Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network
This work addresses the need for automated tool tracking in instructional videos for ophthalmologists and trainees, but it is incremental as it builds on existing YOLO architectures.
This paper tackled the problem of real-time identification of surgical instruments in cataract surgery videos by developing a deep learning model based on YOLOV9, which achieved a superior mAP of 73.74 at IoU 0.5 on a custom dataset.
Instructional cataract surgery videos are crucial for ophthalmologists and trainees to observe surgical details repeatedly. This paper presents a deep learning model for real-time identification of surgical instruments in these videos, using a custom dataset scraped from open-access sources. Inspired by the architecture of YOLOV9, the model employs a Programmable Gradient Information (PGI) mechanism and a novel Generally-Optimized Efficient Layer Aggregation Network (Go-ELAN) to address the information bottleneck problem, enhancing Minimum Average Precision (mAP) at higher Non-Maximum Suppression Intersection over Union (NMS IoU) scores. The Go-ELAN YOLOV9 model, evaluated against YOLO v5, v7, v8, v9 vanilla, Laptool and DETR, achieves a superior mAP of 73.74 at IoU 0.5 on a dataset of 615 images with 10 instrument classes, demonstrating the effectiveness of the proposed model.