Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
The accurate identification of look-alike medical vials is essential for patient safety, particularly when similar vials contain different substances, volumes, or concentrations. Traditional methods, such as manual selection or barcode-based identification, are prone to human error or face reliabili...
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MDPI AG
2025-05-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/5/223 |
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| author | Bahar Uddin Mahmud Guanyue Hong Virinchi Ravindrakumar Lalwani Nicholas Brown Zachary D. Asher |
| author_facet | Bahar Uddin Mahmud Guanyue Hong Virinchi Ravindrakumar Lalwani Nicholas Brown Zachary D. Asher |
| author_sort | Bahar Uddin Mahmud |
| collection | DOAJ |
| description | The accurate identification of look-alike medical vials is essential for patient safety, particularly when similar vials contain different substances, volumes, or concentrations. Traditional methods, such as manual selection or barcode-based identification, are prone to human error or face reliability issues under varying lighting conditions. This study addresses these challenges by introducing a real-time deep learning-based vial identification system, leveraging a Lightweight YOLOv4 model optimized for edge devices. The system is integrated into a Mixed Reality (MR) environment, enabling the real-time detection and annotation of vials with immediate operator feedback. Compared to standard barcode-based methods and the baseline YOLOv4-Tiny model, the proposed approach improves identification accuracy while maintaining low computational overhead. The experimental evaluations demonstrate a mean average precision (mAP) of 98.76 percent, with an inference speed of 68 milliseconds per frame on HoloLens 2, achieving real-time performance. The results highlight the model’s robustness in diverse lighting conditions and its ability to mitigate misclassifications of visually similar vials. By combining deep learning with MR, this system offers a more reliable and efficient alternative for pharmaceutical and medical applications, paving the way for AI-driven MR-assisted workflows in critical healthcare environments. |
| format | Article |
| id | doaj-art-02f1bc2f851a41d69c47d6a7fb21823f |
| institution | OA Journals |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-02f1bc2f851a41d69c47d6a7fb21823f2025-08-20T02:33:50ZengMDPI AGFuture Internet1999-59032025-05-0117522310.3390/fi17050223Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep LearningBahar Uddin Mahmud0Guanyue Hong1Virinchi Ravindrakumar Lalwani2Nicholas Brown3Zachary D. Asher4Department of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USADepartment of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USADepartment of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USAEnergy Efficient and Autonomous Vehicle (EEAV) Lab, Western Michigan University, Kalamazoo, MI 49008, USAEnergy Efficient and Autonomous Vehicle (EEAV) Lab, Western Michigan University, Kalamazoo, MI 49008, USAThe accurate identification of look-alike medical vials is essential for patient safety, particularly when similar vials contain different substances, volumes, or concentrations. Traditional methods, such as manual selection or barcode-based identification, are prone to human error or face reliability issues under varying lighting conditions. This study addresses these challenges by introducing a real-time deep learning-based vial identification system, leveraging a Lightweight YOLOv4 model optimized for edge devices. The system is integrated into a Mixed Reality (MR) environment, enabling the real-time detection and annotation of vials with immediate operator feedback. Compared to standard barcode-based methods and the baseline YOLOv4-Tiny model, the proposed approach improves identification accuracy while maintaining low computational overhead. The experimental evaluations demonstrate a mean average precision (mAP) of 98.76 percent, with an inference speed of 68 milliseconds per frame on HoloLens 2, achieving real-time performance. The results highlight the model’s robustness in diverse lighting conditions and its ability to mitigate misclassifications of visually similar vials. By combining deep learning with MR, this system offers a more reliable and efficient alternative for pharmaceutical and medical applications, paving the way for AI-driven MR-assisted workflows in critical healthcare environments.https://www.mdpi.com/1999-5903/17/5/223look-alike vialreal time detectionAIdeep learningAI in healthcareobject detection |
| spellingShingle | Bahar Uddin Mahmud Guanyue Hong Virinchi Ravindrakumar Lalwani Nicholas Brown Zachary D. Asher Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning Future Internet look-alike vial real time detection AI deep learning AI in healthcare object detection |
| title | Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning |
| title_full | Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning |
| title_fullStr | Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning |
| title_full_unstemmed | Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning |
| title_short | Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning |
| title_sort | real time identification of look alike medical vials using mixed reality enabled deep learning |
| topic | look-alike vial real time detection AI deep learning AI in healthcare object detection |
| url | https://www.mdpi.com/1999-5903/17/5/223 |
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