Wrist arthroscopy is a rapidly expanding surgical discipline, but has a long and challenging learning curve. One of its difficulties is distinguishing the various anatomical structures during the procedure.

Although artificial intelligence has made significant progress in recent decades, its potential as a valuable tool in surgery training is largely untapped.

The objective of this study was to develop an algorithm that could accurately recognize the anatomical bone structures of the wrist during arthroscopy. We prospectively included 20 wrist arthroscopies: 10 in patients and 10 in cadavers. For each surgery, we extracted and labeled images of the various carpal bones. These images were used to create a database for training, validating and testing a structure recognition algorithm. The primary criterion used was a Dice loss detection and categorization score for structures of interest, with a threshold greater than 80%.

The database contained 511 labeled images (4,088 after data augmentation). We developed a Deeplabv3+ classification algorithm with a U-Net architecture. After training and testing our algorithm, we achieved an average Dice loss score of 89% for carpal bone recognition.

This study demonstrated reliable detection of different carpal bones during arthroscopic wrist surgery using artificial intelligence. However, some bones were detected more accurately than others, suggesting that additional algorithm training could further enhance performance. Application in real-life conditions could validate these results and potentially contribute to learning and improvement in arthroscopic wrist surgery.

IV.