Automated Quality Control of Orthopedic Screws, Plates, and Rods Using Computer Vision and Deep Learning to Improve Manufacturing Accuracy

Author(s)	Sayed Rafi Basheer
Country	United States
Abstract	Orthopedic screws, plates, and rods require high-dimensional precision and flawless surface quality to ensure structural integrity and patient safety. Traditional inspection methods rely heavily on manual measurement and visual evaluation, which are time-consuming, inconsistent, and prone to human error. This paper proposes an automated, computer vision-driven quality control system capable of detecting screw pitch deviations, thread geometry deformation, bending angle inconsistencies, and machining inaccuracies using deep learning techniques. A hybrid CNN-Transformer architecture is developed to extract geometric features and achieve sub-millimeter measurement precision. Experimental validation demonstrates a defect classification accuracy of over 95 percent and a dimensional prediction error of less than 0.05 mm. The proposed system reduces inspection time by over 80 percent and offers a scalable, objective, and regulatorily compliant approach for orthopedic device manufacturers.
Keywords	Orthopedic implants; Quality control; Computer vision; Deep learning; Manufacturing inspection; Screw pitch measurement; Medical device manufacturing.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-04
DOI	https://doi.org/10.71097/IJSAT.v16.i4.10085
Short DOI	https://doi.org/hbkrgr

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International Journal on Science and Technology