AIMC Topic: Retrospective Studies
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Longitudinal Model Shifts of Machine Learning-Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals.
Journal of medical Internet research
Dec 13, 2024
BACKGROUND: In recent years, machine learning (ML)-based models have been widely used in clinical domains to predict clinical risk events. However, in production, the performances of such models heavily rely on changes in the system and data. The dyn...
Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer.
JCO clinical cancer informatics
Dec 13, 2024
PURPOSE: This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) using real-world data (RWD) and clinical trial data.
A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.
Frontiers in endocrinology
Dec 13, 2024
OBJECTIVE: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after su...
Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.
Frontiers in immunology
Dec 13, 2024
OBJECTIVE: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast c...
Machine learning-based radiomic features of perivascular adipose tissue in coronary computed tomography angiography predicting inflammation status around atherosclerotic plaque: a retrospective cohort study.
Annals of medicine
Dec 12, 2024
OBJECTIVES: This study expolored the relationship between perivascular adipose tissue (PVAT) radiomic features derived from coronary computed tomography angiography (CCTA) and the presence of coronary artery plaques. It aimed to determine whether PVA...
Prediction of mucinous adenocarcinoma in colorectal cancer with mucinous components detected in preoperative biopsy diagnosis.
Abdominal radiology (New York)
Dec 12, 2024
OBJECTIVES: Endoscopic biopsy diagnosis for the preoperative assessment of mucinous components in patients with colorectal cancer is limited. This study investigated a radiomics model and established an explainable prediction model by using machine l...
Raising awareness may increase the likelihood of hematopoietic stem cell donation: a nationwide survey using artificial intelligence.
International journal of hematology
Dec 12, 2024
BACKGROUND: In Italy, the demand for allogeneic transplantation exceeds the number of compatible donors in the Italian Bone Marrow Donor Registry (IBMDR). This study aimed to explore the knowledge, beliefs, opinions, values, and feelings of the Itali...
Assessment of the stability of intracranial aneurysms using a deep learning model based on computed tomography angiography.
La Radiologia medica
Dec 12, 2024
PURPOSE: Assessment of the stability of intracranial aneurysms is important in the clinic but remains challenging. The aim of this study was to construct a deep learning model (DLM) to identify unstable aneurysms on computed tomography angiography (C...
Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.
Renal failure
Dec 12, 2024
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This stud...
Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study.
Eye (London, England)
Dec 11, 2024
BACKGROUND: Investigate retinal fluid changes via a novel deep-learning algorithm in real-world patients receiving faricimab for the treatment of neovascular age-related macular degeneration (nAMD).
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