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Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and non-CKD patients based on interpretable machine learning methods.

International urology and nephrology
BACKGROUND: Calcification is prevalent in CKD patients, with abdominal aortic calcification (AAC) being a strong predictor of coronary calcification. We aimed to identify key calcification factors in CKD and non-CKD populations using machine learning...

nnU-Net-based high-resolution CT features quantification for interstitial lung diseases.

European radiology
OBJECTIVES: To develop a new high-resolution (HR)CT abnormalities quantification tool (CVILDES) for interstitial lung diseases (ILDs) based on the nnU-Net network structure and to determine whether the quantitative parameters derived from this new so...

Predicting treatment response to systemic therapy in advanced gallbladder cancer using multiphase enhanced CT images.

European radiology
BACKGROUND: Accurate estimation of treatment response can help clinicians identify patients who would potentially benefit from systemic therapy. This study aimed to develop and externally validate a model for predicting treatment response to systemic...

Machine Learning Methods to Track Dynamic Facial Function in Facial Palsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: For patients with facial palsy, the wait for return of facial function and resulting vision risk from poor eye closure, difficulty speaking and eating from flaccid oral sphincter muscles, and psychological morbidity from the inability to s...

Prediction of perimetric progression in ocular hypertension and open angle glaucoma based on corneal biomechanics.

European journal of ophthalmology
PurposeTo identify parameters that are significant risk predictors of visual field (VF) progression in patients with ocular hypertension (OHT) or early primary open-angle glaucoma (POAG), using Goldmann applanation tonometry intraocular pressure (IOP...

External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT.

Abdominal radiology (New York)
PURPOSE: This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geo...

Evaluating Trunk Control Ability in Patients With Spinal Cord Injury via a Robotic Brace.

IEEE transactions on bio-medical engineering
Evaluating trunk control ability is significant in guiding patients towards proper functional training. Existing assessment techniques are subjective with low resolution, lack multi-dimensional assessment capability, or fail to provide active protect...

Machine Learning Classifier Using Blood Count Parameters and Erythropoietin to Predict JAK2 Mutations in Patients With Erythrocytosis.

Archives of pathology & laboratory medicine
CONTEXT.—: Differentiating polycythemia vera from other causes of erythrocytosis is a diagnostic challenge. Although most patients with polycythemia vera have Janus kinase 2 (JAK2) mutations, extensive testing is impractical because this is an uncomm...

A Pilot Study on Fabric-Based Pneumatic Soft Gloves for Assisting Patients With Severe Brachial Plexus Injury.

IEEE transactions on bio-medical engineering
OBJECTIVE: Robotic gloves show promise in hand assistance due to their wearability and home-based potential, yet empirical research remains limited. This pilot study presents a fabric-based pneumatic soft glove, aiming to identify its potential and c...