Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images

Intervertebral disc degeneration was first described by Dexler in 1896 [1]. And now, it is a worldwide health problem related to enormous medical and social costs [2]. This degenerative progression occurs most commonly in the cervical and lumbar intervertebral discs. And in clinical practice, magnetic resonance imaging (MRI) is the best noninvasive assessment for investigating degenerative discs. However, there are several pitfalls in MR imaging. Some benign lesions and normal variations could be confused with more severe pathologies in clinics. Intervertebral disc prolapse sometimes mimics infective spondylitis or vertebral osteophytes. A sequestrated disc fragment could easily be mistaken for neurogenic tumors, epidural synovial cysts, epidural hematomas, and conjoined nerve roots [3].

In the last decade, there has been a massive increase in the use of artificial intelligence (AI) and machine learning (ML) technologies for auxiliary diagnosis clinically. It has excellent image recognition ability for the intervertebral disc. And it can improve diagnostic accuracy, mark critical information and reduce human error.

Previous reports show that deep learning approaches can achieve good performance in identifying, diagnosing, and grading lumbar degenerative diseases. The accuracy of some classification methods is comparable to that of radiologists [4‐7]. However, most of these studies only use limited data from a single center. Thus, the robustness and generalization ability of the algorithms could be unreliable. In addition, many of those still require manual segmentations as input, which is expensive, time-consuming, and may also cause subjective bias.

In this multicenter study, we proposed a deep learning model for detecting degenerative discs in both sagittal and axial views of T2-weighted MRI. As an exploration of the application of deep learning for detecting lumbar degenerative disease, this work also assesses the robustness and generalization ability in detecting cervical degenerative disease on an independent dataset via a transfer learning approach.

The aging of society has resulted in a significant increase in spinal images over the past decades, for both the growing burden of spinal disease and the more popularized application of MR. Artificial intelligence (AI) and machine learning (ML) technologies are playing a more critical role in the diagnosis of spinal disorders [12]. Researchers and engineers are working together to develop AI-assisted diagnostic systems to improve the accuracy of diagnosis and reduce the disease burden on society. In recent years, machine learning techniques have been applied in the diagnosis of spinal disorders, such as spinal degenerative disease, trauma, oncology, and deformity. Recent literature also shows the potential of deep learning-based approaches for reliable quantifications of the vertebrae and discs and for facilitating decision-making in the treatment of lumbar disc herniations. However, most of the ML studies are based on limited data in a single center. It is essential to develop optimized algorithms that allow AI programs have the ability to analyze all kinds of images precisely from different medical centers with various scanning parameters. Furthermore, it is also crucial for an ideal AI system to give both qualitative and quantitative descriptions of the lesions. For example, in the case of intervertebral disc prolapse, the AI system should not only tell surgeons the level of the prolapsed disc, but also exhibit the borderline of the lesion and its relationship with other critical organs and tissues.

This study included a wide range of patients aged 12–86 years to add a relatively wide variety of disc morphology and hydration to the Deep Learning database. And it provided more reasonable results. The major findings of our study reveal that the deep learning algorithm has achieved good performance and is highly consistent with the radiologist’s expert reading for detecting lumbar degenerative disease. Several previous researches have reported automated detection of intervertebral disc degeneration using deep learning approaches [13]. However, these studies only use limited data from a single center to validate the performance. As an important step for evaluating a deep learning-based model, external validation is necessary for assessing the model’s robustness and generalization, which could avoid the overestimation of model performance caused by overfitting [14]. Various studies have demonstrated that the diagnostic performance of deep learning models can vary across different datasets [15, 16]. Our proposed model showed high detective accuracy on both sagittal and axial views of T2-weighted MRI in the internal validation dataset. The model also had good robustness, achieving favorable performance in two external validation datasets.

Quantitative analysis provides valuable information and can help clinicians make better decisions, and some deep learning-based lumbar disc degeneration-related quantitative models have been developed in recent years [2, 17, 18]. However, the reliability and validity of quantitative segmentation and measurement of disease regions still need further validation in clinical practice. Moreover, the diseased area of the degenerative cervical spine is commonly irregular, with unclear boundaries in the MRI image, which makes the accurate annotation of the disease region difficult. Meanwhile, the quality of MR images is mainly dependent on the scanning parameters, which are usually changed across different institutions or even varied in each MR scanner in the same center [19]. Therefore, the subjective bias of assessment and segmentation of lesion areas in the MR images seems inevitable, even between experienced radiologists [20]. This, in turn will have a substantial impact on the training and validation of the segmentation neural networks. Therefore, in this case, we choose to use detection networks to solve this problem rather than the segmentation networks such as U-Net.

An important distinction of our work from previous studies is the use of transfer learning to detect cervical disc herniation. Cervical and lumbar disc herniations are both degenerative spinal diseases in their pathophysiological mechanism, and the degenerative discs in the cervical and lumbar regions show similar characteristics in MR imaging. For these reasons, our research further explored the possibility of transferring the learning technique generated from the lumbar dataset to evaluate cervical disks. Although it was only a preliminary study, the results were quite inspiring. As a highly effective technique, transfer learning is suitable for dealing with medical images, particularly when faced with limited data [21, 22]. By using the transfer learning technique, Kermany et al. [23] built a generalized platform that could both classify diabetic macular edema and age-related macular degeneration on optical coherence tomography images and diagnose pediatric pneumonia on chest X-ray images. In this study, we also investigated the effectiveness of transfer learning in detecting cervical degenerative disease on limited T2-weighted MR images from 200 patients. We found that our model retained high detective accuracy, thereby illustrating the generalization of our proposed deep learning algorithm and the power of transfer learning to achieve good performance even with a small dataset.

There are several limitations of our study. First, although a relatively larger cohort from several institutions was collected for model development and validation, we are still aware that the sample size is not big enough for a deep learning algorithm that contains millions of parameters. More patients from different hospitals should be collected to improve the accuracy of lumbar and cervical degenerative disease detection. Second, our study focused on the analysis of T2-weight MR images, which were most commonly used to investigate degenerative discs. However, other modalities such as radiographs, CT, and lumbar discography could also infer anatomic changes and help to exclude other diagnoses [24]. The fusion of multi-modality images such as CT and MR could be a promising area of future investigation. Third, although our model had achieved high accuracy in detecting degenerative discs, there were some technical difficulties in the accurate identification of the boundary of a herniated disc, especially the floor of the lesion in MRI. Further quantitative analysis, including characterization of different degeneration grades, would lead to more critical in clinical practice, which would also be our future work.

The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.