Estimating Suture Needle Size via Selective Detuning of Chipless RFID Tags

The time spent on cleaning and preparation in the operating room accounts for up to 23% of the average procedure time. Reducing these times could decrease cost and optimize care. Preparation time includes surgical instrument counting (SC) to prevent the problem of retained surgical instruments (RSI), which can cost hospitals over $200K per incident. Current SC methods are inadequate for smaller tools like surgical needles (SN). This study explores the use of a high-resolution circular chipless RFID tag for automatically differentiating suture needle sizes. The change in the signal response of the chipless RFID tag, affected by an overlaid SN, is used as a feature set to train a random forest classifier. We demonstrate that the system achieves a classification accuracy of 98.55%, effectively distinguishing between three needle sizes. These findings validate the potential of chipless RFID technology combined with machine learning as a viable tool for automated SC. Future research directions are also discussed.