BACKGROUND: Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. ...
Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examinatio...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) traces remains a challenging task and is crucial for telemonitoring of patients after stroke. This study aimed to quantify the generalizability of a deep learning (DL)-based...
OBJECTIVE: Robot-assisted pedicle screw placement in spinal fusion has been well studied. However, few studies have evaluated robot-assisted sacroiliac joint (SIJ) fusion. The aim of this study was to compare surgical characteristics, accuracy, and c...
To compare the quality and interobserver agreement in the evaluation of lumbar spinal stenosis (LSS) on computed tomography (CT) images between deep-learning reconstruction (DLR) and hybrid iterative reconstruction (hybrid IR). This retrospective stu...
Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
Jun 9, 2023
To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. A total of 35 000 clinical orthodontic images were collected in the Department o...
INTRODUCTION: Prostate cancer (PCa) is a disease with a high prevalence and incidence worldwide. Screening has pursued the early diagnosis of this disease to provide early treatment. We sought to characterize patients from a local hospital with respe...
Background Handcrafted radiomics and deep learning (DL) models individually achieve good performance in lesion classification (benign vs malignant) on contrast-enhanced mammography (CEM) images. Purpose To develop a comprehensive machine learning too...
Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on s...
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