Diagnostic accuracy of convolutional neural network algorithms to distinguish gastrointestinal obstruction on conventional radiographs in a pediatric population
This paper describes a novel area of utilization of well-known deep learning algorithm models. Although some studies in the literature have shown the efficiency of CNN models in identifying small bowel obstruction with high accuracy for the adult population or some specific diseases, our study is unique for the pediatric population and for evaluating the requirement of surgical versus medical treatment.
Authors
- Hasan Güçlü (ORCID: https://orcid.org/0000-0003-3582-9460)
- Ercan Ayaz (ORCID: https://orcid.org/0000-0003-2726-2097)
- Ayşe Betül Oktay (ORCID: https://orcid.org/0000-0003-0827-173X)
Institutions
- TOBB University of Economics and Technology (TR)
- Sağlık Bilimleri Üniversitesi (TR)
- Istanbul Technical University (TR)
- Istanbul Medeniyet University (TR)
- University of Health Sciences Antigua (AG)
Publication Details
- Journal
- Diagnostic and Interventional Radiology
- Published
- 2025-02-28
- DOI
- https://doi.org/10.4274/dir.2025.242950
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72