Accepted on 25 Mar 2021
Submitted on 29 Jan 2021
Low back pain (LBP) is the leading cause of disability worldwide [1]. The incidence of LBP is highest in the third decade of life, with overall prevalence increasing with age until the person is 60–65 years of age and then LBP gradually declines [2]. LBP is a health and socio-economic problem since LBP affects the active population and is the most common cause of workforce absenteeism, mainly for people with high intensity work compared to sedentary work [3, 4, 5]. LBP is a recurrent problem – estimates of recurrence at 1 year range from 24% to 80% [2], what makes prevention an opportunity. Approximately 90% of the patients diagnosed are classified as having «non-specific» LBP, where no clear cause can be found [6] and treatment is directed at reducing pain and its consequences. Management of the condition consists of education and reassurance, analgesic medicines, and non-pharmacological therapies [7]. Physical and psychological treatments, information, and education programmes, combined with manual treatments, are likely to be cost-effective options for LBP [8]. The current evidence suggests that exercise alone or in combination with education, is effective in preventing LBP [9]. Home-based rehabilitation programmes are effective in preventing LBP [10, 11], but the success of an exercise program depends on the adherence of patients to the treatment plan and on the accurate performance of the exercises, which is problematic to measure accurately [12].
Telerehabilitation (TR) is a solution that allows for home-based rehabilitation. TR research started about 20 years ago [13] with a focus on information technologies to provide remote support, assessment and information to people with physical and/or cognitive impairments [14]. The technology used in TR may include video conferencing platforms, wearable devices, apps, audio and video communication and social media, as well as many research-driven prototypes housed on a variety of platforms [15]. More complex solutions may further incorporate robots [16] and machine-learning-based systems [17].
Machine Learning (ML) is a branch of artificial intelligence that addresses the question of how to build computers that improve automatically through experience [18, 19]. It provides computational methods for accumulating, changing, and updating knowledge-based models in intelligent systems. It provides learning mechanisms which can infer predictions from examples or data.
ML methods are useful in cases where algorithmic solutions are not available, there is a lack of formal models, or the knowledge about the application domain is poorly defined. During the last couple of decades, ML has been increasingly explored in the context of the medical sciences. In this context, ML is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g. prediction of disease progression, for the extraction of medical knowledge for outcomes research, for therapy planning and support, and for overall patient management [20]. There are two main forms of ML, particularly when it comes to classification methods: supervised and unsupervised. In supervised ML, algorithms are given labelled training data, which is analysed for features important to the discrimination of classes, and then «trained» with this data before being tested with unseen unlabelled data. Unsupervised ML is used to identify patterns without prior knowledge of their classes; common forms are cluster analysis (where data is grouped by patterns of characteristics) or association (where rules are discovered by which data is governed) [19].
The majority of publications on AI in spinal disorders are in the domain of diagnosis, followed by prognosis, prediction, and biomechanical for spinal applications [21]. A recent review concluded that AI and particularly ML could enhance the ability to detect patterns of clinical characteristics in LBP and guide treatment [22]. As there has been enormous progress in the fields of artificial intelligence (AI) and data sciences in the last decade, and as there is an increasing development in the Telerehabilitation field, one of the applications of AI in Telerehabilitation is through virtual coaching. A virtual coach can provide guidance and training to users through a set of tasks, with the aim of supporting positive actions or assisting in learning new skills [23]. In LPB virtual coaches could help users to define and preserve an exercise program, suggest problem-solving skills training, or advise patients.
Reliable non-invasive monitoring procedures combined with self-management are crucial to provide the necessary clinical data for reliable TR. Data collected via inertial sensors in wearable technologies can be used to classify whether users are accurately performing and adhering to the exercise, with up to 99.4% classification accuracy, as demonstrated in [24]. However, this type of approach introduces discomfort to the user, as the sensors must be placed in contact with the body during capture. Furthermore, the sensors must be placed in the exact corresponding positions, a task that takes time and requires experience. The ideal solution would be based on marker less visual perception of the user’s posture and position in space, whether static or dynamic. Such a solution, would make it possible for the patient to perform rehabilitation exercises without being restricted to a specific point in the room/scenario nor unconstrained by wearable apparatus, resulting in greater natural movement and effective comfort for the user.
