Abstract
Purpose Since artificial intelligence is transitioning from an experimental stage to clinical
implementation, the aim of our study was to evaluate the performance of a commercial,
computer-aided detection algorithm of computed tomography pulmonary angiograms regarding
the presence of pulmonary embolism in the emergency room.
Materials and Methods This retrospective study includes all pulmonary computed tomography angiogram studies
performed in a large emergency department over a period of 36 months that were analyzed
by two radiologists experienced in emergency radiology to set a reference standard.
Original reports and computer-aided detection results were compared regarding the
detection of lobar, segmental, and subsegmental pulmonary embolism. All computer-aided
detection findings were analyzed concerning the underlying pathology. False-positive
findings were correlated to the contrast-to-noise ratio.
Results Expert reading revealed pulmonary embolism in 182 of 1229 patients (49 % men, 10–97
years) with a total of 504 emboli. The computer-aided detection algorithm reported
3331 findings, including 258 (8 %) true-positive findings and 3073 (92 %) false-positive
findings. Computer-aided detection analysis showed a sensitivity of 47 % (95 %CI:
33–61 %) on the lobar level and 50 % (95 %CI 43–56 %) on the subsegmental level. On
average, there were 2.25 false-positive findings per study (median 2, range 0–25).
There was no significant correlation between the number of false-positive findings
and the contrast-to-noise ratio (Spearman’s Rank Correlation Coefficient = 0.09).
Soft tissue (61.0 %) and pulmonary veins (24.1 %) were the most common underlying
reasons for false-positive findings.
Conclusion Applied to a population at a large emergency room, the tested commercial computer-aided
detection algorithm faced relevant performance challenges that need to be addressed
in future development projects.
Key Points:
Computed tomography pulmonary angiograms are frequently acquired in emergency radiology.
Computer-aided detection algorithms (CADs) can support image analysis.
CADs face challenges regarding false-positive and false-negative findings.
Radiologists using CADs need to be aware of these limitations.
Further software improvements are necessary ahead of implementation in the daily routine.
Citation Format
Müller-Peltzer K, Kretzschmar L, Negrão de Figueiredo G et al. Present Limitations
of Artificial Intelligence in the Emergency Setting – Performance Study of a Commercial,
Computer-Aided Detection Algorithm for Pulmonary Embolism. Fortschr Röntgenstr 2021;
193: 1436 – 1444
Key words pulmonary embolism - CAD - CT pulmonary angiogram - computer-aided detection
