AIMC Topic: Middle Aged

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Image Quality Assessment of a Deep Learning-Based Automatic Bone Removal Algorithm for Cervical CTA.

Journal of computer assisted tomography
BACKGROUND: The present study aims to evaluate the postprocessing image quality of a deep-learning (DL)-based automatic bone removal algorithm in the real clinical practice for cervical computed tomography angiography (CTA).

Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction.

Investigative radiology
OBJECTIVES: Reducing gadolinium-based contrast agents to lower costs, the environmental impact of gadolinium-containing wastewater, and patient exposure is still an unresolved issue. Published methods have never been compared. The purpose of this stu...

Deep Learning Based Automatic Segmentation of the Thoracic Aorta from Chest Computed Tomography in Healthy Korean Adults.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: Segmenting the aorta into zones based on anatomical landmarks is a current trend to better understand interventions for aortic dissection or aneurysm. However, comprehensive reference values for aortic zones are lacking. The aim of this st...

Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

Head & neck
BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (...

Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and fu...

Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.

Annals of surgery
OBJECTIVE: To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC).

Predictive modeling of mortality in carbapenem-resistant bloodstream infections using machine learning.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives due to escalating carbapenem resistance. This study meticulously examined clinical, microbiological...

The impact of high-order features on performance of radiomics studies in CT non-small cell lung cancer.

Clinical imaging
High-order radiomic features have been shown to produce high performance models in a variety of scenarios. However, models trained without high-order features have shown similar performance, raising the question of whether high-order features are wor...

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

Clinical imaging
PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and cli...

Automated detection of tonic seizures using wearable movement sensor and artificial neural network.

Epilepsia
Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neu...