# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 6 Conclusions  
This study conducted a comparative evaluation of several YOLO and SSD-based object detection models for the task of digit and acronym recognition on medical device displays. The results demonstrated that YOLOv8l (640) achieved the highest accuracy with an mAP@50 of 0.979, while lighter models such as YOLOv8n (320) obtained faster inference times (129.79 FPS) at the expense of accuracy (0.78). These findings underscore the importance of balancing performance metrics such as accuracy, speed, and model complexity. Rather than declaring a single optimal model, the results support that the model selection should be based on the operational constraints and priorities of specific deployment scenarios.  
Furthermore, the comparison carried out in the classification component between the standalone YOLOv8l (640) and the hybrid approach that combines YOLOv8l (640) with a CNN-based classifier demonstrated that, despite a slight reduction in overall accuracy, certain classes benefited from the addition of the CNN.  
Future work should extend this evaluation to include a more diverse and noisy dataset, incorporating real-world variability such as inconsistent lighting, device display differences, and user-related factors (e.g., angle, blur, or obstruction), and should also include a more comprehensive analysis of the trade-offs between multiple performance indicators. Additionally, research should explore lightweight and efficient models suitable for deployment on mobile or embedded hardware, where inference speed, memory usage, and power consumption are critical constraints.  
**Author Contributions:** Conceptualization, P.F. and P.M.; methodology, P.F. and P.M.; software, P.F.; investigation, P.F. and P.M.; resources, J.L.V.; writing--original draft preparation, P.F.; writing--review and editing, J.L.V., P.L., P.M., and F.R.; visualization, J.L.V., P.L., P.M., and F.R.; supervision, J.L.V. and P.M.; project administration, J.L.V. and P.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Innovation Pact HfPT - Health from Portugal, co-funded by the \"Mobilizing Agendas for Business Innovation\" of the \"Next Generation EU\" program of Component 5 of the Recovery and Resilience Plan (RRP), concerning \"Capitalization and Business Innovation\", under the Regulation of the Incentive System \"Agendas for Business Innovation\". This work was also financed by national funds, through the FCT--Foundation for Science and Technology and FCT/MCTES under the projects UIDB/05549:2 Ai, UIDP/05549:2 Ai, LASI-LA/P/0104/2020, and CEECINST/00039/2021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.