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Using Computer Vision to Identify Objects in an Operating Theatre to Support Safer Working

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Thesis(4.75 MB)

Date

2023

Authors

Stephen, Okeke

Supervisor

Nguyen, Minh
Madanian, Samaneh

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Doctor of Philosophy

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Publisher

Auckland University of Technology

Abstract

The accurate selection and identification of drugs in the operating rooms (OR) is a priority for every healthcare institution. During drug preparation and administration in the operating theatre, the anesthetists need to correctly select medications to administer them. Giving the wrong medication can lead to very serious consequences. However, anesthetists can make error cases, especially when they are tired or distracted. This project aims to use computer vision to reduce the likelihood of error and increase patient safety during medication administration in operating theatres. Computer vision methods driven by artificial intelligence have outperformed humans in many tedious tasks involving object identification and recognition. Therefore, in this work, a computer vision-based framework is proposed to identify and extract critical information from medication container labels used during anesthesia in the operating theatres, which can be processed to confirm that the correct medication has been selected. The framework is built to automatically generate voice feedback or raise concerns in the event of a potential drug error. The proposed framework will form part of a front-end to the existing anesthetics medication preparation systems and increase the safety of the whole anesthetic workflow. A number of different approaches have been proposed; currently, the project focuses on automatically recognizing the labels on drug ampules and vials using artificial intelligence-powered computer vision methodologies without the need for QR codes or barcodes on the medication ampoules or vials. The framework is tested for accuracy and compatibility with the anesthetic drug preparation, administration workflow, and efficiency. Also, the research investigated ways to ensure the ampule-to-patient flow is safe – for example, by producing compatible syringe embeddable labels and examining other technologies for recording data around the site of injection for the anesthetic record. After the proof-of-concept development, the framework was rigorously tested and validated for usability using real-time procedures. The reliability, accuracy, and processing speed of critical healthcare products, especially those used in a setting such as the operating theatres, are of utmost importance. The proposed framework achieved remarkable accuracy in identifying the anesthetic drug samples with a rapid processing speed below the maximum threshold (1 second) set for the project while exceeding the originally estimated accuracy threshold.

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http://hdl.handle.net/10292/16637

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