The investigation of the effect of transcutaneous electrical nerve stimulation and interferential current on pain in patients diagnosed with lumbar disc herniation and its modeling in artificial intelligence

Lumbar disc herniation (LDH) is one of the most common causes of low back pain and significantly affects individuals' quality of life and functional capacity. Conservative treatment approaches play a primary role in LDH management and electrotherapy modalities are frequently used to alleviate pain. Among these modalities, Transcutaneous Electrical Nerve Stimulation (TENS) and Interferential Current Therapy (IFC) are widely applied non-invasive methods in clinical practice. However, the relative effectiveness of these two interventions on pain reduction remains controversial in the literature. The aim of this study was to compare the effects of TENS and interferential current therapy on pain in patients diagnosed with lumbar disc herniation and to evaluate the effectiveness of these electrotherapy modalities using artificial intelligence-based modeling. The study was designed as a randomized controlled experimental trial. A total of 72 patients aged between 18 and 65 years with a confirmed diagnosis of LDH were included. Participants were randomly assigned into two groups: the TENS group (n=36; 16 males, 20 females) and the IFC group (n=36; 13 males, 23 females). Both groups received electrotherapy treatment for a total of 10 sessions, administered once daily, five days per week, with each session lasting 20 minutes. Pain levels were assessed before and after the treatment period using the Pain Quality Assessment Scale. Statistical analyses were performed using IBM SPSS Statistics version 24.0. Independent samples t-tests were used for between-group comparisons, while paired samples t-tests were applied for within-group analyses. The results demonstrated a statistically significant reduction in pain levels in both the TENS and IFC groups following treatment (p<0.001). However, no statistically significant difference was found between the two groups regarding the magnitude of pain reduction (p>0.05). These findings indicate that both electrotherapy modalities are effective in reducing pain associated with lumbar disc herniation. A distinctive aspect of this study is the application of artificial intelligence in analyzing clinical data. The collected data were transferred to a Python-based Google Colab environment, where machine learning algorithms were employed to model pain-related outcomes. The artificial intelligence-based analysis provided a multidimensional evaluation of treatment effectiveness and supported the findings obtained from conventional statistical methods by identifying individual response patterns to electrotherapy. In conclusion, both TENS and interferential current therapy were found to be effective in reducing pain in patients with lumbar disc herniation. Artificial intelligence-assisted modeling enabled a more objective and comprehensive assessment of treatment outcomes. The results of this study may contribute to clinical decision-making processes and support the development of personalized treatment strategies in electrotherapy applications. KEYWORDS: Lumbar disc herniation, TENS, interferential current, pain, artificial intelligence, electrotherapy