Real-time anatomical landmark indication system for robot-assisted single-port laparoscopic hysterectomy based on deep learning YOLOv5 network
Objectives:To develop and evaluate artificial intelligence models based on deep learning,and apply them to clinical teaching to help clinicians learn to recognize anatomical landmarks,improve learning interest,and enhance their surgical capabilities.Methods:A deep learning model was devel-oped and trained to recognize the following anatomical landmarks,including the uterus,fallopian tubes,round ligament,ovary,utero-ovarian ligament,bladder,utero-bladder space(bladder separa-tion safe zone),uterosacral ligament,and the edge of the KOH colpotomizer system during the hyster-ectomy.P(precision),mAP(average precision),F1-confidence curve,and R(recall rate)were used to evaluate the accuracy of the model,and the application of clinical group teaching was used to verify whether the model can help clinical teaching.Results:We trained the artificial intelligence model on the library of surgical videos built by Da Vinci robotic system in our hospital.The P value of this mod-el is 94.80％,the mAP value is 99.10％,the F1 score is 96.00％,and the R is 99.00％.After it was applied to clinical teaching,the students in the experimental group had significantly improved their proficiency in identifying gynecological anatomical structures under robotic laparoscopy in 8 as-pects(P＜0.05),especially the uterine-ovarian ligament,bladder,and uterine-bladder space(safety zone for bladder separation),there has been a significant improvement.After training,the theoretical scores of the gynecology specialty of the two groups of clinicians were significantly increased(both P＜0.05),and the scores of the experimental group were better than those of the control group.Con-clusion:Artificial intelligence can be used to identify anatomical structures in the surgical field,help clinicians learn anatomical landmark recognition faster and better,strengthen the surgical ability of cli-nicians,and help reduce the risk of intraoperative adverse events.
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