AIMC Topic: Retrospective Studies
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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).
Pterygium
Conjunctival Neoplasms
Adult
Tomography, Optical Coherence
Carcinoma, Squamous Cell
Sensitivity and Specificity
Deep Learning
Humans
Anterior Eye Segment
Reproducibility of Results
Male
Diagnosis, Differential
Artificial Intelligence
Corneal Diseases
Aged
Middle Aged
Retrospective Studies
Aged, 80 and over
Female
Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification.
Computers in biology and medicine
Feb 19, 2025
BACKGROUND: Breast cancer is the most common cancer worldwide, and magnetic resonance imaging (MRI) constitutes a very sensitive technique for invasive cancer detection. When reviewing breast MRI examination, clinical radiologists rely on multimodal ...
Diagnosis of Acute Appendicitis with Machine Learning-Based Computer Tomography: Diagnostic Reliability and Role in Clinical Management.
Journal of laparoendoscopic & advanced surgical techniques. Part A
Feb 19, 2025
Acute appendicitis (AA) is a common surgical emergency affecting 7-8% of the population. Timely diagnosis and treatment are crucial for preventing serious morbidity and mortality. Diagnosis typically involves physical examination, laboratory tests, ...
Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients.
Journal of the American Heart Association
Feb 19, 2025
BACKGROUND: Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been...
Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.
Journal of intensive care medicine
Feb 18, 2025
BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mortality in AP. This study evaluates the association between sepsis occurrence and all-cause mortality ...
Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.
European journal of nuclear medicine and molecular imaging
Feb 18, 2025
PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ c...
Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.
Medical physics
Feb 18, 2025
BACKGROUND: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses includin...
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