دورية أكاديمية
أعرض في EDS

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions.

التفاصيل البيبلوغرافية
العنوان: Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions.
المؤلفون: Rueckel J; From the Department of Radiology, University Hospital, LMU Munich, Munich, Germany (J.Rueckel, C.S., B.F.H., J.Ricke, J.Rudolph, B.O.S.); Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany (J.Rueckel, T.L.); and XP Technology and Innovation, Siemens Healthcare GmbH, Forchheim, Germany (C.H., G.B.)., Huemmer C, Shahidi C, Buizza G, Hoppe BF, Liebig T, Ricke J, Rudolph J, Sabel BO
المصدر: Investigative radiology [Invest Radiol] 2024 Apr 01; Vol. 59 (4), pp. 306-313. Date of Electronic Publication: 2023 Sep 08.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0045377 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-0210 (Electronic) Linking ISSN: 00209996 NLM ISO Abbreviation: Invest Radiol Subsets: MEDLINE
أسماء مطبوعة: Publication: 1998- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Philadelphia.
مواضيع طبية MeSH: Central Venous Catheters* , Catheterization, Central Venous*/methods, Humans ; Artificial Intelligence ; Radiography ; Radiography, Thoracic/methods
مستخلص: Purpose: To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning.
Materials and Methods: Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks. The CVC/TT information is estimated by introducing a new multitask neural network architecture for jointly performing type/existence classification, course segmentation, and tip detection. Validation data consisted of 589 SCXRs that have been radiologically annotated for inserted TTs/CVCs, including an experts' categorical positioning assessment (reading 1). In-image positions of algorithm-detected TT/CVC tips could be corrected using a validation software tool (reading 2) that finally allowed for localization accuracy quantification. Algorithmic detection of images with misplaced devices (reading 1 as reference standard) was quantified by receiver operating characteristics.
Results: Supine chest radiographs were correctly classified according to inserted TTs/CVCs in 100%/98% of the cases, thereby with high accuracy in also spatially localizing the medical device tips: corrections less than 3 mm in >86% (TTs) and 77% (CVCs) of the cases. Chest radiographs with malpositioned devices were detected with area under the curves of >0.98 (TTs), >0.96 (CVCs with accidental vessel turnover), and >0.93 (also suboptimal CVC insertion length considered). The receiver operating characteristics limitations regarding CVC assessment were mainly caused by limitations of the applied CXR position definitions (region of interest derived from anatomical landmarks), not by algorithmic spatial detection inaccuracies.
Conclusions: The TT and CVC tips were accurately localized in SCXRs by the presented algorithms, but triaging applications for CVC positioning assessment still suffer from the vague definition of optimal CXR positioning. Our algorithm, however, allows for an adjustment of these criteria, theoretically enabling them to meet user-specific or patient subgroups requirements. Besides CVC tip analysis, future work should also include specific course analysis for accidental vessel turnover detection.
Competing Interests: Conflicts of interest and sources of funding: The research was supported by an institutional research grant (see funding below). J. Rueckel and B.O.S received financial compensation for speaker's activities by Siemens Healthineers (lectures at conferences not related to this research project). C.H. and G.B. received financial compensation by Siemens Healthineers (employees).
(Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
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تواريخ الأحداث: Date Created: 20230908 Date Completed: 20240311 Latest Revision: 20240621
رمز التحديث: 20240622
DOI: 10.1097/RLI.0000000000001018
PMID: 37682731
قاعدة البيانات: MEDLINE
الوصف
تدمد:1536-0210
DOI:10.1097/RLI.0000000000001018