AIMC Topic: Retrospective Studies
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Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.
Nuclear medicine communications
Feb 18, 2025
OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration in the striatum. In assessing the neuron degeneration, visual and semiquantitative methods are used t...
Humans
Aged
Male
Middle Aged
Deep Learning
Convolutional Neural Networks
Neural Networks, Computer
Brain
Retrospective Studies
Female
Dopamine Plasma Membrane Transport Proteins
Image Processing, Computer-Assisted
Parkinson Disease
Image Interpretation, Computer-Assisted
Tomography, Emission-Computed, Single-Photon
Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.
Cancer imaging : the official publication of the International Cancer Imaging Society
Feb 18, 2025
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant ris...
Deep learning-based automated guide for defining a standard imaging plane for developmental dysplasia of the hip screening using ultrasonography: a retrospective imaging analysis.
BMC medical informatics and decision making
Feb 18, 2025
BACKGROUND: We aimed to propose a deep-learning neural network model for automatically detecting five landmarks during a two-dimensional (2D) ultrasonography (US) scan to develop a standard plane for developmental dysplasia of the hip (DDH) screening...
Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.
BMC cancer
Feb 18, 2025
OBJECTIVE: This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.
Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.
Scientific reports
Feb 18, 2025
Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were...
Exploring the Effect of an 8-Week AI-Composed Exercise Program on Pain Intensity and Well-Being in Patients With Spinal Pain: Retrospective Cohort Analysis.
JMIR formative research
Feb 18, 2025
BACKGROUND: Spinal pain, one of the most common musculoskeletal disorders (MSDs), significantly impacts the quality of life due to chronic pain and disability. Physical activity has shown promise in managing spinal pain, although optimizing adherence...
Recurrence patterns and prediction of survival after recurrence for gallbladder cancer.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Feb 17, 2025
BACKGROUND: Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict surviv...
Comparative ranking of marginal confounding impact of natural language processing-derived versus structured features in pharmacoepidemiology.
Computers in biology and medicine
Feb 17, 2025
OBJECTIVE: To explore the ability of natural language processing (NLP) methods to identify confounder information beyond what can be identified using claims codes alone for pharmacoepidemiology.
A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.
BMC cancer
Feb 17, 2025
BACKGROUND: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event predic...
Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.
Scientific reports
Feb 17, 2025
Cervical spinal cord injury is often catastrophic, frequently leading to irreversible impairment. MRI has become the gold standard for evaluating spinal cord injuries (SCI). Our study aimed to assess the accuracy of a radiomics approach, based on mac...
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