AI Medical Compendium Topic:
Tomography, X-Ray Computed

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Development and validation of radiology-clinical statistical and machine learning model for stroke-associated pneumonia after first intracerebral haemorrhage.

BMC pulmonary medicine
BACKGROUND: Society is burdened with stroke-associated pneumonia (SAP) after intracerebral haemorrhage (ICH). Cerebral small vessel disease (CSVD) complicates clinical manifestations of stroke. In this study, we redefined the CSVD burden score and in...

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with ...

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

European radiology experimental
BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck C...

Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma.

Pediatric pulmonology
OBJECTIVES: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway change...

Detection and characterization of pancreatic lesion with artificial intelligence: The SFR 2023 artificial intelligence data challenge.

Diagnostic and interventional imaging
PURPOSE: The purpose of the 2023 SFR data challenge was to invite researchers to develop artificial intelligence (AI) models to identify the presence of a pancreatic mass and distinguish between benign and malignant pancreatic masses on abdominal com...

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

Journal of applied clinical medical physics
PURPOSE: Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medica...

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Scientific reports
Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing el...

Artificial Intelligence in Pancreatic Image Analysis: A Review.

Sensors (Basel, Switzerland)
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical ima...

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.

BMC cancer
BACKGROUND: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tum...

A systematic review of deep learning-based spinal bone lesion detection in medical images.

Acta radiologica (Stockholm, Sweden : 1987)
Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for e...