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Influence of deep learning-based super-resolution reconstruction on Agatston score.
European radiology
Mar 20, 2025
OBJECTIVE: To evaluate the impact of deep learning-based super-resolution reconstruction (DLSRR) on image quality and Agatston score.
Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients.
Surgical infections
Mar 20, 2025
Surgical quality datasets are critical to decision-making tools including surgical infection (SI). Machine learning models (MLMs), a branch of artificial intelligence (AI), are increasingly being ingrained within surgical decision-making algorithms....
Plasma Proteomic Profiles Predict Individual Future Osteoarthritis Risk.
Arthritis & rheumatology (Hoboken, N.J.)
Mar 20, 2025
OBJECTIVE: Osteoarthritis (OA) is a widespread degenerative joint disease that causes a considerable socioeconomic burden. Despite progress in genetic and environmental insights, early diagnosis is still limited by the lack of evident symptoms during...
Can Artificial Intelligence Software be Utilised for Thyroid Multi-Disciplinary Team Outcomes?
Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
Mar 20, 2025
OBJECTIVES: ChatGPT is one of the most publicly available artificial intelligence (AI) softwares. Ear, nose and throat (ENT) services are often stretched due to the increasing incidence of thyroid malignancies. This study aims to investigate whether ...
Using Machine Learning to Identify Social Determinants of Health that Impact Discharge Disposition for Hospitalized Patients.
Journal of the American Medical Directors Association
Mar 20, 2025
OBJECTIVE: To identify self-reported social determinants of health (SDOH) among hospitalized patients that predict discharge to a skilled nursing facility (SNF).
A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.
Cell reports. Medicine
Mar 20, 2025
Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evalu...
PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.
Medicina (Kaunas, Lithuania)
Mar 20, 2025
: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on stand-alone models or International Classification of Diseases (ICD) codes is insufficient due to limited predictive power and inconsistencies in clini...
Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer.
Tomography (Ann Arbor, Mich.)
Mar 20, 2025
BACKGROUND: Predictive models like Residual Neural Networks (ResNets) can use Magnetic Resonance Imaging (MRI) data to identify cervix tumors likely to recur after radiotherapy (RT) with high accuracy. However, there persists a lack of insight into m...
Development of PDAC diagnosis and prognosis evaluation models based on machine learning.
BMC cancer
Mar 20, 2025
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Existing serum biomarkers like CA19-9 are limited in early diagnosis, failing to meet clinical needs. Mac...
Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.
BMC anesthesiology
Mar 20, 2025
BACKGROUND: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction in the early postoperative period. Cu...
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