Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks

Brown, Matthew S.; Wong, Kam W.; Shrestha, Liza; Wahi-Anwar, Muhammad; Daly, Morgan; Foster, George; Abtin, Fereidoun; Ruchalski, Kathleen; Goldin, Jonathan G.; Enzmann, Dieter R.

doi:10.1016/j.acra.2022.04.022

Cited by 14 publications

(19 citation statements)

References 27 publications

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“…The SimpleMind environment has improved the performance of a number of medical imaging applications [ 5 , 6 , 8 ], and we believe that there is strong potential utility for broader research and commercial applications in building trustworthy AI. The open source software allows for knowledge base expansion and agent aggregation by a community of developers.…”

Section: Discussionmentioning

confidence: 99%

“…Figs 8 – 10 show results from the SimpleMind chest x-ray ETT application. For learning within the knowledge base, we used 2000 images collected retrospectively from ICU patients between April 2018 and September 2019 [ 5 ]. All of the DNN nodes are trained with 1488 images, and the application was evaluated with 512 images.…”

Section: Application Examplesmentioning

confidence: 99%

“…The application was evaluated on 512 test CXR images, of which 285 had an ETT present [ 5 ]. The AI ETT detection and segmentation was accurate in 89% of cases (454/512).…”

Section: Application Examplesmentioning

confidence: 99%

“…The AI ETT detection and segmentation was accurate in 89% of cases (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) [ 5 ].…”

Section: Application Examplesmentioning

confidence: 99%

“…We will describe the SimpleMind environment for Cognitive AI and demonstrate its flexible application to medical image segmentation on chest x-ray [5], kidney CT [6,7], and prostate MRI [8].…”

Section: Introductionmentioning

confidence: 99%

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SimpleMind: An open-source software environment that adds thinking to deep neural networks

2023

Self Cite

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Deep neural networks (DNNs) detect patterns in data and have shown versatility and strong performance in many computer vision applications. However, DNNs alone are susceptible to obvious mistakes that violate simple, common sense concepts and are limited in their ability to use explicit knowledge to guide their search and decision making. While overall DNN performance metrics may be good, these obvious errors, coupled with a lack of explainability, have prevented widespread adoption for crucial tasks such as medical image analysis. The purpose of this paper is to introduce SimpleMind, an open-source software environment for Cognitive AI focused on medical image understanding. It allows creation of a knowledge base that describes expected characteristics and relationships between image objects in an intuitive human-readable form. The knowledge base can then be applied to an input image to recognize and understand its content. SimpleMind brings thinking to DNNs by: (1) providing methods for reasoning with the knowledge base about image content, such as spatial inferencing and conditional reasoning to check DNN outputs; (2) applying process knowledge, in the form of general-purpose software agents, that are dynamically chained together to accomplish image preprocessing, DNN prediction, and result post-processing, and (3) performing automatic co-optimization of all knowledge base parameters to adapt agents to specific problems. SimpleMind enables reasoning on multiple detected objects to ensure consistency, providing cross-checking between DNN outputs. This machine reasoning improves the reliability and trustworthiness of DNNs through an interpretable model and explainable decisions. Proof-of-principle example applications are provided that demonstrate how SimpleMind supports and improves deep neural networks by embedding them within a Cognitive AI environment.

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Section: Discussionmentioning

confidence: 99%

Section: Application Examplesmentioning

confidence: 99%

“…The application was evaluated on 512 test CXR images, of which 285 had an ETT present [ 5 ]. The AI ETT detection and segmentation was accurate in 89% of cases (454/512).…”

Section: Application Examplesmentioning

confidence: 99%

Section: Application Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SimpleMind: An open-source software environment that adds thinking to deep neural networks

2023

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Deep learning for tubes and lines detection in critical illness: Generalizability and comparison with residents

Wongveerasin,

Tongdee,

Saiviroonporn

2024

European Journal of Radiology Open

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The Promise of Artificial Intelligence in Neuroanesthesia: An Update

Liao,

Mathur,

Joshi

et al. 2024

J Neuroanaesth Crit Care

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Artificial intelligence (AI) is poised to transform health care across medical specialties. Although the application of AI to neuroanesthesiology is just emerging, it will undoubtedly affect neuroanesthesiologists in foreseeable and unforeseeable ways, with potential roles in preoperative patient assessment, airway assessment, predicting intraoperative complications, and monitoring and interpreting vital signs. It will advance the diagnosis and treatment of neurological diseases due to improved risk identification, data integration, early diagnosis, image analysis, and pharmacological and surgical robotic assistance. Beyond direct medical care, AI could also automate many routine administrative tasks in health care, assist with teaching and training, and profoundly impact neuroscience research. This article introduces AI and its various approaches from a neuroanesthesiology perspective. A basic understanding of the computational underpinnings, advantages, limitations, and ethical implications is necessary for using AI tools in clinical practice and research. The update summarizes recent reports of AI applications relevant to neuroanesthesiology. Providing a holistic view of AI applications, this review shows how AI could usher in a new era in the specialty, significantly improving patient care and advancing neuroanesthesiology research.

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Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks

Cited by 14 publications

References 27 publications

SimpleMind: An open-source software environment that adds thinking to deep neural networks

SimpleMind: An open-source software environment that adds thinking to deep neural networks

Deep learning for tubes and lines detection in critical illness: Generalizability and comparison with residents

The Promise of Artificial Intelligence in Neuroanesthesia: An Update

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