AIMC Topic: Retrospective Studies
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Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.
Annals of surgical oncology
Feb 20, 2025
BACKGROUND: Postdischarge venous thromboembolism (pdVTE) is a life-threatening complication following resection for pancreatic cancer (PC). While national guidelines recommend extended chemoprophylaxis for all, adherence is low and ranges from 1.5 to...
Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.
American journal of ophthalmology
Feb 20, 2025
PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection of anti-vascular endothelial growth factor (anti-VEGF) with data from optical coherence tomography ...
Pilot Projects
Ranibizumab
Visual Acuity
Female
Humans
Bevacizumab
Angiogenesis Inhibitors
Aged
Tomography, Optical Coherence
Diabetic Retinopathy
Treatment Outcome
Male
Macular Edema
Neural Networks, Computer
Follow-Up Studies
Vascular Endothelial Growth Factor A
Middle Aged
Retrospective Studies
Intravitreal Injections
Convolutional Neural Networks
T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.
Journal of applied clinical medical physics
Feb 20, 2025
PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).
Predicting hepatocellular carcinoma survival with artificial intelligence.
Scientific reports
Feb 20, 2025
Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in pred...
Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.
Journal of cancer research and clinical oncology
Feb 20, 2025
PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients...
Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study.
European journal of nuclear medicine and molecular imaging
Feb 19, 2025
PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate o...
Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.
European journal of nuclear medicine and molecular imaging
Feb 19, 2025
PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [F]PSMA-1007 PET/CT ...
CT-Based Machine Learning Radiomics Analysis to Diagnose Dysthyroid Optic Neuropathy.
Seminars in ophthalmology
Feb 19, 2025
PURPOSE: To develop CT-based machine learning radiomics models used for the diagnosis of dysthyroid optic neuropathy (DON).
Accuracy of cephalometric landmark identification by artificial intelligence platform versus expert orthodontist in unilateral cleft palate patients: A retrospective study.
International orthodontics
Feb 19, 2025
OBJECTIVE: The primary aim of the study was to evaluate the accuracy of automated artificial intelligence (AI) cephalometric landmark identification in cleft patients and compare it to landmarks identified by an expert orthodontist. The secondary obj...
Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography.
American journal of ophthalmology
Feb 19, 2025
PURPOSE: To develop and validate a deep learning (DL) model to differentiate ocular surface squamous neoplasia (OSSN) from pterygium and pinguecula using high-resolution anterior segment optical coherence tomography (AS-OCT).
Retrospective Studies
Female
Corneal Diseases
Carcinoma, Squamous Cell
Pterygium
Reproducibility of Results
Conjunctival Neoplasms
Aged
Humans
Artificial Intelligence
Middle Aged
Deep Learning
Diagnosis, Differential
Tomography, Optical Coherence
Anterior Eye Segment
Aged, 80 and over
Adult
Sensitivity and Specificity
Male
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