Recent advances in imaging techniques and deep learning applications for early diagnosis of knee osteoarthritis: A narrative review

Knee osteoarthritis (KOA), a common degenerative joint disease affected by age, sex, obesity, and trauma, causes significant global disability and socioeconomic burdens, with higher female prevalence. Early KOA (EKOA), defined as Kellgren–Lawrence grades 1–2, presents a critical intervention window for slowing or reversing progression; however, standardized early diagnosis remains challenging. This review synthesizes imaging and deep learning (DL) advances for EKOA diagnosis. A PubMed search (2017–2025) identified 40 studies on EKOA, traditional imaging, emerging technologies, and DL. Traditional imaging shows varied utility: Radiographs detect osteophytes but lack soft tissue sensitivity; ultrasonography assesses synovial effusion/blood flow; and magnetic resonance imaging (MRI) visualizes cartilage microstructure, which can be enhanced by artificial intelligence (AI). Emerging techniques like shear wave elastography quantify cartilage hardness for grading, while positron emission tomography/MRI links metabolic-structural changes in early osteoarthritis. DL models [e.g., visual geometry group-16 (VGG-16) and ResNet50] achieve high accuracy (92% for radiographs and 99.71% for MRI), but they face data dependency, high costs, and interpretability issues due to opaque algorithms. In conclusion, imaging and DL offer promise for EKOA diagnosis; however, standardized definitions, larger datasets, and multi-modality integration are needed to advance clinical use. Addressing these will enable timely intervention and reduce disease burden.