AIMC Topic: Tomography, X-Ray Computed
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A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.
BMC medical imaging
May 24, 2024
OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomic...
Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.
Surgery today
May 23, 2024
PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of...
Artificial Intelligence
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Humans
Neoplasm Invasiveness
Neoplasm Staging
Pleural Neoplasms
Preoperative Period
Pleura
Middle Aged
Deep Learning
Aged, 80 and over
Lung Neoplasms
Adult
Carcinoma, Non-Small-Cell Lung
Sensitivity and Specificity
Male
Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis.
Lung
May 23, 2024
BACKGROUND: There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (C...
Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study.
Computers in biology and medicine
May 23, 2024
Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluate...
Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data.
BMC pulmonary medicine
May 23, 2024
BACKGROUND: Mortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the t...
Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.
Scientific reports
May 23, 2024
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT sca...
Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.
Scientific reports
May 23, 2024
This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentiating between cerebral cavernous malformation (CCM) and acute intraparenchymal hemorrhage (AIH) on brain computed tomography (CT). A retrospective, mul...
Preoperatively predicting survival outcome for clinical stage IA pure-solid non-small cell lung cancer by radiomics-based machine learning.
The Journal of thoracic and cardiovascular surgery
May 22, 2024
OBJECTIVE: Clinical stage IA non-small cell lung cancer (NSCLC) showing a pure-solid appearance on computed tomography is associated with a worse prognosis. This study aimed to develop and validate machine-learning models using preoperative clinical ...
Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients.
Journal of medical radiation sciences
May 22, 2024
INTRODUCTION: This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) regio...
Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.
European radiology
May 22, 2024
PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans.
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