An artificial intelligence-enabled consumables tracking system for medical laboratories
The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing g...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-11-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2023-0208 |
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| _version_ | 1846162280409989120 |
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| author | Sritart Hiranya Tosranon Prasong Taertulakarn Somchat |
| author_facet | Sritart Hiranya Tosranon Prasong Taertulakarn Somchat |
| author_sort | Sritart Hiranya |
| collection | DOAJ |
| description | The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories. |
| format | Article |
| id | doaj-art-f3f49a52a0b340e8adf18414ee26054b |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-f3f49a52a0b340e8adf18414ee26054b2024-11-20T15:34:54ZengDe GruyterJournal of Intelligent Systems2191-026X2024-11-013316799110.1515/jisys-2023-0208An artificial intelligence-enabled consumables tracking system for medical laboratoriesSritart Hiranya0Tosranon Prasong1Taertulakarn Somchat2Department of Medical Technology, Faculty of Allied Health Sciences, Thammasat University, Pathum Thani, 12120, ThailandDepartment of Industrial Physics and Medical Instrumentation, Faculty of Applied Sciences, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, ThailandDepartment of Medical Technology, Faculty of Allied Health Sciences, Thammasat University, Pathum Thani, 12120, ThailandThe medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories.https://doi.org/10.1515/jisys-2023-0208artificial intelligenceobject detectiondeep learningconsumables tracking systemmedical laboratories |
| spellingShingle | Sritart Hiranya Tosranon Prasong Taertulakarn Somchat An artificial intelligence-enabled consumables tracking system for medical laboratories Journal of Intelligent Systems artificial intelligence object detection deep learning consumables tracking system medical laboratories |
| title | An artificial intelligence-enabled consumables tracking system for medical laboratories |
| title_full | An artificial intelligence-enabled consumables tracking system for medical laboratories |
| title_fullStr | An artificial intelligence-enabled consumables tracking system for medical laboratories |
| title_full_unstemmed | An artificial intelligence-enabled consumables tracking system for medical laboratories |
| title_short | An artificial intelligence-enabled consumables tracking system for medical laboratories |
| title_sort | artificial intelligence enabled consumables tracking system for medical laboratories |
| topic | artificial intelligence object detection deep learning consumables tracking system medical laboratories |
| url | https://doi.org/10.1515/jisys-2023-0208 |
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