AI Framework for Detecting Lordosis-Kyphosis-Scoliosis in Salmon X-Rays

Lordosis-Kyphosis-Scoliosis (LKS) is a spinal de-formity that affects salmon, leading to health and economic consequences in aquaculture. Early detection of LKS is critical for mitigating it effects and improving fish welfare. This paper presents an AI-driven system for detecting and classifying LKS in salmon using X-ray imaging and Cobb angle analysis. The proposed system incorporates image preprocessing, vertebra detection using the YOLOv10 model, and Cobb angle calculation to quantify spinal deformities. A dataset of 1,888 X-ray images was utilized to evaluate the performance of the system, achieving high accuracy in vertebra detection and real-time prediction with an average inference time of 3–5 seconds. Our results demonstrate that this system offers an efficient and scalable solution for integrating automated health monitoring in aquaculture environments, ultimately enhancing both productivity and fish welfare.