AIMC Topic: Tomography, X-Ray Computed
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Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging.
Computers in biology and medicine
Dec 19, 2024
We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO)...
A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.
Abdominal radiology (New York)
Dec 18, 2024
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.
Journal of X-ray science and technology
Dec 18, 2024
BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular e...
Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.
Tomography (Ann Arbor, Mich.)
Dec 18, 2024
OBJECTIVES: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).
Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.
Cancer imaging : the official publication of the International Cancer Imaging Society
Dec 18, 2024
BACKGROUND: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bev...
Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this s...
SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.
Academic radiology
Dec 16, 2024
RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical data. This study constructed a multimodal diagnostic model for abdominal trauma using deep learning...
Automated Bone Cancer Detection Using Deep Learning on X-Ray Images.
Surgical innovation
Dec 16, 2024
In recent days, bone cancer is a life-threatening health issue that can lead to death. However, physicians use CT-scan, X-rays, or MRI images to recognize bone cancer, but still require techniques to increase precision and reduce human labor. These m...
Concordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.
Artificial intelligence in medicine
Dec 16, 2024
In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predi...
Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.
Emergency radiology
Dec 16, 2024
BACKGROUND AND AIM: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologists evaluating computed tomography (CT) scans. The necessity for automated diagnostic tools has been ...
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