# Real-Time Surgical Instrument Defect Detection via Non-Destructive Testing

arXiv: https://arxiv.org/abs/2510.14525 · HTML: https://mlsanity.com/paper/2510.14525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Concurrent Segmentation and Localization for Tracking of Surgical Instruments](https://mlsanity.com/paper/1703.10701/) (2017)
- [Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery](https://mlsanity.com/paper/1710.06668/) (2017)
- [Localizing dexterous surgical tools in X-ray for image-based navigation](https://mlsanity.com/paper/1901.06672/) (2019)
- [Real-time image-based instrument classification for laparoscopic surgery](https://mlsanity.com/paper/1808.00178/) (2018)

## Abstract (arXiv)

Defective surgical instruments pose serious risks to sterility, mechanical integrity, and patient safety, increasing the likelihood of surgical complications. However, quality control in surgical instrument manufacturing often relies on manual inspection, which is prone to human error and inconsistency. This study introduces SurgScan, an AI-powered defect detection framework for surgical instruments. Using YOLOv8, SurgScan classifies defects in real-time, ensuring high accuracy and industrial scalability. The model is trained on a high-resolution dataset of 102,876 images, covering 11 instrument types and five major defect categories. Extensive evaluation against state-of-the-art CNN architectures confirms that SurgScan achieves the highest accuracy (99.3%) with real-time inference speeds of 4.2-5.8 ms per image, making it suitable for industrial deployment. Statistical analysis demonstrates that contrast-enhanced preprocessing significantly improves defect detection, addressing key limitations in visual inspection. SurgScan provides a scalable, cost-effective AI solution for automated quality control, reducing reliance on manual inspection while ensuring compliance with ISO 13485 and FDA standards, paving the way for enhanced defect detection in medical manufacturing.