Another challenge in TR is engagement. Several studies suggest that increases in engagement with rehabilitation exercise can result from gamification, by leveraging principals of game design such as meaningful play, feedback, goals, reward, challenge, difficulty, failure and flow, [25, 26].
The aim of this systematic review is to identify studies where machine learning techniques were applied for rehabilitation of chronic (defined as pain that continues for 12 weeks or longer) and non-specific LBP (no clear tissue cause of pain can be found). The research question for this systematic review, which followed the PICOS components (Population, Intervention, Comparison, Outcomes, Study Design) [27] is: in people with chronic and non-specific LBP (P) what is the utility and effectiveness/efficacy of Machine Learning applied for Rehabilitation (I) compared with standard/usual care (C), what outcomes (clinical and non-clinical, e.g. acceptability, usability, and adherence) were measured (O) and what types of studies have been done (S).
This review is based on research material obtained from MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Web of Science and IEEE Xplore until January 19, 2021. The selection of articles was performed manually, as described in the following section. No automatic limits were applied. The search terms were those found in the U.S. National Library of Medicine’s controlled vocabulary (MeSH – medical subject headings). A search in PROSPERO was made to exclude any revision already initiated. The same search terms were used for all databases to guarantee comparison among obtained results. The search string and Boolean operators were: («machine learning» OR «artificial intelligence») AND (rehabilitation OR physiotherapy OR exercises) AND («back pain» OR «low back pain» OR «lumbar pain»). The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement [28] (Figure 1). Additional searching included visually scanning reference lists from relevant studies, hand searching key journals and conference proceedings, contacting study authors, experts and other organizations, searching internet resources and citation searching.
Flow diagram adapted from PRISMA, showing the process for identification and screening of articles for inclusion and exclusion.
After removing duplicates, two authors independently screened records based on titles and abstracts of papers. The inclusion criteria were: 1) focus on chronic and non-specific LBP; 2) general population (specific groups such as a particular profession nor practitioners of a specific sport or activity excluded); 3) using ML techniques; 4) focus on clinical outcomes on physical examination (although other outcomes such as usability, acceptability/adherence are also relevant); 5) applicable to rehabilitation settings; 6) human research. No restrictions were included based on race, sex or age. For ML approaches in LBP, there was no restriction on study design, to ensure that all research on this matter to date was identified. Exclusion criteria were: 1) no peer review of full conference abstract (proceedings excluded); 2) non original research (reviews or opinion papers excluded); 3) not written in English; 4) non published results (e.g. trials in process); 5) non patient involvement research. A record was excluded when marked by the two authors. All the remaining articles were further examined through their full text for exclusion of irrelevant studies. Disagreements were resolved through team discussion. The protocol was registered in PROSPERO (CRD42021232769).
The initial search retrieved 83 records initially. After a stepwise process of screening titles, abstract, and then full-text articles using the defined eligibility criteria, the authors identified 14 studies to be included (see Figure 1). An overview of the articles and their main characteristics can be found in Table 1.
Table 1
Studies characteristics included in systematic review.
