AIMC Topic: Aged
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Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
An Automatic Deep-Radiomics Framework for Prostate Cancer Diagnosis and Stratification in Patients with Serum Prostate-Specific Antigen of 4.0-10.0 ng/mL: A Multicenter Retrospective Study.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model.
Oral oncology
Jan 2, 2025
BACKGROUND: Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral squamous cell carcinoma (OSCC), in which occult metastasis is a subtype that makes prediction challenging. Here, we developed and validated a deep lear...
Machine Learning Predictions of Recovery in Bilingual Poststroke Aphasia: Aligning Insights With Clinical Evidence.
Stroke
Jan 2, 2025
BACKGROUND: Predicting treated language improvement (TLI) and transfer to the untreated language (cross-language generalization, CLG) after speech-language therapy in bilingual individuals with poststroke aphasia is crucial for personalized treatment...
Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection.
JAMA network open
Jan 2, 2025
IMPORTANCE: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published in 2011, but the extent to which they align with current p...
Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.
JAMA network open
Jan 2, 2025
IMPORTANCE: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-correc...
Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.
Scientific reports
Jan 2, 2025
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-in...
Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.
Cardiovascular diabetology
Jan 2, 2025
BACKGROUND: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learnin...
Male
Cardiovascular Diseases
Middle Aged
Risk Assessment
Deep Learning
Female
Reproducibility of Results
Risk Factors
Aged
Image Interpretation, Computer-Assisted
Photography
Retinal Vessels
Time Factors
Diabetes Mellitus, Type 2
Predictive Value of Tests
Heart Disease Risk Factors
Diabetic Retinopathy
Decision Support Techniques
Prognosis
Humans
Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.
Arthritis research & therapy
Jan 2, 2025
BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Clas...
Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.
Scientific reports
Jan 2, 2025
A dual-stage model for classifying Parkinson's disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson's disease is the primary neurodegenerative disorder...
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