Multi-class Detection and Tracking of Intracorporeal Suturing Instruments in an FLS Laparoscopic Box Trainer Using Scaled-YOLOv4

Mohaidat, Mohsen; Grantner, Janos L.; Shebrain, Saad; Abdel‐Qader, Ikhlas

doi:10.1007/978-3-031-20713-6_16

Cited by 5 publications

(1 citation statement)

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“…A dataset of suturing tasks was used to train this system, which strikes a balance between cuttingedge architectures. In [20], the authors proposed a skill evaluation system that employs Scaled-YOLOv4 and a centroid tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Multi-Class Object Detection and Tracking for the FLS Pattern Cutting Task

Alkhamaiseh,

Grantner,

Shebrain

et al. 2023

Adv. sci. technol. eng. syst. j.

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The advent of laparoscopic surgery has increased the need to incorporate simulator-based training into traditional training programs to improve resident training and feedback. However, current training methods rely on expert surgeons to evaluate the dexterity of trainees, a time-consuming and subjective process. Through this research, we aim to extend the use of object detection in laparoscopic training by detecting and tracking surgical tools and objects. In this project, we trained YOLOv7 object detection neural networks on Fundamentals of Laparoscopic Surgery pattern-cutting exercise videos using a trainable bag of freebies. Experiments show that YOLOv7 has a mAP score of 95.2, 95.3 precision, 94.1 Recall, and 78 accuracy for bounding boxes on a limited-size training dataset. This research clearly demonstrates the potential of using YOLOv7 as a single-stage real-time object detector in automated tool motion analysis for the assessment of the resident's performance during training.

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Section: Introductionmentioning

confidence: 99%