| AUTHOR, PUBLICATION YEAR | POPULATION | INTERVENTION | CONTROL | OUTCOMES | STUDY DESIGN | RESULTS |
|---|---|---|---|---|---|---|
| Wai Lo, 2018 [29] | 161 LBP | ML method: a Neural Network (MLP-ANN) was used to
determine the most suitable exercise program for each user, based on
the subjective information. A mobile App called “Well Health» suggests personalized interventions that are tailored to individual needs for the self-management of chronic neck and back pain, according to reported symptoms on the questionnaire. Input: evaluation questionnaire. The idea was to explore if using the App (i) increases time spent on therapeutic exercise, (ii) affects pain level (assessed by the 0-10 Numerical Pain Rating Scale), and (iii) reduces the need for other interventions. |
No control group «before-after» comparison |
Adherence Pain Need for other interventions Usability |
Non controlled Non randomized Inclusion criteria: 18–65 years, neck or LBP within the past 3 months, access to a mobilephone that could play video on the internet. |
An increase in time spent on therapeutic exercise per
day was observed. The median Numerical Pain Rating Scale scores were 6 (interquartile range [IQR] 5–8) before and 4 (IQR 3-6) after using the mobile app (95% CI 1.18–1.81). 3-point reduction in participants who used the app for more than six months. Reduction in the usage of other interventions while using the app. |
| Naifu Jiang, 2017 [30] | 30 LBP | ML method: Support Vector Machine used to discriminate
between “responding” and “non-responding”
LBP patients to functional restoration rehabilitation after a
12-week rehabilitation program. Topography SEMG is used as the data
source for feature extraction. Input data: dynamic SEMG topography during symmetrical and asymmetrical trunk-movement. |
48 healthy | Root-mean-square difference (RMSD) between painsymptoms: visual analogue scale (VAS), Oswestry Disability Index (ODI) calculated from OswestryLow Back Pain Questionnaire | Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
RMSD feature parameters following rehabilitation in the “responding” group showed a significant difference (p < 0.05) with the one in the “non-responding” group. |
| Louise Sandal, 2020 [31] | 51 LBP (Follow-up was obtained for 43 LBP) |
ML method: A recommendation system provides weekly
tailored self-management plans targeting physical activity, strength
and flexibility exercises, and education for 6 weeks, using an App
called selfBACK. The construction of the self-management plans was
achieved using case-based reasoning (CBR) to capture and reuse
information from previous successful cases. Input data: pain-related disability (Roland-Morris Disability Questionnaire). |
No | Participantscompleted the primary outcome pain-related disability (Roland-Morris Disability Questionnaire [RMDQ]) at baselineand 6-week follow-up along with a range of secondary outcomes. Metrics of app use were collected throughoutthe intervention period | Non controlled Non randomized Inclusion criteria: who had sought help for LBP of any duration from primary care (physiotherapy, chiropractic, or general practice) within the past 8 weeks. |
Pilot participants engaged with the app on a weekly basis and reported high achievement scores within all three content categories: physical activity, exercise, and education. |
| Nian Wang, 2018 [32] | 119 LBP | ML method: A Random Forest was used to recognize
symptomatic NLBP muscles (6 muscles) via an isometric
exercise. Input data: wearable sEMG measurements. |
72 healthy | sEMG test | Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
The average recognition accuracy was 86.16% for all six
low back muscles. Show potential value in unspecific LBP treatment. |
| Diana Andrei, 2015 [33] | 163 LBP medical recovered patients (lot2) |
ML method: A modified fuzzy inference system is used to
implement an artificially intelligent patient assessment system. The
fuzzy system consisting of 246 rules (51 of the rules indicate that
appropriate action must be of lumbar spine surgery, 163 of the rules
indicate that the patient should continue medical rehabilitation and
32 of the rules indicate that the patient is medically
healthy). Input: patient’s current medical status represented by eight linguistic variables obtained using a subjective questionnaire to assess pain intensity and the level of the daily activity described in burned calories, the forward flexion, backward extension of the torso, and the left and right flexion and the rotation (measured using a Zebris system). |
107 LBP surgical patients (lot 1) |
Daily activities according to Kcal (WHO formula), functional status | Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
Patients from lot 1 significantly decreased the risk of
a new surgery compared with lot 2 that increased the risk of surgery. Patients in lot 2 were recommended for surgery. |
| Temitayo O., 2014 [34] | 18 LBP | ML method: Support Vector Machine with feature level
fusion of body motion and muscle activity descriptors was used to
discriminate three levels of pain (none, low and high). All subjects
underwent a forward-reaching exercise which is typically feared
