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Real-time automatic detection of gynecological laparoscopic surgical instruments and exploration in surgical skills
Huanyu Wei1,2, Li Deng1, Xueju Wu1
1Department of Obstetrics and Gynecology, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, China.
Background:
Automatic detection of surgical instruments is essential for artificial intelligence surgery. This study aimed to construct a large-scale dataset of gynecological laparoscopic surgical instruments based on real surgical scenarios, achieve high-precision real-time detection of surgical instruments, and explore their potential application in surgical skill evaluation.
Materials And Methods:
This cross-sectional study collected 265 gynecological laparoscopic surgical videos from two medical centers for instrument detection. Videos were divided into training and testing sets in a 4:1 ratio, with 161 348 instrument instances extracted. The instruments were detected using Real-Time Models for Object Detection (RTMDet). The mean average precision, sensitivity, and F1 score served as evaluation metrics. External validation was conducted on an independent dataset from a third medical center. Additionally, we further compared the RTMDet with the state-of-the-art PP-YOLOE model on the same dataset. Furthermore, this study performed real-time tracking of instruments during the vaginal cuff suturing step of laparoscopic hysterectomy and compared the differences in kinematic data between proficient and non-proficient videos.
Results:
The mean average precision, sensitivity, and F1 score for nine types of surgical instruments were 91.75%, 94.29%, and 93.00%, respectively. External validation on the independent dataset demonstrated robust performance. In the comparison with PP-YOLOE, RTMDet demonstrated superior performance in all metrics. In the comparative analysis of kinematic data, the proficient group demonstrated significantly lesser path lengths and inter-quartile range, shorter moving times, and higher movement velocities for instruments used by both hands compared to the non-proficient group.
Conclusions:
This study established a large-scale, real scenario-based database of gynecological laparoscopic instruments. Using the RTMDet model, high-precision real-time detection and tracking of multiple instruments were achieved. Furthermore, this study identified several instrument kinematic metrics that can be used for surgical skill assessment, providing a reference for the objective quantification of the subjective Global Operative Assessment of Laparoscopic Skills.
