ArticleBiomolecules2023
An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Inflammatory Aortopathies in Rheumatic Diseases: A State-of-the-Art Review.Diagnostics (Basel, Switzerland) · 2026Review
- Aortitis as a High-Risk Vascular Syndrome: Integrating Phenotype-Driven Diagnosis, Multidisciplinary Assessment, and Personalised Management.Journal of personalized medicine · 2026Review
- Building a GUI Tool for Automated Aortic Segmentation in Low-Dose Chest CT Images with PET-Based Standard Uptake Value (SUV) Analysis.Advances in experimental medicine and biology · 2026Article
- A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [Diagnostics (Basel, Switzerland) · 2025Article
- Cardio-rheumatology: integrated care and the opportunities for personalized medicine.Therapeutic advances in musculoskeletal disease · 2025Review
- Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2024Review
- Multimodality imaging to assess diagnosis and evaluate complications of large vesselarteritis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2024Review
- Validation of ChatGPT 3.5 as a Tool to Optimize Readability of Patient-facing Craniofacial Education Materials.Plastic and reconstructive surgery. Global open · 2024Article
- Editorial: Nuclear medicine advances through artificial intelligence and intelligent informatics.Frontiers in nuclear medicine · 2024Article
- The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine.The British journal of radiology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.