# Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
## 5 Conclusions  
A rule-based NLP system was developed to identify 23 findings associated with LBP from X-ray, CT, and MRI radiology reports sampled from CHA University Bundang CHA \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Topic (Findings)** & **Source** & **Year** \\ \hline \multicolumn{4}{c}{Datasets from radiological image reports} \\ \hline [PERSON] et al. [16] & Radiologic findings & Lumbar MRI reports & 2018 \\  & associated with LBP. & and X-ray reports & 2022 \\ [PERSON] et al. [17] & Radiologic findings & Lumbar MRI reports & 2022 \\  & associated with LBP. & and X-ray reports & 2022 \\  & Relationship between & & \\  & radiologic findings of & & \\ [PERSON] et al. [19] & degenerative spinal & & \\  & stenosis on lumbar MRI & Lumbar MRI reports & 2021 \\  & (LMRI) and patient & & \\  & characteristics. & & \\  & Performance metric for & & \\ [PERSON] et al. [20] & measuring global severity & & \\  & of lumbar degenerative & & \\  & disease (LSDD). & & \\ [PERSON] et al. [18] & Patients with Type 1 Modic & & \\  & endplate changes. & & \\ [PERSON] et al. [26] & Producing automatic MRI & & \\  & reports. & & \\ [PERSON] et al. [21] & Generating annotations for & & \\  & radiographic images. & & \\ \hline \multicolumn{4}{c}{Datasets from free-text type reports} \\ \hline [PERSON] et al. [27] & Classification of acute LBP & Free-text clinical & 2020 \\  & episode. & notes & 2020 \\ [PERSON] et al. [28] & Identifying the axial SpA & Electronic medical & 2017 \\  & concepts. & records & \\ [PERSON] et al. [29] & Identifying the axial SpA. & Clinical chart & \\  & database & & \\ [PERSON] et al. [30] & Classification of the axial & Electronic health & 2020 \\  & SpA patients. & records & \\ [PERSON] et al. [31] & Identifying incidental & Intra-operative & \\  & durotomy. & electronic health & 2020 \\  & records & & \\ [PERSON] et al. [32] & Identifying incidental & Free-text operation & \\  & durotomy. & notes & \\  & Prediction of & & \\  & intra-operative vascular & Free-text operation & \\  & injury (VI) and identifying & notes & 2021 \\  & VI. & & \\  & Identifying the & & \\  & post-operative wound & & \\ [PERSON] et al. [34] & infection that requires & & \\  & reoperation after lumbar & & \\  & discectomy. & & \\  & Predicting 90-day & & \\ [PERSON] et al. [35] & unplanned readmission of & Free-text notes during & \\  & the lumbar spine fusion & hospitalization period & \\  & patients. & & \\ [PERSON] et al. [36] & Venous thromboembolism. & Electronic medical & \\  & records & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification of previous studies to develop NLP systems regarding LBP pain, spinal disease, and complications of spinal surgery. Medical Center. This rule-based NLP system presented good performance. In particular, the potentially clinically important seven radiologic findings exhibited high F1 scores. Through research, an NLP system was developed to identify and extract the necessary terms from the free-text form clinical data used in medical institutions. Through this study, an NLP system for extracting words associated with LBP from not only X-ray and MRI reports, but also CT reports, was developed. The utilization of this NLP system will be expanded to extract data needed for medical research or data that becomes a marker for diagnosis or treatment from free-text form clinical data of the medical institution. This NLP system has potential for use in clinical diagnosis, treatment decisions, and large-scale research. In future studies, the criteria of radiologic findings will be subdivided according to a lesion location. Additionally, results of each patient's complete blood count (CBC) test and blood chemistry test should be used for the prediction of chronic LBP persistency, along with the radiologic findings from the rule-based NLP system. Moreover, the correlation between the degree of pain and the radiologic findings from the NLP system will be investigated. Conceptualization and methodology; H.-W.H. and I.H., writing; Y.K., S.B.K., G.S.