among people with chronic back pain. Salient features were
identified using a backward feature selection process. Input data: body motion capture (MoCap) and sEMG data from the lumbar paraspinal and upper trapezius muscles. The levels of pain were categorized from control subjects (no pain) and thresholded self-reported levels from people with chronic pain. |
13 healthy | MoCapand sEMG information. Self-reported levels of pain |
Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
Using feature sets from each modality separately led to high pain classification: F1 scores of 0.63 and 0.69 for movement and muscle activity, respectively. However, using a combined bimodal feature set, this increased to F1 = 0.8. |
| Temitayo O., 2015 [35] | Datasets from 23 persons with chronic pain (including LBP) | ML method: Random Forest (RF) and Support Vector
Machine (SVM) was used to automatically discriminate between people (i) with a low level of pain, (ii) high-level pain, and (iii) control participants while exercising. A variation of the Branch and Bound algorithm (B&B) was used to optimize the feature selection for the two classification algorithms. |
Datasets from 30 persons without chronic pain | Accuracy of pain classification As depression can affect pain experience, participants’ depression scores were included on a standard questionnaire and this improved discrimination between the control participants and the people with pain | Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
Best results were obtained from the feature set
optimization algorithms: 94% and 80% for the full trunk flexion and
sit-to-stand movements. Flexion only had one instance misclassified. |
| Mashfiqui Rabbi, 2018 [36] | 10 LBP | ML method: MyBehaviorCBP is a mobile phone app that
uses machine learning on sensor-based and self-reported physical
activity data to find routine behaviors and automatically generates
physical activity recommendations that are similar to existing
behaviors. Reinforcement learning (RL) is used to continually adapt
the recommendations to the patient. The RL agent can take a sequence
of decisions in an environment to reach a predefined objective where
each subsequent decision is based on the success or failure of the
previous decisions. Input Data: patient questionnaire at the end of the study. |
Compared the levels of adherence to the suggestions between the experts and the automated system | Acceptance toward the different forms of recommendations | Controlled Non randomized 5-week pilot study After a week long-baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, the automated MyBehaviorCBP recommendations were issued. |
90% (9/10) of participants felt positive about trying the MyBehaviorCBP and found MyBehaviorCBP recommendations easier to adopt compared to the control (βint = 0.42, P < .001) on a 5-point Likert scale. |
| Masoud Abdollahi, 2020 [37] | 94 LBP | ML method: Support Vector Machine (SVM) and Multi-Layer
Perceptron (MLP) were used to categorize patients into two main
groups: high vs. low-medium risk from kinematic data. STarT Back
Screening Tool (SBST) results were used as ground truth.
Inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at a self-selected pace. Input data: kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output used as ground truth. |
Provided algorithm results are compared against SBSTresults (used as ground truth). Classification according to the results of the STarT questionnaire in high, medium, and low-risk (28, 37, and 29, respectively) | Trunk motion and balance-related measures, in conjunction with STarT output | Controlled Not randomized Inclusion criteria: between the age of 20–50, nonspecific LBP with or without radiating leg pain, during the assay, pain intensity had to remain less than five on the Visual Analog Scale (VAS); free of any form of spinal surgery. |
Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. The use of time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). |
| Kerstin T, 2020 [38] | 244 light to moderate LBP + 19 higher levels of LBP validation |
ML method: Random Forest models were used based on
information extracted from the Patient-Reported Outcome Measures
(PROMs), combined with other factors like age and gender. The model
predicts the presence or absence of impairment at a specific ICF
category. Input data: Patient-Reported Outcome Measures (PROM’s) |
Performance of the proposed method was compared with ICF classification from experts | Roland-Morris disability questionnaire, Pain Disability Index Activity and participation component of the ICF brief core set for low back pain | Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
The accuracy for 9 of the 11 ICF categories was above 75%. |
| Bernard Liew, 2019 [39] | 17 LBP | ML methods: three scalar-on-function (SoFR) logistic
regression models were used for binary classification of LBP to
evaluate the predictive performance of statistical models that
distinguishes different low back pain (LBP) sub-types and healthy
