An AI-driven algorithm (CINA-PE) demonstrated 93.9% sensitivity and 94.8% specificity for detecting pulmonary embolism on CT pulmonary angiography, reducing the miss rate from 15.6% to 3.8%.
Observational (n=1,204)
Yes
Does an AI-driven algorithm (CINA-PE) improve the detection of pulmonary embolism on CT pulmonary angiography compared to standard clinical reporting?
An AI-enabled tool for CT pulmonary angiography demonstrated high sensitivity and specificity, significantly reducing the rate of missed pulmonary embolisms compared to standard clinical reports.
Effect estimate: Specificity 94.8% (95% CI 89.3%-96.9%)
PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA). PATIENTS AND METHODS: CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed. RESULTS: A total of 1204 CTPAs (mean age 62.1 years ± 16.6SD, 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186). CONCLUSIONS: The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.
Ayobi et al. (Tue,) conducted a observational in Pulmonary embolism (n=1,204). CINA-PE (AI-driven algorithm) vs. Clinical report / Expert radiologist ground truth was evaluated on Per-case sensitivity for PE detection (Specificity 94.8%, 95% CI 89.3%-96.9%). An AI-driven algorithm (CINA-PE) demonstrated 93.9% sensitivity and 94.8% specificity for detecting pulmonary embolism on CT pulmonary angiography, reducing the miss rate from 15.6% to 3.8%.
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