Table 2.

Summary characteristics of pain studies included in this review

Study	Type of pain	Study population	Use of ML	Main findings
Abdollahi 2020 [9]	Low back	94 patients, age 20–50 years	Classification	ML can effectively classify pain intensity based on quantitative kinematic data
Lee 2019 [12]	Low back	53 patients, age 18–60 years	Classification	ML can effectively classify intensity of evoked pain
Liew 2020 [13]	Low back	33 patients and 16 controls, age 18–55 years	Classification	ML can effectively classify pain intensity using electromyographic and kinematic data
Rahman 2018 [15]	Various causes	782 patients	Manifestation	ML can effectively measure and predict pain volatility
Santana 2019 [16]	Low back fibromyalgia	60 patients and 98 controls, age 18–55 years	Classification	ML can effectively classify pain intensity using fMRI data
Snyder 2021 [18]	Low back	10 subjects	Manifestation	ML can classify the relative risk of low back pain due to lifting activities, using gyroscope and accelerometer data
Kimura 2021 [21]	Osteoarthritis	23 patients, age 44–80 years	Classification	ML can effectively classify pain using EEG data
Levitt 2020 [26]	Spinal cord injury	37 patients and 20 controls, age ≥ 25 years	Classification	ML can effectively classify pain using EEG data
Rojas-Mendizabal 2021 [29]	Thoracic	256 patients	Classification	ML can effectively classify pain using demographic and clinical data
Gruss 2015 [25]	Evoked heat pain	85 subjects, age 18–65 years	Classification	ML can effectively classify evoked pain using biopotential data
Santra 2020 [17]	Low back	30 patients	Diagnosis	ML can effectively diagnose the cause of low back pain
Rogachov 2018 [30]	Ankylosing spondylitis	71 patients and 62 controls, age 18–61 years	Classification	ML can effectively classify pain using fMRI data
Grauhan 2021[11]	Shoulder	2442 patients	Diagnosis	ML can effectively diagnose the cause of shoulder pain analysig plain X-rays
Darvishi 2017 [10]	Low back	92 patients and 68 controls, age 29–50 years	Manifestation	ML can predict development of work-related low back pain
Miettinen 2021 [14]	Various causes	277 patients, age 18–77 years	Classification	ML can effectively predict pain based on sleep patterns
Fernandes 2017 [20]	Osteoarthritis	1822 subjects, age 40–79 years	Manifestation	ML can effectively predict pain manifestation in community-based population using clinical data
Lotsch 2020 [22]	Rheumatoid arthritis	288 patients, age 18–70 years	Manifestation	ML can effectively predict pain manifestation using demographic and clinical data
Tighe 2020 [24]	Post-surgery	8071 subjects, age > 21 years	Manifestation	ML can effectively predict acute pain manifestation using clinical data
Tan 2020 [31]	Labor	20,716 subjects	Manifestation	ML can effectively predict pain manifestation during labor using clinical data
Juwara 2020 [28]	Cancer	195 subjects, mean age 56 years	Manifestation	ML can effectively predict manifestation of neuropathic pain using clinical data
Goldstein 2020 [33]	Low back pain	65 patients, age 21–70	Manifestation	ML can effectively predict pain manifestation based on clinical data
Yang 2018 [32]	Sickle cell disease	40 patients	Classification	ML can predict pain intensity using physiological parameters
Pouromran 2021 [27]	Evoked heat pain	87 subjects, age 18–65 years	Classification	ML can predict pain intensity using electrophysiological parameters
Parthipan 2019 [23]	Post-surgery	4306 subjects, mean age 58 years	Management	ML can effectively predict the required opioids dose for pain management
Ahn 2018 [19]	Osteoarthritis	40 patients, age 50–70 years	Management	ML demonstrated the effect of transcranial direct current stimulation (tDCS) in the management of pain
Wang 2021 [34]	Cancer	746 subjects	Management	ML can effectively predict whether patients should receive local treatment for pain due to bone metastases