AIMC Topic: Aged

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The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BMC medical imaging
BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19.

Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.

European radiology
OBJECTIVES: To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST).

Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic CTA: A feasibility study of 70 kVp and reduced contrast medium volume.

European journal of radiology
PURPOSE: To investigate the image quality and feasibility of a novel artificial intelligence iterative reconstruction (AIIR) algorithm for aortic computer tomography angiography (CTA) with a low radiation dose and contrast material (CM) dosage protoc...

Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma.

Scientific reports
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic ...

Early heart rate variability evaluation enables to predict ICU patients' outcome.

Scientific reports
Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such var...

Anatomical Partition-Based Deep Learning: An Automatic Nasopharyngeal MRI Recognition Scheme.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Training deep learning (DL) models to automatically recognize diseases in nasopharyngeal MRI is a challenging task, and optimizing the performance of DL models is difficult.

Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.

Journal of Korean medical science
BACKGROUND: Videofluoroscopic swallowing study (VFSS) is currently considered the gold standard to precisely diagnose and quantitatively investigate dysphagia. However, VFSS interpretation is complex and requires consideration of several factors. The...

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Scientific reports
Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were t...

Predictive classification of Alzheimer's disease using brain imaging and genetic data.

Scientific reports
For now, Alzheimer's disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few ...

Antemortem detection of Parkinson's disease pathology in peripheral biopsies using artificial intelligence.

Acta neuropathologica communications
The diagnosis of Parkinson's disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmortem assessment remains the gold standard for a definitive diagnosis. While it is well recognized th...