. We used a semi-automatic deep learning...

Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large train...

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review.

Academic radiology
OBJECTIVE: Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of bre...

Analysis of the short-term outcomes of biportal robot-assisted lobectomy.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The present study aimed to assess the short-term consequences of biportal robot-assisted lobectomy, validating its safety and effectiveness.

Deep learning-based Hounsfield unit value measurement method for bolus tracking images in cerebral computed tomography angiography.

Computers in biology and medicine
BACKGROUND: Patient movement during bolus tracking (BT) impairs the accuracy of Hounsfield unit (HU) measurements. This study assesses the accuracy of measuring HU values in the internal carotid artery (ICA) using an original deep learning (DL)-based...

Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients.

Chinese medical journal
BACKGROUND: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM fo...

Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.

The Lancet. Digital health
BACKGROUND: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening te...

Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy.

Scientific reports
The manual reading of capsule endoscopy (CE) videos in small bowel disease diagnosis is time-intensive. Algorithms introduced to automate this process are premature for real clinical applications, and multi-diagnosis using these methods has not been ...