{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T03:39:04Z","timestamp":1787974744725,"version":"build-2784847793"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"1177","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of artificial intelligence (AI) based algorithms in detecting pneumoperitoneum on medical imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Online databases were searched until June 2024. Statistical analyses were conducted using Open Meta-Analyst software and STATA 17.0. The analysis included overall sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Meta-regression and subgroup analyses were conducted to identify sources of heterogeneity among the included studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the 14 AI-based radiograph models analysed, AI demonstrated high diagnostic accuracy for pneumoperitoneum, with a sensitivity of 83.6% (95% CI: 80.2%-86.4%), specificity of 92.9% (95% CI: 88.3%-95.8%), negative likelihood ratio of 0.18, and positive likelihood ratio of 11.76 (all P\u2009&amp;lt;\u2009.001). Deep learning models showed higher sensitivity (83.7%) but slightly lower specificity (91.2%) compared to machine learning models (sensitivity 77%, specificity 98%). The AUC was 0.93, with a DOR of 76. Meta-regression revealed larger sample sizes significantly improved specificity. Deeks\u2019 funnel plot showed no publication bias.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI models are effective in diagnosing pneumoperitoneum. The high accuracy of these models enhances the potential for rapid and precise detection, thereby improving patient management. Future prospective multicentre studies with larger sample sizes and comparisons of various models are highly anticipated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>This is the first meta-analysis to evaluate AI\u2019s diagnostic accuracy for pneumoperitoneum, revealing high sensitivity and specificity, comparing deep learning and machine learning performance, and highlighting AI\u2019s potential to enhance early diagnosis and prioritization in clinical workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqaf309","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T18:23:29Z","timestamp":1766600609000},"page":"37-49","source":"Crossref","is-referenced-by-count":2,"title":["Diagnostic performance of artificial intelligence in radiographs for pneumoperitoneum detection: a systematic review and meta-analysis"],"prefix":"10.1093","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8745-0260","authenticated-orcid":false,"given":"Ruaa Mustafa","family":"Qafesha","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Al-Quds University , Jerusalem,","place":["Palestine"]},{"name":"Medical Research Group of Egypt, Negida Academy , Arlington, MA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3085-6665","authenticated-orcid":false,"given":"Mahmoud Diaa","family":"Hindawi","sequence":"additional","affiliation":[{"name":"Medical Research Group of Egypt, Negida Academy , Arlington, MA,","place":["United States"]},{"name":"Faculty of Medicine, Al-Azhar University , Cairo,","place":["Egypt"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2934-7151","authenticated-orcid":false,"given":"Israa","family":"Sharabati","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Al-Quds University , Jerusalem,","place":["Palestine"]},{"name":"Medical Research Group of Egypt, Negida Academy , Arlington, MA,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5914-6462","authenticated-orcid":false,"given":"Muhiddin","family":"Dervis","sequence":"additional","affiliation":[{"name":"Medical Research Group of Egypt, Negida Academy , Arlington, MA,","place":["United States"]},{"name":"eHarvard Medical School, Massachusetts General Hospital ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0460-507X","authenticated-orcid":false,"given":"Qasi","family":"Najah","sequence":"additional","affiliation":[{"name":"Medical Research Group of Egypt, Negida Academy , Arlington, MA,","place":["United States"]},{"name":"Faculty of Medicine, Elmergib University , Alkhums,","place":["Libya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7213-7722","authenticated-orcid":false,"given":"Ammar","family":"Albostani","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Aleppo , Aleppo, Syria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2706-5275","authenticated-orcid":false,"given":"Ahmed Hamdy","family":"G. 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