AIMC Topic: Retrospective Studies

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Novel artificial intelligence approach in neurointerventional practice: Preliminary findings on filter movement and ischemic lesions in carotid artery stenting.

Clinical neurology and neurosurgery
BACKGROUND AND OBJECTIVES: Embolic protection devices (EPDs) used during carotid artery stenting (CAS) are crucial in reducing ischemic complications. Although minimizing the filter-type EPD movement is considered important, limited research has demo...

Phenomenological psychopathology meets machine learning: A multicentric retrospective study (Mu.St.A.R.D.) targeting the role of Aberrant Salience assessment in psychosis detection.

Schizophrenia research
BACKGROUND: The Aberrant Salience (AS) model conceptualizes psychosis onset as the altered attribution of salience to neutral stimuli. The Aberrant Salience Inventory (ASI), a psychometric tool, measures this phenomenon. This study utilized a multi-c...

Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.

Annals of surgical oncology
OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.

Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US.

Radiology. Artificial intelligence
Purpose To develop and evaluate an artificial intelligence-based model for real-time nuchal translucency (NT) plane identification and measurement in prenatal US assessments. Materials and Methods In this retrospective multicenter study conducted fro...

Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint clinical assessment in shoulder CT scans.

Artificial intelligence in medicine
Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumer...

Multitask Deep Learning for Automated Detection of Endoleak at Digital Subtraction Angiography during Endovascular Aneurysm Repair.

Radiology. Artificial intelligence
Purpose To develop and evaluate a novel multitask deep learning framework for automated detection and localization of endoleaks at aortic digital subtraction angiography (DSA) performed during real-world endovascular aneurysm repair (EVAR) procedures...

ASSOCIATIONS BETWEEN HEART RATE VARIABILITY AND NEED FOR LIFESAVING INTERVENTION IN A LARGE HELICOPTER EMS SERVICE.

Shock (Augusta, Ga.)
Background : Heart rate variability (HRV) measures give insight into the autonomic regulation of cardiac function in healthy and critically ill patients. The ease and predictive potential of HRV measures may be valuable in optimizing prehospital tria...

Automated computation of the HEART score with the GPT-4 large language model.

The American journal of emergency medicine
BACKGROUND: Automated computation of the HEART score has the potential to facilitate clinical decision support and safety interventions. The goal of this study was to assess the performance of the GPT-4 large language model (LLM) in computation of th...