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Miró Catalina Q, Vidal-Alaball J, Fuster-Casanovas A, Escalé-Besa A, Ruiz Comellas A, Solé-Casals J. Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings. Sci Rep 2024; 14:5199. [PMID: 38431731 PMCID: PMC10908781 DOI: 10.1038/s41598-024-55792-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
Interpreting chest X-rays is a complex task, and artificial intelligence algorithms for this purpose are currently being developed. It is important to perform external validations of these algorithms in order to implement them. This study therefore aims to externally validate an AI algorithm's diagnoses in real clinical practice, comparing them to a radiologist's diagnoses. The aim is also to identify diagnoses the algorithm may not have been trained for. A prospective observational study for the external validation of the AI algorithm in a region of Catalonia, comparing the AI algorithm's diagnosis with that of the reference radiologist, considered the gold standard. The external validation was performed with a sample of 278 images and reports, 51.8% of which showed no radiological abnormalities according to the radiologist's report. Analysing the validity of the AI algorithm, the average accuracy was 0.95 (95% CI 0.92; 0.98), the sensitivity was 0.48 (95% CI 0.30; 0.66) and the specificity was 0.98 (95% CI 0.97; 0.99). The conditions where the algorithm was most sensitive were external, upper abdominal and cardiac and/or valvular implants. On the other hand, the conditions where the algorithm was less sensitive were in the mediastinum, vessels and bone. The algorithm has been validated in the primary care setting and has proven to be useful when identifying images with or without conditions. However, in order to be a valuable tool to help and support experts, it requires additional real-world training to enhance its diagnostic capabilities for some of the conditions analysed. Our study emphasizes the need for continuous improvement to ensure the algorithm's effectiveness in primary care.
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Affiliation(s)
- Queralt Miró Catalina
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Carrer Pica d'Estats, 13-15, 08272, Sant Fruitós de Bages, Barcelona, Spain
- Faculty of Science Technology and Engineering, University of Vic-Central University of Catalonia, Vic, Spain
| | - Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain.
- Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Carrer Pica d'Estats, 13-15, 08272, Sant Fruitós de Bages, Barcelona, Spain.
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain.
| | - Aïna Fuster-Casanovas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Carrer Pica d'Estats, 13-15, 08272, Sant Fruitós de Bages, Barcelona, Spain
| | - Anna Escalé-Besa
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Carrer Pica d'Estats, 13-15, 08272, Sant Fruitós de Bages, Barcelona, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Anna Ruiz Comellas
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain
- Health Promotion in Rural Areas Research Group, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Carrer Pica d'Estats, 13-15, 08272, Sant Fruitós de Bages, Barcelona, Spain
- Faculty of Medicine, University of Vic-Central University of Catalonia, Vic, Spain
| | - Jordi Solé-Casals
- Data and Signal Processing Group, Faculty of Science, Technology and Engineering, University of Vic-Central University of Catalonia, Vic, Spain.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
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Bennani S, Regnard NE, Ventre J, Lassalle L, Nguyen T, Ducarouge A, Dargent L, Guillo E, Gouhier E, Zaimi SH, Canniff E, Malandrin C, Khafagy P, Koulakian H, Revel MP, Chassagnon G. Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs. Radiology 2023; 309:e230860. [PMID: 38085079 DOI: 10.1148/radiol.230860] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Background Chest radiography remains the most common radiologic examination, and interpretation of its results can be difficult. Purpose To explore the potential benefit of artificial intelligence (AI) assistance in the detection of thoracic abnormalities on chest radiographs by evaluating the performance of radiologists with different levels of expertise, with and without AI assistance. Materials and Methods Patients who underwent both chest radiography and thoracic CT within 72 hours between January 2010 and December 2020 in a French public hospital were screened retrospectively. Radiographs were randomly included until reaching 500 radiographs, with about 50% of radiographs having abnormal findings. A senior thoracic radiologist annotated the radiographs for five abnormalities (pneumothorax, pleural effusion, consolidation, mediastinal and hilar mass, lung nodule) based on the corresponding CT results (ground truth). A total of 12 readers (four thoracic radiologists, four general radiologists, four radiology residents) read half the radiographs without AI and half the radiographs with AI (ChestView; Gleamer). Changes in sensitivity and specificity were measured using paired t tests. Results The study included 500 patients (mean age, 54 years ± 19 [SD]; 261 female, 239 male), with 522 abnormalities visible on 241 radiographs. On average, for all readers, AI use resulted in an absolute increase in sensitivity of 26% (95% CI: 20, 32), 14% (95% CI: 11, 17), 12% (95% CI: 10, 14), 8.5% (95% CI: 6, 11), and 5.9% (95% CI: 4, 8) for pneumothorax, consolidation, nodule, pleural effusion, and mediastinal and hilar mass, respectively (P < .001). Specificity increased with AI assistance (3.9% [95% CI: 3.2, 4.6], 3.7% [95% CI: 3, 4.4], 2.9% [95% CI: 2.3, 3.5], and 2.1% [95% CI: 1.6, 2.6] for pleural effusion, mediastinal and hilar mass, consolidation, and nodule, respectively), except in the diagnosis of pneumothorax (-0.2%; 95% CI: -0.36, -0.04; P = .01). The mean reading time was 81 seconds without AI versus 56 seconds with AI (31% decrease, P < .001). Conclusion AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Souhail Bennani
