ArticlePloS one2025
A deep learning AI model for determining the relationship between X-Ray detectors and patient positioning in chest radiography.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe objective of this study was to create an artificial intelligence (AI) system capable of automatically detecting the positional relationship between an X-ray detector and the patient during anteroposterior chest radiography.
methodsIn this study, a total of 22299 images depicting the positional states of X-ray detectors relative to patients were used to develop an AI system for the automatic determination of X-ray detector status. The images were captured from Routine clinical chest X-ray radiography practice settings, without exposing any patient privacy, adhering to the Declaration of Helsinki. The PyTorch library was utilized for customizing a Convolutional Neural Network (CNN) model for the training of the chest radiography positional determination model.
resultsThe average accuracy of Model A on the validation set was 0.9668, with an average loss function value of 0.1078. In contrast, Model B achieved an average accuracy of 0.9776, also with an average loss function value of 0.0970. In the test set results, Model A -fold 4 demonstrated a true negative rate (TNR) of 0.9925, negative predictive value (NPV) of 0.9925, precision of 0.9699, recall of 0.9700, accuracy of 0.9700, and an F1 score of 0.9699. Model B -fold 1exhibited a TNR of 0.9946, NPV of 0.9946, precision of 0.9787, recall of 0.9784, accuracy of 0.9784, and an F1 score of 0.9784. McNemar's test indicated statistical difference between the two models.
conclusionThe AI model utilizing a customized CNN architecture has demonstrated its potential to automatically detect the positional relationship between the patient and the X-ray detector during chest radiography procedures. This model can potentially alleviate the workload of radiologic technologists in producing chest radiographs and enhance the accuracy of the imaging process.
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.