ArticlePediatric surgery international2023
DraiNet: AI-driven decision support in pneumothorax and pleural effusion management.
Article in Pediatric surgery international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- AI-Assisted Preoperative Diagnosis of Wilms Tumor.Life (Basel, Switzerland) · 2026Article
- The role of AI assistance in the evaluation of unenhanced chest CT scans in an emergency setting.Polish journal of radiology · 2026Article
- Clinical decision-making frameworks for pandemic preparedness and response: A scoping review.Health promotion perspectives · 2025Review
- Development of an AI model for pneumothorax imaging: Dataset and model optimization strategies for real-world deployment.European journal of radiology open · 2025Article
- Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review.European respiratory review : an official journal of the European Respiratory Society · 2025Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study presents DraiNet, a deep learning model developed to detect pneumothorax and pleural effusion in pediatric patients and aid in assessing the necessity for tube thoracostomy. The primary goal is to utilize DraiNet as a decision support tool to enhance clinical decision-making in the management of these conditions.
methodsDraiNet was trained on a diverse dataset of pediatric CT scans, carefully annotated by experienced surgeons. The model incorporated advanced object detection techniques and underwent evaluation using standard metrics, such as mean Average Precision (mAP), to assess its performance.
resultsDraiNet achieved an impressive mAP score of 0.964, demonstrating high accuracy in detecting and precisely localizing abnormalities associated with pneumothorax and pleural effusion. The model's precision and recall further confirmed its ability to effectively predict positive cases.
conclusionThe integration of DraiNet as an AI-driven decision support system marks a significant advancement in pediatric healthcare. By combining deep learning algorithms with clinical expertise, DraiNet provides a valuable tool for non-surgical teams and emergency room doctors, aiding them in making informed decisions about surgical interventions. With its remarkable mAP score of 0.964, DraiNet has the potential to enhance patient outcomes and optimize the management of critical conditions, including pneumothorax and pleural effusion.
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Registered trials
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