An AI algorithm enhanced radiologists' detection of incidental pulmonary emboli (IPE) on routine contrast-enhanced CT studies.
Key Details
- 1Study assessed 14,453 contrast-enhanced outpatient CT chest/abdomen/pelvis exams for IPEs using Aidoc's AI algorithm.
- 2AI detected 224 IPE cases, 36 of which radiologists missed; these were mostly small and distal emboli.
- 3AI missed 30 IPEs that radiologists found, most being small, with one large central embolus missed.
- 4Radiologist re-review of 1,400 negative cases found 8 additional IPEs missed initially.
- 5Specificity for identifying IPE: 65.1% for radiologists, 66.9% for AI, and 75.8% when combined.
- 6AI showed similar sensitivity as radiologists but a lower positive predictive value; false positive rate noted.
Why It Matters
Automated AI second reads can improve detection of incidental findings on routine CT exams, suggesting a synergistic workflow where AI and human expertise together outperform either alone. This has potential clinical impact for patient outcomes and radiology practice, though current AI tools require continued human oversight.
Source
AuntMinnie
Related News
•Radiology Business
Patients Favor AI in Imaging Diagnostics, Hesitate on Triage Use
Survey finds most patients support AI in diagnostic imaging but are reluctant about its use in triage decisions.
•AuntMinnie
Deep Learning AI Outperforms Radiologists in Detecting ENE on CT
A deep learning tool, DeepENE, exceeded radiologist performance in identifying lymph node extranodal extension in head and neck cancers using preoperative CT scans.
•Radiology Business
FDA Clears Multi-Disease AI Screening Platform for CT Imaging
HeartLung Corporation's AI-CVD platform receives FDA clearance to detect multiple diseases from a single CT scan.