AIMC Topic: Retrospective Studies
Clear Filters Showing 161 to 170 of 9137 articles
Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).
International ophthalmology
May 5, 2025
PURPOSE: To predict nomogram for small incision lenticule extraction (SMILE) using machine learning technology and preoperative clinical data.
Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.
Medical physics
May 3, 2025
BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studies have shown that a high proportion of these patients will experience a recurrence after their resec...
Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.
European journal of medical research
May 3, 2025
OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). Accurate and interpretable mortality predic...
Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.
BMC pregnancy and childbirth
May 3, 2025
OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk as...
Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.
BMC medical imaging
May 3, 2025
OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. Thi...
Meningeal Neoplasms
Male
Sella Turcica
Middle Aged
Craniopharyngioma
Female
Adolescent
Central Nervous System Cysts
Aged
Magnetic Resonance Imaging
Support Vector Machine
Young Adult
Pituitary Neoplasms
Humans
Adult
Adenoma
Machine Learning
Radiomics
Diagnosis, Differential
Retrospective Studies
Meningioma
Surgical and radiological outcomes of giant cell tumor of the bone: prognostic value of Campanacci grading and selective use of denosumab.
Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology
May 3, 2025
BACKGROUND: Advancements in diagnostic and therapeutic modalities for giant cell tumors of bone (GCTB) have introduced molecular and radiological tools that refine clinical decision-making. H3.3 G34W immunohistochemical staining has become a routine ...
Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study.
Academic radiology
May 2, 2025
RATIONALE AND OBJECTIVES: To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.
Detecting the left atrial appendage in CT localizers using deep learning.
Scientific reports
May 2, 2025
Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example, to determine whether thrombi are present in the LAA. To guide the imaging process, technologists first perform a localizer scan, which is a prelimina...
Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: The automatic recognition of intracraial aneurysms by means of machine-learning algorithms represents a new frontier for diagnostic and therapeutic goals. Yet, the current algorithms focus solely on the aneurysms and not on th...
Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: The diagnostic performance of deep learning model that simultaneously detecting and quantifying nigrosome-1 abnormality by using susceptibility map-weighted imaging (SMwI) remains unexplored. This study aimed to develop and va...
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