# Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
## 3 Results
### NLP System Performances  
To evaluate the best performances of the NLP system, the NLP model was trained and datasets tested by calculation of the recall, precision, accuracy, and the F1 score. The raw data of the NLP system is available at [[https://github.com/YJK96/Low-Back-Pain-NLP.git](https://github.com/YJK96/Low-Back-Pain-NLP.git)]([https://github.com/YJK96/Low-Back-Pain-NLP.git](https://github.com/YJK96/Low-Back-Pain-NLP.git)), which was accessed on 28 November 2022. In the test datasets (_n_ = 120) of the rule-based NLP model, all 23 radiologic findings had scores of more than 0.9 for recall, precision, accuracy, and the F1 score. These four scores were 1.0 for 10 radiologic findings--listhesis, annular fissure, disc bulge, disc extrusion, disc protrusion, endplate edema or type 1 Modic change, lateral recess stenosis, Schnorl's node, osteophyte, and any stenosis. The lowest F1 score was 0.9802 for facet hypertrophy. The highest recall score was 1.0 for 14 radiologic findings--listhesis, fracture, annular fissure, disc bulge, disc extrusion, disc herniation, disc protrusion, endplate edema or type 1 Modic, facet hypertrophy, lateral recess stenosis, spondylosis, Schnorl's node, osteophyte and any stenosis. The lowest recall score was 0.9840 for central stenosis. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**Training (N = 720)**} & \multicolumn{1}{c}{**Testing (N = 180)**} & \multicolumn{1}{c}{_p_-Value**} \\ \hline Mean age (SD) & 62.5 (15.9) & 61.3 (15.4) & 0.402 \\ Sex (_n_) & & & 0.066 \\ Male & 291 (40.4) & 67 (37.2) & \\ Female & 429 (59.6) & 113 (62.8) & \\ Listhesis (\%) & 259 (36.0) & 60 (33.3) & 0.508 \\ scoliosis (\%) & 147 (20.4) & 34 (18.9) & 0.647 \\ Fracture (\%) & 130 (18.1) & 37 (20.6) & 0.440 \\ Spondylosis (\%) & 286 (39.7) & 77 (42.8) & 0.455 \\ Annular fissure (\%) & 58 (8.1) & 10 (5.6) & 0.256 \\ Disc bulge (\%) & 405 (56.3) & 97 (53.9) & 0.568 \\ Disc degeneration (\%) & 17 (2.4) & 2 (1.1) & 0.297 \\ Disc extrusion (\%) & 82 (11.4) & 25 (13.9) & 0.354 \\ Disc herniation (\%) & 15 (2.1) & 0 (0.0) & 0.051 \\ Disc protrusion (\%) & 152 (21.1) & 43 (23.9) & 0.418 \\ Endplate edema or type 1 & 22 (3.1) & 10 (5.6) & 0.105 \\ Modic change (\%) & & & \\ Facet hypertrophy (\%) & 19 (2.6) & 0 (0.0) & 0.028 \\ Central stenosis (\%) & 215 (29.9) & 55 (30.6) & 0.856 \\ Foraminal stenosis (\%) & 218 (30.3) & 51 (28.3) & 0.610 \\ Nerve root & 54 (7.5) & 17 (9.4) & 0.387 \\ displaced/compressed (\%) & & & \\ Lateral recess stenosis (\%) & 18 (2.5) & 10 (5.6) & 0.035 \\ Spondylosis (\%) & 18 (2.5) & 5 (2.8) & 0.833 \\ Schnorl’s node (\%) & 46 (6.4) & 17 (9.4) & 0.151 \\ Osteophyte (\%) & 42 (5.8) & 11 (6.1) & 0.887 \\ Disc space narrowing (\%) & 181 (25.1) & 51 (28.3) & 0.381 \\ Any stenosis (\%) & 29 (4.0) & 1 (0.6) & 0.020 \\ Disc sequestration (\%) & 10 (1.4) & 8 (4.4) & 0.009 \\ Intradiscal vacuum (\%) & 62 (8.6) & 19 (10.6) & 0.415 \\ \hline \hline \end{tabular} This table shows the number of diseases diagnosed in 720 training set and 180 test set reports.  
\end{table} 
Table 2: Characteristics of training and testing datasets for the development of the natural language processing pipeline. The highest precision score was 1.0 for 16 radiologic findings--listhesis, scoliosis, spondylosis, annular fissure, disc bulge, disc extrusion, disc protrusion, endplate edema or type 1 Modic, central stenosis, nerve root displaced/compressed, lateral recess stenosis, Schmorl's node, osteophyte, disc space narrowing, any stenosis, and disc sequestration. The lowest precision score was 0.9611 for facet hypertrophy. The highest accuracy score was 1.0 for 10 radiologic findings--listhesis, annular fissure, disc bulge, disc extrusion, disc protrusion, endplate edema or type 1 Modic, lateral recess stenosis, Schmorl's node, osteophyte, and any stenosis. The lowest accuracy score was 0.9611 for facet hypertrophy (Table 3).