controls. Input data: time-varying signals of electromyographic and kinematic variables, collected during low-load lifting and between-group comparisons in the baseline demographic and pain characteristics, for continuous and categorical variables, respectively. |
Healthy control = 16, RemissionLBP = 16 | Demographic and pain characteristics, kinematic and electromyographic variables | Controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
The prediction accuracy was between 90.4%-96.7%. |
| Norbert Gal, 2014 [40] | 15 LBP | ML methods: A Fuzzy Inference System was used to
predict appropriate action for patients that presented lower back
pain. Input data: questionnaires (demographic data), combined with parameters related to the mobility of the spinal cord. A Zebris Mobility facility measures the minimum and maximum values that reflect the mobility of the spinal cord when the patient performs flexion, extension, and rotation. |
No | Daily activity expressed in calories (24 possibilities)
Mobility degree measured using the Zebris device (minimum/maximum – 3 values). The Zebris Mobility (ZM) device measures the minimum and maximum values reflecting the mobility of the spinal cord when the patient performs flexion, extension, and rotation |
Non controlled Non randomized Detailed inclusion and exclusion criteria not specified. |
The results for both physical exercises, Sit-to-Stand and One-Leg-Stand, evidenced an F1-score of 0.8 and 0.73, respectively. The conclusion was that supervised machine learning applied on labels with a high level of abstraction like «guarding» is a viable solution. |
| Min Aung, 2014 [41] | 21 LBP | ML method: Ensembles of Decision Trees (Random Forests)
were used to automatically recognize a specific form of protective
behavior, «guarding», common in people with chronic lower
back pain. Two classes were considered, «guarded» and
«not guarded». A static feature vector of 30 elements was
used to represent each exercise. Input data: posture and velocity, based features from motion capture (Inertial Measuring Unit) and EMG data (4 wireless EMG sensors attached to the lumbar paraspinal muscles and the upper section of the trapezius muscles). The training testing datasets were manually labeled by four experts. |
comparison of the proposed algorithms against the labelled version of the same exercises performed by the 4 experts | Ranges of the joint angles Mean values of joint energies Mean values of rectified EMG values |
Controlled Non randomized Inclusion criteria: aged between 18 and 65 years, neck and LBP within the past 3 months, access to a mobile phone that could play video on the internet. Exclusion criteria: medically unstable and reported having red flags. |
The results for both physical exercises (Sit-to-Stand and One-Leg-Stand) were F1-score: 0.8 and 0.73 and show that supervised machine learning on labels with a high level of abstraction such as guarding is feasible if a specific scenario approach is used. |
| Fasih Haider,2020 [42] |
30 LBP | ML method: Four ML methods were evaluated: decision
trees (DT), nearest neighbor (KNN), linear discriminant analysis
(LDA), and Random Forest (RF). A marker-based system was used to
capture data that allowed the previously cited methods to
automatically classify participants according to: i) Pain: three categories – Healthy, Low-level pain, and High-level pain. ii) Movement behavior: two classes: -Not protective and Protective. Input data: captured data for each subject’s movement (eight cameras were used with eight markers positioned on each subject’s body). A public dataset was used to train and evaluate the ML algorithms. |
28 healthy | Matthews Correlation coefficient (MCC) was used to evaluate the performance of both pain recognition and movement behaviour tasks | Controlled No randomized Detailed inclusion and exclusion criteria not specified. |
The F1-classification scores of 0.81 and 0.73 for sitting to standing and one leg stand exercises respectively. |
Table 1 summarizes the main characteristics of the studies based on PICOS assessment and main studies results. No randomized control trials (RCT) were found. The majority of the studies did not report details on the study methodology used, which prevents quality analysis of the studies. Information on inclusion and exclusion criteria were absent in almost all the studies. Ten studies [30, 32, 33, 34, 35, 36, 38, 39, 41, 42] made an effort to have a control group but definition of controls was not always the same: in four studies control group included healthy patients [30, 32, 34, 42], one used datasets from healthy persons [35]; in one study [33] the control group was represented by LBP surgical patients; in three studies [36, 38, 41] the comparison used was experts suggestions/classification/evaluation (versus ML system suggestions); in one study [34] the algorithm results were compared against a back screening tool results; in one study [39] the results from active LBP patients were compared with healthy controls and with remission LBP patients.
In all studies, the results from ML solutions were superior to conventional approaches, but that measurement was not always rigorously ascertained and statistically analysed.
