AIMC Topic: Aged

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CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

European radiology
OBJECTIVES: To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.

European radiology
OBJECTIVES: To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

European radiology
OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system.

Robot-Assisted Minimally Invasive Multivessel Coronary Bypass Guided by Computerized Tomography.

Innovations (Philadelphia, Pa.)
OBJECTIVE: Robot-assisted minimally invasive coronary bypass surgery is one of the least invasive approaches that offers multivessel revascularization and accelerated recovery. We investigated the benefits of computed tomography angiography (CTA) gui...

Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading.

Journal of computer assisted tomography
OBJECTIVE: The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysem...

Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound.

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology
BACKGROUND: The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We aimed at utilizing the deep learning (DL)-based approach for differentiating XGC and GBC on ultrasoun...

Predicting treatment response using machine learning: A registered report.

The British journal of clinical psychology
OBJECTIVE: Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively scr...