J Med Imaging (Bellingham)
September 2023
Purpose: To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.
Approach: In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow.
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 And Methods: 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.
A combined computational and experimental study was conducted to examine the effect of extended benzannelation orientation on C-C and C-C cyclization of acyclic quinoxalenediynes. Calculations (mPW1PW91/cc-pVTZ//mPW1PW91/6-31G(d,p)) on terminal and phenylethynyl-substituted 5,6-diethynylquinoxaline and 6,7-diethynylquinoxaline showed C-C Bergman cyclization as the favored thermodynamic reaction pathway, with larger C-C preference for the angular quinoxalenediynes due to gain of a new aromatic sextet. Kinetic studies, as a function of 1,4-cyclohexadiene concentration, revealed retro-Bergman ring opening predominates over hydrogen atom abstraction (k > k) for 6,7-diethynylquinoxaline while 5,6-diethynylquinoxaline undergoes irreversible Bergman cyclization indicative of a large retro-Bergman ring opening barrier (k > k).
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