دورية أكاديمية
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance.
| العنوان: | Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance. |
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| المؤلفون: | Rueckel J; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Sperl JI; Siemens Healthineers AG, Erlangen, Germany., Kaestle S; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Hoppe BF; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Fink N; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.; Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany., Rudolph J; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Schwarze V; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Geyer T; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Strobl FF; Department of Radiology, University Hospital, LMU Munich, Munich, Germany.; Die Radiologie am Isarklinikum, Munich, Germany., Ricke J; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Ingrisch M; Department of Radiology, University Hospital, LMU Munich, Munich, Germany., Sabel BO; Department of Radiology, University Hospital, LMU Munich, Munich, Germany. |
| المصدر: | Quantitative imaging in medicine and surgery [Quant Imaging Med Surg] 2021 Jun; Vol. 11 (6), pp. 2486-2498. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: AME Pub Country of Publication: China NLM ID: 101577942 Publication Model: Print Cited Medium: Print ISSN: 2223-4292 (Print) Linking ISSN: 22234306 NLM ISO Abbreviation: Quant Imaging Med Surg Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: [Hong Kong] : AME Pub. |
| مستخلص: | Background: Radiology reporting of emergency whole-body computed tomography (CT) scans is time-critical and therefore involves a significant risk of pathology under-detection. We hypothesize a relevant number of initially missed secondary thoracic findings that would have been detected by an artificial intelligence (AI) software platform including several pathology-specific AI algorithms. Methods: This retrospective proof-of-concept-study consecutively included 105 shock-room whole-body CT scans. Image data was analyzed by platform-bundled AI-algorithms, findings were reviewed by radiology experts and compared with the original radiologist's reports. We focused on secondary thoracic findings, such as cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms and vertebral fractures. Results: We identified a relevant number of initially missed findings, with their quantification based on 105 analyzed CT scans as follows: up to 25 patients (23.8%) with cardiomegaly or borderline heart size, 17 patients (16.2%) with coronary plaques, 34 patients (32.4%) with aortic ectasia, 2 patients (1.9%) with lung lesions classified as "recommended to control" and 13 initially missed vertebral fractures (two with an acute traumatic origin). A high number of false positive or non-relevant AI-based findings remain problematic especially regarding lung lesions and vertebral fractures. Conclusions: We consider AI to be a promising approach to reduce the number of missed findings in clinical settings with a necessary time-critical radiological reporting. Nevertheless, algorithm improvement is necessary focusing on a reduction of "false positive" findings and on algorithm features assessing the finding relevance, e.g., fracture age or lung lesion malignancy. Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-1037). Dr. JIS reports personal fees from Siemens Healthcare GmbH, outside the submitted work and during the conduct of the study (employment); Drs. JR and BOS report compensation by Siemens Healthineers for speaker’s activity at conferences. All authors affiliated to LMU Department of Radiology report grants from Siemens Healthcare GmbH, during the conduct of the study (see acknowledgments above). The other authors have no conflicts of interest to declare. (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.) |
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| فهرسة مساهمة: | Keywords: Artificial intelligence (AI); computed tomography (CT); emergency; polytrauma |
| تواريخ الأحداث: | Date Created: 20210603 Latest Revision: 20220423 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC8107306 |
| DOI: | 10.21037/qims-20-1037 |
| PMID: | 34079718 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2223-4292 |
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| DOI: | 10.21037/qims-20-1037 |