Correlating Patient Symptoms and CT Morphology in AI-Detected Incidental Pulmonary Embolisms

Background/Objectives: Incidental pulmonary embolisms (IPEs) may be asymptomatic and radiologists may discover them for unrelated reasons, and they can thereby go underdiagnosed and undertreated. Artificial intelligence (AI) has emerged as a possible aid to radiologists in identifying IPEs. This study aimed to assess the clinical and radiological significance of IPEs that a deep learning AI algorithm detected and correlate them with thrombotic burden, CT morphologic signs of right heart strain, and clinical symptoms. Methods: We retrospectively evaluated 13,603 contrast-enhanced thoracic and abdominal CT scans performed over one year at a tertiary care hospital using a CE- and FDA-cleared AI algorithm. Natural language processing (NLP) tools were used to determine whether IPEs were reported by radiologists. We scored confirmed IPEs using the Mastora, Qanadli, Ghanima, and Kirchner scores, and morphologic indicators of right heart strain and clinical parameters such as symptomatology, administered anticoagulation, and 6-month outcomes were analyzed. Results: AI identified 41 IPE cases, of which 61% occurred in oncologic patients. Most emboli were segmental, with no signs of right heart strain. Only 10% of patients were symptomatic. Thrombotic burden scores were similar between oncologic and non-oncologic groups. Four deaths occurred—all in oncologic patients. One untreated case experienced the recurrence of pulmonary embolism. Despite frequent detection, many IPEs were clinically silent. Conclusions: AI can effectively detect IPEs that are missed on initial review. However, most AI-detected IPEs are clinically silent. Integrating AI findings with morphologic and clinical criteria is crucial to avoid overtreatment and to guide appropriate management.