# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 1 Introduction  
Medication errors persist as the predominant category of errors in emergency departments. Children face a higher risk of prescription errors due to the lack of standardized doses, necessitating individual calculations depending on their weight. The rates of drug errors in emergency departments (ED) vary from 4% to 14%, escalating to 39% in pediatric cases [1]. A recent investigation into prehospital pediatric medication dosing errors revealed the ongoing prevalence of errors despite the availability of state-wide dosing resources such as the MI-MEDIC card, with a notable proportion of these errors arising from medications necessitating intricate calculations (e.g., dextrose, glucagon, intranasal fentanyl, midazolam, and epinephrine) [2]. Globally, the World Health Organization (WHO) estimates that medication errors cost approximately $42 billion annually, contributing to severe patient harm and increased healthcare expenditures. These findings underscore the need for more resilient systems that guarantee precise dosage calculations and facilitate theproper identification of vials in real time, particularly when vials are visually similar. The identification of medical vials is an essential procedure in healthcare settings. Due to the range in vial sizes, labeling discrepancies, and the rapid speed of medical settings, there is a significant danger of human error in vial identification. This is especially crucial, as errors in vial identification could have significant health consequences. In healthcare settings, accurate and efficient vial identification is crucial to prevent medication errors. Although barcode scanning is widely used, several other identification methods are also employed to enhance patient safety. These include color-coding, manual verification, and Radio Frequency Identification (RFID)-based solutions. However, each of these approaches presents challenges in terms of human error, efficiency, and real-time usability, particularly in high-pressure environments such as emergency departments and operating rooms. Color-coding, for example, is designed to facilitate the quick identification of medications by assigning different colors to different drug classes. However, its effectiveness is limited by the small number of distinctly recognizable colors, making it inadequate to differentiate a wide range of medications [3]. Furthermore, reliance solely on color-coding can lead to misinterpretation if healthcare providers bypass reading the label, assuming that the color alone is sufficient for identification [4]. The lack of a standardized color-coding system across different institutions further exacerbates this confusion, particularly for healthcare workers operating in multiple facilities [5]. Manual verification, which involves healthcare professionals cross-checking medication labels before administration, remains fundamental but is highly prone to human error due to fatigue, distractions, and high workloads [6]. Moreover, inconsistencies in diligence and experience among staff can lead to unreliable verification processes. RFID-based solutions, which use electronic tags attached to medication vials for automatic identification and tracking, offer improved inventory management and reduce dependency on manual checks. However, challenges such as the high implementation costs, interoperability issues, and security concerns, including the unauthorized interception of RFID data, limit their widespread adoption in healthcare settings [7]. These limitations highlight the necessity for more advanced and reliable vial identification systems that enhance patient safety and operational efficiency. In this context, integrating deep learning-based object detection with Mixed Reality (MR) presents a promising approach, offering real-time, automated, and highly accurate vial recognition. Recent advances in Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) have significantly transformed medical applications, particularly in education, training, and clinical procedures.