AIMC Topic: Retrospective Studies
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The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.
Scientific reports
Apr 22, 2025
Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously develop...
SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal Tension Glaucoma.
Journal of glaucoma
Apr 21, 2025
PRCIS: The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field deterioration in patients with myopic normal tension glaucoma. Using SHAP analysis, the key variables driving disease progression were identified.
Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Apr 21, 2025
BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.
Using the Geriatric Emergency Perioperative Risk Index Derived From Artificial Intelligence Algorithms to Predict Outcomes of Geriatric Emergency General Surgery.
The Journal of surgical research
Apr 21, 2025
INTRODUCTION: The objective of this study was to employ artificial intelligence (AI) technology for the development of a model that can accurately forecast the outcome of emergency general surgery (EGS) in elderly patients. Additionally, an innovativ...
Combining Ultrasound Imaging and Molecular Testing in a Multimodal Deep Learning Model for Risk Stratification of Indeterminate Thyroid Nodules.
Thyroid : official journal of the American Thyroid Association
Apr 21, 2025
Indeterminate cytology (Bethesda III and IV) represents 15-30% of biopsied thyroid nodules and require additional diagnostic testing. Molecular testing (MT) is a commonly used diagnostic tool that evaluatesmalignancy risk through next generation seq...
Machine learning models for predicting tibial intramedullary nail length.
BMC musculoskeletal disorders
Apr 21, 2025
BACKGROUND: Tibial intramedullary nailing (IMN) represents a standard treatment for fractures of the tibial shaft. Nevertheless, accurately predicting the appropriate nail length prior to surgery remains a challenging endeavour. Conventional techniqu...
A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.
BMC infectious diseases
Apr 21, 2025
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely interven...
Use of ChatGPT for patient education involving HPV-associated oropharyngeal cancer.
American journal of otolaryngology
Apr 21, 2025
OBJECTIVE: This study aims to investigate the ability of ChatGPT to generate reliably accurate responses to patient-based queries specifically regarding oropharyngeal squamous cell carcinoma (OPSCC) of the head and neck.
Artificial intelligence prediction model for readmission after DIEP flap breast reconstruction based on NSQIP data.
Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Apr 20, 2025
BACKGROUND: Readmissions following deep inferior epigastric perforator (DIEP) flap breast reconstruction represent a significant healthcare burden, yet current risk prediction methods lack precision in identifying high-risk patients. We developed a m...
X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head.
Scientific reports
Apr 20, 2025
This study aimed to develop an X-ray radiomics model for predicting collapse of early-stage osteonecrosis of the femoral head (ONFH). A total of 87 patients (111 hips; training set: n = 67, test set: n = 44) with non-traumatic ONFH at Association Res...
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