Artificial Intelligence-Based Automated Analysis for Pleural Effusion Detection on Thoracic Ultrasound: A Systematic Review
AI-Powered Thoracic Ultrasound for Pleural Effusion: Promising but Premature for Routine Clinical Use
This systematic review critically evaluates the diagnostic accuracy of AI-assisted Thoracic Ultrasound (TUS) for pleural effusion (PE) detection. While demonstrating high performance in curated datasets, significant methodological limitations, including retrospective designs, data leakage, and limited external validation, temper confidence in current findings. Further robust, prospective, and multi-centric studies are essential before widespread clinical implementation.
Executive Impact: AI in Pleural Effusion Diagnostics
AI-assisted TUS shows potential for enhancing diagnostic confidence and standardizing interpretation. Our analysis reveals key performance indicators and critical areas for development before widespread adoption.
Key AI Performance Metrics for PE Detection
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Average Sensitivity (70.6-100%)
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Average Specificity (67-100%)
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Average AUC (0.77-0.99)
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Total Patients Analyzed Across Studies
Deep Analysis & Enterprise Applications
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Enterprise Process Flow: Systematic Review Methodology
Database Search (MEDLINE, Scopus, Google Scholar, IEEE Xplore, Cochrane CENTRAL, ClinicalTrials.gov)
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Duplicate Removal
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Title & Abstract Screening
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Full-Text Eligibility Assessment
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Data Extraction
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Risk of Bias (QUADAS-2) & Certainty (GRADE)
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Structured Narrative Synthesis
This systematic review followed rigorous PRISMA guidelines and was registered with PROSPERO. A comprehensive search identified studies assessing AI-based TUS for PE, with expert interpretation or chest CT serving as reference standards. Due to significant methodological and clinical heterogeneity, a quantitative meta-analysis was deemed inappropriate, and findings were synthesized narratively.
Key AI Diagnostic Performance Ranges
70.6-100%
Sensitivity Range
67-100%
Specificity Range
0.77-0.99
AUC Range
Across 5 studies (7565 patients), AI models using CNNs (ResNet, EfficientNet, U-net) showed strong discriminatory capability in controlled environments. However, performance declined for small, trace, or complex effusions, in critically ill patients, and upon external validation, highlighting challenges in generalizability.
AI as a Complementary Decision-Support Tool
AI-assisted TUS shows promise as a complementary decision-support tool, especially in resource-limited settings and for less-experienced operators, by standardizing interpretation and democratizing access to high-quality ultrasound. However, it is crucial that AI outputs are interpreted within the broader clinical context, integrated with conventional imaging (CT, X-ray), and never replace comprehensive clinical assessment. Real-time quality assessment and transparent confidence reporting are essential to mitigate risks inherent in operator-dependent acquisition and varied scan quality.
The 'Black Box' Challenge: Bridging AI Explanations and Clinician Trust
A significant limitation highlighted in the review is the rudimentary state of explainability (XAI) in AI-assisted TUS models. While some studies employed Grad-CAM heatmaps (Chen, Chaudhary, Hong) to highlight image regions, formal validation of these explanations by clinicians was largely absent. For instance, Chaudhary et al. provided comprehensive temporal confidence plotting but lacked expert assessment to confirm alignment with diagnostic features. Tsai et al. offered implicit localization without visualization. This absence of clinically validated explanations hinders clinician trust, impedes regulatory approval, and makes error detection difficult, rendering these AI models 'black boxes' unsuitable for autonomous deployment.
| Area | Recommendation for Future Research |
|---|---|
| Study Design | Prospective, multicentre studies with consecutive enrollment across diverse settings and device vendors. |
| Validation | Rigorous external and temporal validation with transparent reporting of calibration and uncertainty intervals. |
| Dataset Composition | Inclusion of technically challenging cases (obesity, emphysema) and dedicated pediatric cohorts. |
| Comparative Evaluation | Direct AI-versus-human evaluation using identical test sets and gold-standard references. |
| Explainability | Clinically validated XAI frameworks combining technical visualization with quantitative evaluation and multi-reader assessment. |
| Outcomes | Cost-effectiveness analyses, pragmatic randomized trials evaluating patient outcomes and procedural safety. |
Projected ROI: Quantify Your AI Impact
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Projected Annual Savings
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Annual Hours Reclaimed
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