AIMC Topic: Tomography, X-Ray Computed

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Deep learning image reconstruction for quality assessment of iodine concentration in computed tomography: A phantom study.

Journal of X-ray science and technology
BACKGROUND: Recently, deep learning reconstruction (DLR) technology aiming to improve image quality with minimal radiation dose has been applied not only to pediatric scans, but also to computed tomography angiography (CTA).

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.

Current medical imaging
BACKGROUND: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of success...

Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.

JCO clinical cancer informatics
PURPOSE: We developed a deep neural network that queries the lung computed tomography-derived feature space to identify radiation sensitivity parameters that can predict treatment failures and hence guide the individualization of radiotherapy dose. I...

Segmentation of Clinical Target Volume From CT Images for Cervical Cancer Using Deep Learning.

Technology in cancer research & treatment
Segmentation of clinical target volume (CTV) from CT images is critical for cervical cancer brachytherapy, but this task is time-consuming, laborious, and not reproducible. In this work, we aim to propose an end-to-end model to segment CTV for cervi...

Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Artificial intelligence (AI) technology is a promising diagnostic adjunct in fracture detection. However, few studies describe the improvement of clinicians' diagnostic accuracy for nasal bone fractures with the aid of AI technology.

Quality Assurance based on Deep Learning for Pelvic OARs Delineation in Radiotherapy.

Current medical imaging
BACKGROUND: Correct delineation of organs at risk (OARs) is an important step for radiotherapy and it is also a time-consuming process that depends on many factors.

The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Cell reports. Medicine
Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank. By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fa...

Robot-assisted anterior transpedicular screw fixation with 3D printed implant for multiple cervical fractures: A case report.

Medicine
RATIONALE: The anterior transpedicular screw (ATPS) fixation in the cervical spine provides the advantages of both anterior and posterior cervical surgery; however, it poses a high risk of screw insertion. In addition, a 3D printed implant can match ...

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

Medicine
BACKGROUND: The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in...

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients.

Briefings in bioinformatics
Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep...