Application of YOLO-v8 model based on lumbar X-ray in grading diagnosis of lumbar facet joint of osteoarthritis

Purpose Lumbar facet joint of osteoarthritis (LFJOA) can cause intractable low back pain in patients. Early evaluation of the status of LFJOA is very important for subsequent treatment. This paper discusses the automatic segmentation and detection of LFJOA by studying the characteristics of artificial intelligence technology and its potential application in medical image analysis. Methods The ability to detect inflammation has been significantly enhanced in recent years due to deep learning technology, especially models based on object detection. This study collected 987 lateral lumbar X-ray from 987 patients, each of which was manually divided into five lumbar facet joint segments. According to the computed tomography (CT) image of each patient, the classification annotation was carried out based on weishaupt standard. Then, the you only look once (YOLO)-v8 model was used for hierarchical diagnosis. Precision, recall, f1 score, mean average precision (map)50, and map50-95 were used to evaluate the model's performance. Additionally, the research examined how this technology could be applied in clinical settings. Results In detecting facet arthritis, the YOLO-v8 model reached a map50 of 0.694, a map50-95 of 0.286, an F1 score of 0.64, a precision rate of 0.71, and a recall of 0.689. Conclusion YOLO-v8 has diagnostic value in detecting the severity of LFJOA. Future research should the model’s classification potential to enhance its clinical application settings, and help spinal surgeons more effectively diagnose the severity of lumbar facet arthritis, so as to formulate accurate treatment plans.