# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Types of Chronic Pain That May Benefit from ML Research  
While many cases of chronic pain originate from primary underlying medical conditions or injury, the majority of studies included in this paper focused on pain conditions with unclear pathophysiology and relatively high prevalence. This subsection describes how ML has been used for the diagnosis, treatment, and management of these conditions. CLBP is the most prevalent chronic pain condition, with estimates falling in the range of 5% to 20% [84]. Prevalence increases linearly with age between the ages of 20 and 60 years and is higher among women than men [84]. In some cases, CLBP can be caused by injury or disease, but ninety percent of patients with CLBP have non-specific low back pain [85]. CLBP is the condition most frequently studied in the current review, and more aspects of the care pathway are covered than is the case for other conditions, as can be seen in Table 3. \begin{table}
\begin{tabular}{c c c} \hline
**Title** & **Participants** & **Data Types** \\ \hline UNBC-McMaster Shoulder Pain & 129 patients with shoulder pain & Video of facial expressions while performing range-of-motion tests. Frames \\ Expression Archive Database [81] & [66 female and 63 male) & are coded with self-reported and observer-reported ratings of pain. \\ \hline EmoPain dataset [64] & 22 patients with CLBP (15 female, 7 male) and 28 healthy controls & A fully labelled multimodal dataset containing face videos, 3D motion capture, and EMG signals from back muscles. Pain-related facial expressions and body movement behaviours were elicited from people with chronic pain performing physical exercises. \\ \hline OpenPain dataset [44] & & \multicolumn{1}{c}{fMRI data of patients with CLBP} \\ \hline Optum Humedica database [83] & \textgreater{}150 million people & De-identified EHR data and claims data, including diagnoses, treatments, pharmacy and over-the-counter prescriptions, text notes, etc. \\ \hline \end{tabular}
\end{table} 
Table 2: Datasets available for academic use. FM has also been examined in several studies. FM is characterised by widespread musculoskeletal pain [92], and its prevalence is estimated as being between 2% and 5% [93; 94]. The most common application of ML in FM is to apply classification and clustering techniques to aid in diagnostics or to discover previously unknown subgroups of FM patients [26; 27; 31; 32; 33; 34; 55; 31; 33; 28; 35; 36; 37; 38; 39; 46; 51; 55]. Identifying subgroups might be particularly useful in the treatment and management of FM since these patients are highly heterogeneous and may respond differently to the same therapy. [PERSON] et al. [31] conducted a study where FM patients were divided into four subgroups using a cluster analysis method. Data on pain, physical, social, and psychological function were used to discover the clusters. Other applications of machine learning in FM included predicting quality of sleep, level of fatigue [60], and suicide risk [62]. ML applications for other functional disorders, such as myofascal pain syndrome (MPS) [28], irritable bowel syndrome (IBS) [26; 49], and chronic fatigue syndrome (CFS) [26; 37; 45], could also be useful for FM patients. Similar to the FM studies, many of these studies focused on the classification of patients. [PERSON] et al. [28] classified MPS patients by SEMG patterns using template matching and K-means clustering. [PERSON] et al. [26] used symptoms data from patients with a functional disorder, FM, IBS, or CFS, to discover symptom clusters. [PERSON] et al. [49] used sMRI data to classify IBS patients versus healthy controls. Two studies focused on the diagnosis of chronic pelvic pain (CPP) (pain in the lower abdomen and/or in the pelvis) [29; 53]. [PERSON] and [PERSON] [29] compared different classification algorithms on the multi-label problem of diagnosing which diseases (e.g., intersitital cyystitis/bladder pain syndrome (IC) and IBS) are causing the pelvic pain. [PERSON] et al.