# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Types of Chronic Pain That May Benefit from ML Research
[53] used sMRI data and a multivariate classification approach to distinguish participants with CPP from healthy controls. Two studies applied ML to identify diagnostic biomarkers for IC [56; 58]. As with many other chronic pain conditions, the underlying cause of the disease and diagnostic biomarkers remain unknown. [PERSON] et al. [56] applied supervised ML on urine samples and questionnaire data to identify biomarkers that can discriminate two sub-types of IC patients and healthy controls. To discover stool-based biomarkers for IC, [PERSON] et al. [58] used ML to classify DNA from stool samples of IC patients and healthy controls. Ankylosing spondylitis (AS), also known as Bekhterev's disease, is a chronic inflammatory autoimmune disease that primarily affects spine joints, causing severe chronic \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **Prevalence** & **Diagnosis** & **Clinical Decision Support** & **Rehabilitation** & **Self-Management** \\ \hline Myofascal Pain Syndrome (MPS) & 9\%–20\% & [28] & & \\ \hline Chronic Musculoskeletal Pain & 11.4\%–24\% & [38] & & \\ \hline Chronic Pelvic Pain (CPP) & 4\%–24\% & [29; 53] & & \\ \hline Chronic Low Back Pain (CLBP) & 5\%–20\% & [39; 44; 52; 54; 77] & [59; 61; 67; 68] & [34; 35; 48; 63; 64; 65; 66] & [47; 71; 73] \\ \hline Chronic Shoulder Pain & 15.1\% & [78; 79; 80; 81] & & \\ \hline Irritable Bowel Syndrome (IBS) & 11\% & [26; 49] & & \\ \hline Interstitial Cystits/bladder Pain & 2.7\%–6.53\% & [56; 58] & & \\ Syndrome (IC) & & & \\ \hline Fibromyalgiagia (FM) & 2\%–5\% & [26; 27; 31; 32; 33; 46; 50; 51; 55] & [27; 60; 62] & \\ \hline Chronic Fatigue Syndrome (CFS) & 0.76\%–3.28 \% & [26; 37; 45] & & \\ \hline Chronic Neck Pain & 2.2\% & & [25] & \\ \hline Rheumatid Arthritis (RA) & 0.41\%–0.54\% & [51] & & \\ \hline Ankylosing Spondylitis (AS) & 0.07\%–0.32\% & [41; 42] & & \\ \hline Trigeminal Neuralgia (TN) & 0.03\%–0.3\% & [40] & & \\ \hline Complex Regional Pain Syndrome (CRPS) & 0.025\% & & [36] & \\ \hline Unspecified Chronic Pain & & [37] & & [69; 72; 74; 76] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Applications of machine learning studies on different types of chronic pain (prevalence sources: [86; 87; 88; 89; 90; 91]). pain [95]. The exact cause of AS remains unclear, and no effective treatment exists [95]. AS was the focus of two studies in the current review, both of which applied ML approaches to the same fMRI dataset to classify AS and healthy controls [41, 42]. For each of the chronic pain conditions complex regional pain syndrome (CRPS), chronic neck pain, and trigeminal neuralgia (TN), we identified a single study. [PERSON] et al. [36] proposed to apply multilayer perceptron neural networks to detect abnormal gait patterns for CRPS. CRPS affects the limbs and can be induced by surgery or trauma [96]. [PERSON] et al. [25] used similarity-based learning for outcome prediction of acupuncture therapy for chronic neck pain. To distinguish TN patients from healthy controls, [PERSON] et al. [40] applied multivariate pattern classification to neuroimaging data. TN is a condition characterised by severe facial pain and can be associated with systemic diseases such as multiple sclerosis or cardiovascular diseases [89]. Four studies focused on shoulder pain; all used the same dataset of facial expression videos to automatically assess pain intensity [78, 79, 80, 81]. Five studies investigated chronic pain in general. Four of these focused on self-management [69, 72, 74, 76], while one study aimed to identify genes relevant to pain [57] to aid diagnostics.