AIMC Topic: Retrospective Studies

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An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have lim...

Lung cancer prediction by Deep Learning to identify benign lung nodules.

Lung cancer (Amsterdam, Netherlands)
INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an in...

Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis.

Physics in medicine and biology
Computer aided detection (CADe) for breast lesions can provide an important reference for radiologists in breast cancer screening. Architectural distortion (AD) is a type of breast lesion that is difficult to detect. A majority of CADe methods focus ...

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design an...

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Nature communications
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment....

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning.

Ophthalmology. Retina
PURPOSE: To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-...

Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...

A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.

European radiology
OBJECTIVES: To investigate the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications.

Intracorporeal anastomosis in right hemicolectomy for colon cancer: short-term outcomes with the DaVinci Xi robot.

Journal of robotic surgery
Intracorporeal anastomosis (IA) may improve outcomes compared with extracorporeal anastomosis (EA) in minimally invasive right colectomy. This is a prospective series of robotic right hemicolectomies (RRC) with IA from one institution. 35 consecutive...