AIMC Topic: Tomography, X-Ray Computed

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Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach.

Journal of cardiovascular translational research
The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG-gated computed tomography data. After 21 exclusions, CT acquired before transcatheter aortic valve implantation in 71 patients were reviewed and rand...

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

International journal of molecular sciences
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machin...

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning.

Computers in biology and medicine
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To...

Low-dose CT denoising via convolutional neural network with an observer loss function.

Medical physics
PURPOSE: Convolutional neural network (CNN)-based denoising is an effective method for reducing complex computed tomography (CT) noise. However, the image blur induced by denoising processes is a major concern. The main source of image blur is the pi...

Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers.

Sensors (Basel, Switzerland)
PROBLEM: An application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented.

A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

Scientific reports
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of rea...

X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.

BioMed research international
PURPOSE: Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (...

Ferromagnetic soft catheter robots for minimally invasive bioprinting.

Nature communications
In vivo bioprinting has recently emerged as a direct fabrication technique to create artificial tissues and medical devices on target sites within the body, enabling advanced clinical strategies. However, existing in vivo bioprinting methods are ofte...

Robot-assisted stereotactic multiple brain abscesses' puncture: technical case report.

Acta neurochirurgica
We report a case of multiple brain abscesses' puncture, employing the ROSA™ Brain surgical robot (Zimmer Biomet) and the O-arm® O2 Imaging System (Medtronic). A 51-year-old man was diagnosed with multiple supratentorial ring enhancing cystic lesions ...

Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT.

Korean journal of radiology
OBJECTIVE: The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT.