Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism. / Grenzen künstlicher Intelligenz in der Notfallbefundung eine Leistungsanalyse eines kommerziellen, computerbasierten Algorithmus zur Detektion von Lungenarterienembolien.
Rofo
; 193(12): 1436-1444, 2021 Dec.
Article
em En, De
| MEDLINE
| ID: mdl-34352914
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 ANDMETHODS:
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 (49â% men, 10-97 years) with a total of 504 emboli. The computer-aided detection algorithm reported 3331 findings, including 258 (8â%) true-positive findings and 3073 (92â%) false-positive findings. Computer-aided detection analysis showed a sensitivity of 47â% (95â%CI 33-61â%) on the lobar level and 50â% (95â%CI 43-56â%) 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 Coefficientâ=â0.09). Soft tissue (61.0â%) and pulmonary veins (24.1â%) 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 etâal. 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 1436â-â1444.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Embolia Pulmonar
/
Inteligência Artificial
Idioma:
De
/
En
Ano de publicação:
2021
Tipo de documento:
Article