# Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
## 4 Discussion
The same radiologic findings can be expressed in different words in radiology reports, and many radiologic findings remain unstructured in medical databases. A trained person can perform the information extraction task, but manual extraction is labor-intensive, costly, and time-consuming. An NLP system can be used to select useful information from unstructured text in electronic medical records, reducing the burden of manual extraction [13]. Numerous NLP systems now exhibit similar accuracy to humans and have already been used for radiologic reports. [PERSON] et al. compared an NLP system with specialist review for chest reports [24]. The chest reports of patients with a positive culture and suspected to have tuberculosis were identified. The focus was on identifying six keywords in the chest reports, and a consensus of 89-92% was gained. [PERSON] et al., developed an NLP system for disease chronicity, certainty, presence, and examination technical quality [25]. The NLP system showed high sensitivity (86-98%) and high specificity (89-93%), but did not exhibit such high results for chronicity (60% and 99%). [PERSON] et al. compared different preprocessing and featurization of the NLP pipeline for classifying radiologic findings associated with LBP from X-ray and MRI radiologic reports. In that study, if the NLP pipeline was developed in the same system (e.g., healthcare institution, EMR system), N-grams was the preferred NLP method. If NLP pipelines were developed in multiple systems, document embeddings were considered the best NLP method [17]. [PERSON] et al., investigated the relationship between the radiologic findings of degenerative spinal stenosis on lumbar MRIs (LMRI) and patient characteristics (e.g., age, sex) using rule-based NLP systems. The NLP system identified the patterns of lumbar spine degeneration [19]. [PERSON] et al. investigated lumbar spine MRIs (LMIR) to identify a performance metric for measuring global severity of lumbar degenerative disease (LSDD) [20]. [PERSON] et al. developed a rule-based NLP system to identify the patients with Type 1 Modic endplate changes from lumbar MRI reports [18]. [PERSON] et al. developed deep learning neural network modes to identify features from MRI digital imaging and communications in medicine (DICOM) datasets to produce automatic MRI reports [26]. [PERSON] et al. developed BERT-based NLP system to generate annotations for radiographic images from lumbar X-ray reports [21]. In many previous studies, there were no NLP systems for classification or generation annotations from CT radiologic reports. Some previous studies used clinical notes or electronic health records to assist in clinical diagnosis or treatment decisions for LBP patients. [PERSON] et al. developed a convolutional neural network model for classifying acute LBP episodes from free-text clinical notes [27]. [PERSON] et al. developed a support vector machine model for identifying the axial spondylarthritis (SpA) concepts from electro medical records [28]. [PERSON], [PERSON] et al. developed an NLP system for identifying the patients with axial SpA from clinical charts [29]. [PERSON] et al. developed the NLP algorithms to classify the axial SpA patients from the free text data of the electronic health records [30]. Particularly, the NLP system was applied to identify the electronic medical record or operation notes during surgery or post-operation. [PERSON] et al. developed the NLP system for identifying incidental durotomies from intra-operative electronic health records [31]. [PERSON] et al. developed the NLP system for identifying incidental durotomies from the free-text operation notes [32]. Additionally, a few previous studies identified the complications of spinal surgery from operative notes or electronic medical records. [PERSON] et al. developed the machine learning algorithms for the prediction of intra-operative vascular injury (VI) and the NLP systems for identifying VI from free-text operative notes [33]. [PERSON] et al.