Development and Statistical Evaluation of a Deep Learning Framework for Real Time Tissue Classification in Robotic Surgery

Abstract

Minimally invasive surgery and robotic assisted procedures are increasingly preferred over traditional open surgeries because they offer faster recovery times and reduce postoperative complications. However, these techniques require precise force application to prevent tissue overstress. The lack of reliable real time tissue recognition limits a surgeon’s ability to apply appropriate force according to tissue type, thereby increasing the risk of injury. This study proposes a framework that applies deep learning and object detection techniques to classify tissues in real time and support safer force modulation in robotic systems. As a proof of concept, the system distinguishes fat, muscle, and skin tissues using GoogLeNet, YOLOv8, and YOLOv10. Skin images were collected from 30 individuals following informed consent, while fat and muscle samples were processed to create a dataset comprising 1,800 augmented images. The GoogLeNet architecture achieved training and test accuracies of 93% and 97.2%, respectively. The YOLOv8 model demonstrated strong performance, achieving a mean average precision (mAP) of 94.7% at IoU = 0.5, with an inference time of 28 ms. YOLOv10 achieved an mAP@0.5 of 96.2% with a latency of 22 ms. The NMS-free architecture of YOLOv10 resulted in a 21% reduction in inference time compared to YOLOv8, along with a 1.5% improvement in accuracy. A statistically significant difference among the evaluated models was confirmed using analysis of variance (ANOVA), with a significance threshold of p < 0.001, indicating that YOLOv10 demonstrated superior performance under the evaluated experimental conditions.

Ähnliche Arbeiten