AIMC Topic: Retrospective Studies

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Prediction of coronal alignment in robotic-assisted total knee arthroplasty with artificial intelligence.

The Knee
INTRODUCTION: Robotic-assisted technologies provide the ability to avoid soft tissue release by utilizing more accurate bony cuts during total knee arthroplasty (TKA). However, the ideal limb alignment is not yet established. The aim of this study wa...

Interpretable Machine Learning for Predicting Anterior Uveitis in Axial Spondyloarthritis.

Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases
BACKGROUND: Axial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the spine and sacroiliac joints, with anterior uveitis (AU) as a common extra-articular manifestation. Predicting AU onset in axSpA patients is challeng...

A Novel Natural Language Processing Model for Triaging Head and Neck Patient Appointments.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Inaccurate patient triage contributes to suboptimal clinical capacity management and delays in patient care, which in cancer patients may significantly increase morbidity and mortality. We developed a natural language processing (NLP) mode...

Opportunistic assessment of abdominal aortic calcification using artificial intelligence (AI) predicts coronary artery disease and cardiovascular events.

American heart journal
BACKGROUND: Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). ...

Effect of AI-based pre-hospital health education via QR code on APAIS scores in patients with breast nodules: A retrospective study.

Breast (Edinburgh, Scotland)
PURPOSE: To explore the effect of AI-based pre-hospital health education via QR code on preoperative anxiety and information needs in patients with breast nodules and provide a decision-making reference for ongoing optimizing clinical workflows.

Artificial Delayed-phase Technetium-99m MIBI Scintigraphy From Early-phase Scintigraphy Improves Identification of Hyperfunctioning Parathyroid Lesions in Patients With Hyperparathyroidism.

Clinical nuclear medicine
PURPOSE: The aim of this study was to generate and validate artificial delayed-phase technetium-99m methoxyisobutylisonitrile scintigraphy (aMIBI) images from early-phase technetium-99m methoxyisobutylisonitrile scintigraphy (eMIBI) images.

Reduction of radiation exposure in chest radiography using deep learning-based noise reduction processing: A phantom and retrospective clinical study.

Radiography (London, England : 1995)
INTRODUCTION: Intelligent noise reduction (INR), a deep learning-based noise reduction developed by Canon, is used in planar radiography to improve image quality and reduce patient exposure dose. This study aimed to evaluate the reduction of patient ...

Optimising coronary imaging decisions with machine learning: an external validation study.

Open heart
BACKGROUND: Exclusion of coronary stenosis in individuals with suggestive symptoms is challenging. Cardiac CT or coronary angiography is often used but is inefficient and costly and involves risks. Sex-stratified algorithms based on electronic health...