# Current Applications and Development of Radiomics in Osteoporosis: A Narrative Review
## 4 Radiomics in Osteoporosis Diagnosis
### Radiomics Based on Radiographs in Osteoporosis Diagnosis  
Radiographs have been extensively utilized in clinical practice due to their lower radiation exposure and cost compared to CT images. Standard radiographs offer adequate spatial resolution for analyzing bone structure and geometry, demonstrating potential in osteoporosis diagnosis [62]. The application of radiographs from various anatomical regions, such as the hip, spine, chest, limbs, and mandible, combined with different image analysis techniques, has yielded promising results in osteoporosis detection [63]. However, these techniques differed in complexity, accuracy, and clinical applicability, highlighting the need to choose the appropriate method. Traditional methods for analyzing bone morphology and structure in radiographs primarily rely on manual measurements or specialized software tools. For instance, the canal-calcar ratio, canal flare index, morphological cortical index, and canal bone ratio of the proximal femur could be manually quantified from anteroposterior radiographs. These parameters offered insights into changes in bone density and structure, which were valuable for diagnosing osteoporosis [64]. The reproducibility results indicated that, despite high intra-operator repeatability reflected by correlation coefficients exceeding 0.800, significant inter-operator differences were observed (\(p\) < 0.05), reflecting poor overall reproducibility [64]. Additionally, trabecular bone structure was frequently quantitatively assessed by the trabecular bone score from lumbar spine DXA images, though this process required utilizing specialized software [65]. The above methods have proven useful in diagnosing osteoporosis, but they still have limitations. Manual measurement techniques depended heavily on operator expertise, which could introduce subjective bias and were time consuming, rendering them impractical for large-scale screening. Although specialized software could improve efficiency, it required specific hardware or paid licenses, thus limiting its broader application. Radiomics and AI-based automation technologies have garnered increasing attention, particularly deep learning algorithms, which show potential in automating the diagnosis of osteoporosis [66]. However, similar to CT-based osteoporosis diagnosis, deep learning algorithms based on clinical radiographs still face challenges due to the limited availability of large, high-quality training datasets, which can result in poor generalization and overfitting to impact model performance and reliability [67]. The application of deep transfer learning algorithms, which leverage pre-trained networks for feature extraction, has addressed the challenge of limited labeled data [60]. The imaging features derived through this approach are commonly referred to as deep learning features. Currently, when radiomics features and/or deep learning features serve as inputs to models constructed using the same machine learning algorithms, the performance of osteoporosis diagnosis varies across different radiograph datasets. In a multi-center study involving 1325 patients, it was found that radiomics features extracted from lumbar radiographs outperformed deep learning-based models in diagnosing osteoporosis [47]. In contrast, another study indicated that, despite employing the same multi-layer perceptron algorithm, the model utilizing solely radiomics features as input exhibited inferior performance compared to the model based exclusively on deep learning features [46]. Nevertheless, an increasing number of studies indicated that combining deep learning features, clinical features, and radiomics features generally yielded superior performance than using these features individually, thereby assisting clinicians in making more reliable and accurate diagnoses. Radiomics features extracted from hip radiographs demonstrated great diagnostic accuracy with an AUC of 0.770, which was further enhanced to 0.890 when combining with deep learning features [46].