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Nor-Eddine Regnard
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Jeanne Ventre
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Louis Lassalle
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Toan Nguyen
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Alexis Ducarouge
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Lucas Dargent
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Enora Guillo
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Elodie Gouhier
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Sophie-Hélène Zaimi
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Emma Canniff
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Cécile Malandrin
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Philippe Khafagy
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Hasmik Koulakian
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Marie-Pierre Revel
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
| | - Guillaume Chassagnon
- From the Department of Thoracic Imaging, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France (S.B., L.D., E. Guillo, E. Gouhier, S.H.Z., E.C., M.P.R., G.C.); Gleamer, Paris, France (S.B., N.E.R., J.V., L.L., T.N., A.D.); Réseau d'Imagerie Sud Francilien, Lieusant, France (N.E.R., L.L., C.M.); Department of Pediatric Radiology, Armand Trousseau Hospital, AP-HP, Paris, France (T.N.); HFR Fribourg, Fribourg, Switzerland (P.K.); and Centre d'Imagerie Médicale de l'Ouest Parisien, Paris, France (H.K.)
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Lind Plesner L, Müller FC, Brejnebøl MW, Laustrup LC, Rasmussen F, Nielsen OW, Boesen M, Brun Andersen M. Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural Effusion. Radiology 2023; 308:e231236. [PMID: 37750768 DOI: 10.1148/radiol.231236] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Background Commercially available artificial intelligence (AI) tools can assist radiologists in interpreting chest radiographs, but their real-life diagnostic accuracy remains unclear. Purpose To evaluate the diagnostic accuracy of four commercially available AI tools for detection of airspace disease, pneumothorax, and pleural effusion on chest radiographs. Materials and Methods This retrospective study included consecutive adult patients who underwent chest radiography at one of four Danish hospitals in January 2020. Two thoracic radiologists (or three, in cases of disagreement) who had access to all previous and future imaging labeled chest radiographs independently for the reference standard. Area under the receiver operating characteristic curve, sensitivity, and specificity were calculated. Sensitivity and specificity were additionally stratified according to the severity of findings, number of findings on chest radiographs, and radiographic projection. The χ2 and McNemar tests were used for comparisons. Results The data set comprised 2040 patients (median age, 72 years [IQR, 58-81 years]; 1033 female), of whom 669 (32.8%) had target findings. The AI tools demonstrated areas under the receiver operating characteristic curve ranging 0.83-0.88 for airspace disease, 0.89-0.97 for pneumothorax, and 0.94-0.97 for pleural effusion. Sensitivities ranged 72%-91% for airspace disease, 63%-90% for pneumothorax, and 62%-95% for pleural effusion. Negative predictive values ranged 92%-100% for all target findings. In airspace disease, pneumothorax, and pleural effusion, specificity was high for chest radiographs with normal or single findings (range, 85%-96%, 99%-100%, and 95%-100%, respectively) and markedly lower for chest radiographs with four or more findings (range, 27%-69%, 96%-99%, 65%-92%, respectively) (P < .001). AI sensitivity was lower for vague airspace disease (range, 33%-61%) and small pneumothorax or pleural effusion (range, 9%-94%) compared with larger findings (range, 81%-100%; P value range, > .99 to < .001). Conclusion Current-generation AI tools showed moderate to high sensitivity for detecting airspace disease, pneumothorax, and pleural effusion on chest radiographs. However, they produced more false-positive findings than radiology reports, and their performance decreased for smaller-sized target findings and when multiple findings were present. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Yanagawa and Tomiyama in this issue.
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Affiliation(s)
- Louis Lind Plesner
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Felix C Müller
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Mathias W Brejnebøl
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Lene C Laustrup
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Finn Rasmussen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Olav W Nielsen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Mikael Boesen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
| | - Michael Brun Andersen
- From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., O.W.N., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Capital Region of Denmark (L.L.P., F.C.M., M.W.B., M.B., M.B.A.); Departments of Radiology (M.W.B., M.B.) and Cardiology (O.W.N.), Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; and Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.)
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