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Precisely detecting puncture times has long posed a challenge in medical education. This challenge is attributable not only to the subjective nature of human evaluation but also to the insufficiency of effective detection techniques, resulting in many medical students lacking full proficiency in injection skills upon entering clinical practice. To address this issue, we propose a novel detection method that enables automatic detection of puncture times during injection without needing wearable devices. In this study, we utilized a hardware system and the YOLOv7 algorithm to detect critical features of injection motion, including puncture time and injection depth parameters. We constructed a sample of 126 medical injection training videos of medical students, and skilled observers were employed to determine accurate puncture times. Our experimental results demonstrated that the mean puncture time of medical students was 2.264 s and the mean identification error was 0.330 s. Moreover, we confirmed that there was no significant difference (p = 0.25 with a significance level of α = 0.05) between the predicted value of the system and the ground truth, which provides a basis for the validity and reliability of the system. These results show our system's ability to automatically detect puncture times and provide a novel approach for training healthcare professionals. At the same time, it provides a key technology for the future development of injection skill assessment systems. [ABSTRACT FROM AUTHOR]

Precisely detecting puncture times has long posed a challenge in medical education. This challenge is attributable not only to the subjective nature of human evaluation but also to the insufficiency of effective detection techniques, resulting in many medical students lacking full proficiency in injection skills upon entering clinical practice. To address this issue, we propose a novel detection method that enables automatic detection of puncture times during injection without needing wearable devices. In this study, we utilized a hardware system and the YOLOv7 algorithm to detect critical features of injection motion, including puncture time and injection depth parameters. We constructed a sample of 126 medical injection training videos of medical students, and skilled observers were employed to determine accurate puncture times. Our experimental results demonstrated that the mean puncture time of medical students was 2.264 s and the mean identification error was 0.330 s. Moreover, we confirmed that there was no significant difference (p = 0.25 with a significance level of α = 0.05) between the predicted value of the system and the ground truth, which provides a basis for the validity and reliability of the system. These results show our system’s ability to automatically detect puncture times and provide a novel approach for training healthcare professionals. At the same time, it provides a key technology for the future development of injection skill assessment systems.