# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 9 Conclusions and Future Work  
This study introduced a mixed reality-assisted real-time vial identification system using a lightweight YOLOv4-based deep learning model. The proposed system was designed to address the limitations of traditional barcode-based and manual identification methods by integrating real-time object detection with MR-based spatial annotations. Through a series of architectural enhancements, including ResBlocks, Spatial Pyramid Pooling, and a multiscale detection framework, the model achieved a mean average precision of 98.76 percent, outperforming baseline YOLOv4-Tiny implementations. The system demonstrated robust performance in real-world scenarios, particularly in controlled lighting conditions, where detection accuracy reached 100 percent for several vial classes. The integration of Mixed Reality enabled seamless user interaction, with spatially anchored bounding boxes ensuring that the vial labels remained correctly positioned in 3D space. The results of the user study confirmed that the MR-assisted system significantly improved efficiency, reducing task completion time by 60 percent compared to manual identification and 40 percent compared to barcode-based methods. Furthermore, the error rate was reduced by 57.3 percent, highlighting the potential of this approach in mitigating vial misidentification errors in pharmaceutical and healthcare settings.  
Despite its advantages, the system exhibited challenges in detecting multiple visually similar vials within the same frame, occasionally misclassifying look-alike medications such as Midazolam and Amiodron. Additionally, performance degradation was observed under extreme lighting conditions and during occlusion events, suggesting the need for further model optimization. Some users also reported mild discomfort when using HoloLens 2 for extended periods, underscoring the need for more ergonomic MR devices. A future study will investigate the incorporation of instance segmentation architectures, such as Mask R-CNN or YOLO-SegFormer, to rectify misclassification under multi-vial conditions. These methodologies facilitate pixel-level segmentation, which is essential for distinguishing closely clustered or partially obscured vials. Furthermore, attention-based modules originating from transformer designs may be used to enhance feature differentiation in visually complex environments. Additional efforts will explore the integration of lightweight augmented reality glasses such as Magic Leap 2 and Nreal Light, which offer improved comfort and reduced fatigue to improve long-term usability. Expanding the dataset to include a wider range of vial types and training in various real-world conditions will further refine the robustness of the model. The findings of this study demonstrate the feasibility of MR-assisted deep learning models in medical applications, paving the way for safer and more efficient vial identification systems in high-risk environments.  
Conceptualization, B.U.M. and G.H.; methodology, B.U.M.; software, B.U.M. and V.R.L.; validation, B.U.M., G.H., N.B. and Z.D.A.; formal analysis, B.U.M.; investigation, B.U.M. and G.H.; resources, B.U.M. and G.H.; data curation, B.U.M., G.H. and V.R.L.; writing--original draft preparation, B.U.M.; writing--review and editing, All authors; visualization, B.U.M., V.R.L. and N.B.; supervision, G.H., Z.D.A. and N.B.; project administration, G.H.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.