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Artificial Intelligence Based Real-Time Processing of Sterile Preparations Compounding

Rehman Faridi, Shah Mohammad Hamoodur
http://orcid.org/0000-0002-0291-4725
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596595453534505

Abstract Details

2020, Master of Science, University of Toledo, Engineering (Computer Science).
The objective of this research is to develop a fully functional semi-automated monitoring and verification system to improve the quality standards in compounding sterile preparations (CSP). To avoid the errors made in the CSP process, a material selection graphical user interface (MSGUI) is integrated with a video processing system (VPS) that provide in-process feedback to the pharmacist preparing a medication order (MO) in the work surface of a laminar airflow workbench (LAFW). A hand gesture-based monitoring and verification (HGMV) system is developed on deep learning technology that helps in monitoring as well as verification of the process using different types of hand-gestures. A barcode enabled product verification (BEPV) technique is also developed and integrated with a compounding database which helps in selecting correct products to be used in CSP. The complete model also includes some other important verification and monitoring features such as a video recording process (VRP) that is used to track all the steps performed in completing a MO, image capturing in between the process, and electronic documentation of all the products used in the process as well as important events that occurred while doing CSP. The developed system was tested for different scenarios that a pharmacist can face in CSP, and the final version of the model was found to be of the highest accuracy. The BEPV and HGMV were modified based on the results from the initial phase of testing, and the final version was highly robust and efficient. Mistakes were made deliberately at the testing phase, and the results matched the expected output. The compounding sterile preparations monitoring and verification system (CSPTVS) provides a cost-effective solution that is capable of improving the quality standards in the field of pharmacy by complete monitoring of the process and providing real-time in-process feedback to the pharmacist while reducing wastage of wrongly-selected products.
Vijay Devabhaktuni (Committee Chair)
Jerry Nesamony (Committee Co-Chair)
Ahmad Javaid (Committee Member)
Weiqing Sun (Committee Member)
115 p.
Artificial Intelligence; Computer Engineering; Computer Science; Pharmaceuticals
compounding; Compounding Sterile Preparations; CSP; pharmaceuticals; pharmacy; Artificial Intelligence; AI; Deep Learning; Convolutional Neural Networks; CNN; hand gestures; barcode detection

Recommended Citations

Citations

  • Rehman Faridi, S. M. H. (2020). Artificial Intelligence Based Real-Time Processing of Sterile Preparations Compounding [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596595453534505

    APA Style (7th edition)

  • Rehman Faridi, Shah Mohammad Hamoodur. Artificial Intelligence Based Real-Time Processing of Sterile Preparations Compounding. 2020. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596595453534505.

    MLA Style (8th edition)

  • Rehman Faridi, Shah Mohammad Hamoodur. "Artificial Intelligence Based Real-Time Processing of Sterile Preparations Compounding." Master's thesis, University of Toledo, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596595453534505

    Chicago Manual of Style (17th edition)

toledo1596595453534505
194
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This open access ETD is published by University of Toledo and OhioLINK.