Key result
Combined AI and radiologist review linked to ~76% fewer PE diagnostic errors vs manual reading.
Why the study?
Rising CTPA case volumes and radiologist shortages challenge pulmonary embolism diagnosis, creating a need to validate commercial AI systems alone and combined with radiologist interpretation.
Does a combined radiologist and AI interpretation approach improve diagnostic accuracy and reduce reading time for pulmonary embolism detection on CTPA compared to radiologist interpretation alone?
Cohort (n=600)
Yes
Does a combined radiologist and AI interpretation approach improve diagnostic accuracy and reduce reading time for pulmonary embolism detection on CTPA compared to radiologist interpretation alone?
Absolute Event Rate: 271% vs 329%
p-value: p=<0.001
Integrating AI with manual radiologist interpretation significantly improves the diagnostic accuracy and speed of pulmonary embolism detection on CTPA compared to radiologist interpretation alone.
May improve accuracy and speed of PE detection on CTPA via AI-radiologist synergy; extends evidence for hybrid imaging interpretation.
Background Pulmonary embolism (PE) is a critical cardiovascular emergency requiring prompt, accurate diagnosis. CT pulmonary angiography (CTPA) is the diagnostic gold standard, yet rising case volumes and radiologist shortages challenge clinical workflows. Artificial intelligence (AI) offers potential to enhance diagnostic precision and efficiency. This multicenter study validates the performance of a commercially available AI system compared with radiologist interpretation alone and in combination. Methods In this retrospective analysis, 600 consecutive patients suspected of PE underwent CTPA between January 2024 and May 2025 at three hospitals in Xinjiang. All scans employed 256-slice CT with standardized protocols (100 kV, 0.625 mm slice thickness, iohexol contrast). Images were processed using uAIDiscover PE software, generating Pulmonary Thrombus Burden Score (PTBS). Manual Pulmonary Artery Obstruction Index (PAOI) was independently scored via the Qanadli system by consensus of three senior radiologists, serving as the reference standard. Diagnostic accuracy and correlation between AI and manual scores were assessed (SPSS 24.0; P < 0.05). Results Among 600 patients analyzed, 271 (45.2%) had pulmonary embolism. PE patients had significantly higher BMI and greater prevalence of hypertension and coronary artery disease (P < 0.05). ROC analysis demonstrated superior diagnostic performance for the combined manual + AI approach across all centers (AUC: 0.928–0.934) compared to AI alone (AUC: 0.807–0.810) or manual reading alone (AUC: 0.888–0.914). AI processing was remarkably fast at 0.19 ± 0.02 min versus 5.26 ± 0.94 min for radiologists alone, while combined approach required 2.61 ± 0.69 min. Strong correlation was observed between AI-derived PTBS and manually calculated PAOI (r = 0.863, P < 0.001). The combined approach significantly reduced diagnostic errors to 7 cases compared to 43 for AI alone and 29 for manual reading alone. Conclusion Integration of AI with manual interpretation improves pulmonary embolism detection accuracy and reduces reading time, supporting its implementation to optimize clinical workflow and patient outcomes.
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Tudi et al. (2026) conducted a cohort in Pulmonary Embolism (n=600). AI-assisted interpretation vs. Radiologist interpretation was evaluated on Detection of pulmonary embolism (p=<0.001). The combined AI and radiologist approach reduced diagnostic errors to 7 cases compared to 43 for AI alone and 29 for manual reading alone, demonstrating improved accuracy in detecting pulmonary embolism.
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