AIMC Topic: Humans

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Robotic tomographic ultrasound and artificial intelligence for management of haemodialysis arteriovenous fistulae.

The journal of vascular access
BACKGROUND: Arteriovenous fistulae (AVF) and Arteriovenous Grafts (AVG) may present a problematic vascular access for renal replacement therapy (RRT), reliant on recurrent specialist nurse and medical evaluation. Dysfunctional accesses are frequently...

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.

European radiology
OBJECTIVES: To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time co...

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.

Journal of biomedical optics
SIGNIFICANCE: Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur fol...

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

The Journal of arthroplasty
BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm...

Artificial Intelligence to Patient-Targeted Health Information on Kidney Stone Disease: Comment.

Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation

Cerebrospinal fluid flow artifact reduction with deep learning to optimize the evaluation of spinal canal stenosis on spine MRI.

Skeletal radiology
PURPOSE: The aim of study was to employ the Cycle Generative Adversarial Network (CycleGAN) deep learning model to diminish the cerebrospinal fluid (CSF) flow artifacts in cervical spine MRI. We also evaluate the agreement in quantifying spinal canal...

Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis.

The American journal of gastroenterology
INTRODUCTION: We estimate the economic impact of applying risk assessment tools to identify very low-risk patients with upper gastrointestinal bleeding who can be safely discharged from the emergency department using a cost minimization analysis.

Assessing the performance of ChatGPT's responses to questions related to epilepsy: A cross-sectional study on natural language processing and medical information retrieval.

Seizure
BACKGROUND: Epilepsy is a neurological condition marked by frequent seizures and various cognitive and psychological effects. Reliable information is essential for effective treatment. Natural language processing models like ChatGPT are increasingly ...

An Effective Hybrid Deep Learning Model for Single-Channel EEG-Based Subject-Independent Drowsiness Recognition.

Brain topography
Nowadays, road accidents pose a severe risk in cases of sleep disorders. We proposed a novel hybrid deep-learning model for detecting drowsiness to address this issue. The proposed model combines the strengths of discrete wavelet long short-term memo...

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

Neuroradiology
PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications i...