Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.
Acad Radiol
; 30(3): 412-420, 2023 Mar.
Article
em En
| MEDLINE
| ID: mdl-35644754
ABSTRACT
RATIONALE AND OBJECTIVES:
To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool. MATERIALS ANDMETHODS:
A retrospective data set including 2000 de-identified images from intensive care unit patients was split into 1488 for training and 512 for testing. AI was developed to automatically identify the ETT, trachea, and carina using semantically embedded neural networks that combine a declarative knowledge base with deep neural networks. To check the ETT tip placement, a "safe zone" was computed as the region inside the trachea and 3-7 cm above the carina. Two AI outputs were evaluated (1) ETT overlay, (2) ETT misplacement alert messages. Clinically relevant performance metrics were compared against prespecified thresholds of >85% overlay accuracy and positive predictive value (PPV) > 30% and negative predictive value NPV > 95% for alerts to move into clinical validation.RESULTS:
An ETT was present in 285 of 512 test cases. The AI detected 95% (271/285) of ETTs, 233 (86%) of these with accurate tip localization. The system (correctly) did not generate an ETT overlay in 221/227 CXRs where the tube was absent for an overall overlay accuracy of 89% (454/512). The alert messages indicating that either the ETT was misplaced or not detected had a PPV of 83% (265/320) and NPV of 98% (188/192).CONCLUSION:
The chest X-ray AI met prespecified performance thresholds to move into clinical validation.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Intubação Intratraqueal
Limite:
Humans
Idioma:
En
Revista:
Acad Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2023
Tipo de documento:
Article