AIMC Topic: Treatment Outcome
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Artificial Intelligence Models Are Limited in Predicting Clinical Outcomes Following Hip Arthroscopy: A Systematic Review.
JBJS reviews
Aug 22, 2024
BACKGROUND: Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed. Artificial intelligence (AI) has emerged as an effective supportive decision-making tool for surgeo...
A preliminary study on the effects of long-term robot suit exercise training on gait function and quality of life in patients with spinal and bulbar muscular atrophy.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Aug 20, 2024
Spinal and bulbar muscular atrophy (SBMA) progressively impairs gait function, resulting in the need for patients to use a wheelchair approximately 20 years after onset. No reports have investigated the effects of long-term exercise training using th...
Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery.
Methodist DeBakey cardiovascular journal
Aug 20, 2024
Heart failure (HF) affects millions of individuals and causes hundreds of thousands of deaths each year in the United States. Despite the public health burden, medical and device therapies for HF significantly improve clinical outcomes and, in a subs...
Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients.
PloS one
Aug 20, 2024
BACKGROUND: There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could hel...
Commentary on Machine Learning to Predict Prostate Artery Embolization Outcomes? Patient Selection for Prostatic Artery Embolization: Why it Matters.
Cardiovascular and interventional radiology
Aug 19, 2024
Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients.
Genes
Aug 19, 2024
UNLABELLED: The incidence of breast cancer (BC) continues to rise steadily, posing a significant burden on the public health systems of various countries worldwide. As a member of the tumor microenvironment (TME), CD8+ T cells inhibit cancer progress...
A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.
Frontiers in endocrinology
Aug 19, 2024
INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed...
Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.
Science bulletin
Aug 16, 2024
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures p...
A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.
Journal of endourology
Aug 12, 2024
We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Data from pediatric patients who underwent ureteroscopy for ...
Machine-Learning Models Reliably Predict Clinical Outcomes in Medial Patellofemoral Ligament Reconstruction.
Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Aug 10, 2024
PURPOSE: To develop a machine-learning model to predict clinical outcomes after medial patellofemoral ligament reconstruction (MPFLR) and identify the important predictive indicators.
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