In order to guarantee the traceability management of reusable medical devices, an efficient method to track individual medical devices is needed. Global medical device standard code (Unique Device Identifier) ​​regulations are strictly controlled by International Medical Device Regulatory Forum (IMDRF). In line with this, in Korea, in accordance with the Enforcement Rules of the Medical Device Act (Prime Minister Decree No. 1512), medical device standard codes are attached and integrated for each medical device class, starting from Stage 4 from July 1, 2019, and Stage 1 from July 1, 2022. Accordingly, 'Dr.Tracking', as a core technology, provides on-device Direct Part Marking (DPM) technology that meets standard code regulations and stores unique information on medical devices efficiently. Data Matrix is ​​a type of barcode and is a two-dimensional matrix code that can store a large amount of data in a small space. Each medical device is engraved on its surface to identify unique information. Using the ECC200 2D code, the latest version of the Data Matrix standard that adopts the data restoration function, two types of data structures, square and rectangular, are applied depending on the size and shape of the medical device requiring engraving. The square type is applied to general medical devices, but the rectangular type is applied to medical devices for which square type marking is difficult due to their small size or circular structure.

The technology, which can acquire and recognize unique information stored on individual reusable medical devices, enables automatic recording of usage history and tracking of real-time locations, results in maximized traceability efficiency. Accordingly, 'Dr.Tracking' provides a technology combining 2D Data Matrix and AI Computer Vision, which is not only easy to apply to all types of medical devices with high durability and low operating costs, but also to recognize large amounts of data at once. A Machine Vision system is applied to recognize and process the Data Matrix information engraved on a large amount of individual medical devices as images at once. The Machine Vision system basically consists of an optical system that reads images and a system (PC, board, monitor) that stores and processes images transmitted from the optical system. Additionally, the optical system consists of a camera, lens, and lighting. The configuration and environmental settings of the optical system used in this study are as follows.

YOLO, the latest deep learning technology for AI Vision, is the main technology for analyzing the Data Matrix. YOLO uses the images read and stored through machine vision, then identifies actual location of objects called Data Matrix, and finally generates unique code for individual medical devices. It is the most important success key factor that work efficiency in hospitals can be increased by maximizing the data throughput processed in limited time and space. Accordingly, 'Dr.Tracking' employs a such technology combining Data Matrix and AI Computer Vision, which simultaneously recognizes the unique information of large quantities (100 to 200) of reusable medical devices at once. Currently, No other competitors in the market have implemented the technology.

Recycling of reusable medical devices in hospitals generally follows the standard step-by-step process to comply with sterilization guidelines. The recycling work processes largely consists of seven steps: incoming (return), washing, packaging, sterilization, storage, outgoing (dispatch), and usage. ‘Dr.Tracking’ is a software platform that provides complete traceability to automatically create and store data at each stage of the recycling processes. Through the platform, workers at hospitals can check the flow status of all reusable medical devices through instant stored data inquiry anytime, anywhere, and can smoothly manage work at each stage.