Diagnostic performance of artificial intelligence in radiographs for pneumoperitoneum detection: a systematic review and meta-analysis
This is the first meta-analysis to evaluate AI's diagnostic accuracy for pneumoperitoneum, revealing high sensitivity and specificity, comparing deep learning and machine learning performance, and highlighting AI's potential to enhance early diagnosis and prioritization in clinical workflows.
Authors
- Ruaa Mustafa Qafesha (ORCID: https://orcid.org/0000-0002-8745-0260)
- Amir Ali (ORCID: https://orcid.org/0000-0001-8436-3316)
- Ammar Albostani (ORCID: https://orcid.org/0000-0002-7213-7722)
- Israa Sharabati
- Mahmoud Diaa Hindawi (ORCID: https://orcid.org/0009-0001-3085-6665)
- Muhi Dervis
- Qasi Najah
Institutions
- University of Aleppo (SY)
- Al-Azhar University (EG)
- Al-Quds University (PS)
- National Research Mordovia State University (RU)
- Massachusetts General Hospital (US)
- Al-Azhar University (ID)
- University Elmergib (LY)
- Military Medical Academy (EG)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1093/bjr/tqaf309
- Citations
- 1
- Primary Topic
- Appendicitis Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 2.25