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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Diagnostic performance of artificial intelligence in radiographs for pneumoperitoneum detection: a systematic review
Ruaa Mustafa Qafesha1,2, Mahmoud Diaa Hindawi2,3, Israa Sharabati1,2
1Faculty of Medicine, Al-Quds University, Jerusalem, Palestine.
Objectives:
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.
Methods:
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.
Results:
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 < .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' funnel plot showed no publication bias.
Conclusions:
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.
Advances In Knowledge:
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.
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