AIMC Topic: Middle Aged

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Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

Cancer control : journal of the Moffitt Cancer Center
BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions.ObjectiveWe aimed to develop a machine learnin...

Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration.

Technology in cancer research & treatment
IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metastasis using primary breast cancer and lymph node features derived from contrast-enhanced mammography...

An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer.

Technology in cancer research & treatment
IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and...

Development and Validation of an Artificial Intelligence Digital Pathology Biomarker to Predict Benefit of Long-Term Hormonal Therapy and Radiotherapy in Men With High-Risk Prostate Cancer Across Multiple Phase III Trials.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Long-term androgen deprivation therapy (ADT) improves survival in men with high-risk localized prostate cancer (PCa) receiving radiotherapy (RT). Predictive biomarkers are needed to guide ADT duration.

Deep learning and conventional hip MRI for the detection of labral and cartilage abnormalities using arthroscopy as standard of reference.

European radiology
OBJECTIVES: To evaluate the performance of high-resolution deep learning-based hip MR imaging (CSAI) compared to standard-resolution compressed sense (CS) sequences using hip arthroscopy as standard of reference.

The impact of ChatGPT on nursing education: A qualitative study based on the experiences of faculty members.

Nurse education today
BACKGROUND: Recent advancements in artificial intelligence technologies particularly ChatGPT have supported learning and fostered critical thinking in nursing education. However, integrating these tools into academic settings requires both ethical an...

Quantification of physiological crystalline Lens decentration using swept source OCT.

European journal of ophthalmology
PurposeTo quantify the decentration of the crystalline lens in a large Austrian adult cataractous and non-cataractous cohort and to predict decentration using biometric parameters.SettingKepler University Clinic, Linz, Austria.DesignRetrospective sin...

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias.

The journal of prevention of Alzheimer's disease
BACKGROUND: Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment.

Prediction of Tumor Budding Grading in Rectal Cancer Using a Multiparametric MRI Radiomics Combined with a 3D Vision Transformer Deep Learning Approach.

Academic radiology
RATIONALE AND OBJECTIVES: The objective is to assess the effectiveness of a multiparametric MRI radiomics strategy combined with a 3D Vision Transformer (ViT) deep learning (DL) model in predicting tumor budding (TB) grading in individuals diagnosed ...