The outcomes measured were very heterogeneous. Pain (visual analogue scale) was the main outcome chosen by researchers. Patient questionnaires focusing on disability impact were used in five of the studies [29, 30, 31, 33] [38]. Usability, acceptability and adherence were also recorded in two studies [29, 36]. Accuracy in the difference between ML results and experts’ opinion/evaluation was another important concern found in this review [36, 38, 41] and the results were quite promising.
ML techniques were also heterogeneous. They used input data from patient questionnaires and from sEMG and motion capture systems. They were used mainly in a supervised ML approach for applications of pain, diagnostics, and exercise prescription. A great number of works rely on Support Vector Machines and Random Forests, which are well-established and well-proven methods in the literature. However, it was disappointing not to find works using more recent approaches used in ML, such as Deep Learning methods, which have shown to be superior to those present in the analysed works. This could be justified by the great demand for big amounts of data, in order for these methods to properly model the problem, which is a constraint for a quick development and deployment strategy.
LBP conventional rehabilitation is based on clinical assessment through information that it is given by patients (such as level of pain or impact in daily living activities) and physical examination (such as posture, muscle contractions, range of motion, gait pattern). Based on that information, doctors decide on the most appropriate therapeutic plan, including ergonomic education, exercises, and medication. ML could help either in the diagnosis [32, 34, 35, 37, 38, 39, 41, 42] or in supporting doctors’ decisions [33, 40] or guiding patients through a personalized therapeutic plan [29, 31, 36], with accuracy and in real-time, giving important clinical information to doctors and therapists and allowing adherence monitoring. ML could also be useful to identify patients’ response to functional rehabilitation [30], automatic recognition of fear-avoidance behaviour [41, 42] or to categorize patients into different subgroups of LBP risk [37]. Some of the studies using ML for LBP treatment use apps [29, 31, 36], since almost everyone has a smartphone nowadays, including medical students and doctors use medical smartphone apps related to procedure documentation, disease diagnosis, clinical score and drug reference [43]. Pilot studies of mobile phone based personalized physical activity recommendations for chronic pain self-management have shown positive results in feasibility and acceptability by patients [44]. The development of mobile phone apps using Cognitive Behavioural Therapy (CBT) principles is increasing within the research area, in order to increase motivation [45].
The subjective information given by patients is crucial to assess levels of pain or depression. ML cannot substitute the absence of this crucial feedback, but it can help process and analyse such information if it is made available.
Pain assessment is probably the most common feature of LBP for the negative impact on functionality and well-being, being one of the most important determinants in quality of life. Because exercises increase the level of pain, anxiety about anticipated pain increase may lead to seatback and intensified sensitivity to pain [35] and a fear-avoidance behaviour [46]; the avoidance is expressed through self-protective body movement to avoid strain in the painful area, decreasing exercise adherence [41, 42]. Visual Analogue Scale or Oswestry Low Back Pain Questionnaire are examples of most used tools for pain assessment and disability in LBP patients [30, 37]. Depression scales are also used in LBP research [35], as well as daily activity questionnaires [33, 40]. A more holistic approach is the one given by International Classification of Functioning, Disability and Health (ICF), a standardized reference system for classifying health by accounting for functioning [47]. ML techniques could be applied based on a translation algorithm that automatically links health information reported from LBP patients to categories representing the Activity and Participation components of ICF [38]. Although pain is usually reported by the patient, studies have been done which apply ML based on body motion and muscle activity to discriminate different levels of pain [35]. Other sensing modalities could be used for clinical assessment, such as sEMG (surface electromyography). LBP is related to localized muscle fatigue [48]. There is a significant difference in the patterns of muscles firing phase between symptomatic and healthy muscles, resulting in different muscle compensation in order to reduce the load on a painful tissue to protect from further pain [49]. Based on that fact, some studies used ML techniques to accurately locate the symptomatic muscles after collecting sEMG [32]. ML can also be used in the development of an assessment system based on a modified fuzzy inference system consisting in a determined number of rules, which could be used to support treatment decisions [33, 40].
A broad search in five databases following a PRISMA checklist in order to find relevant literature on the subject and assess different aspects, was systematically conducted, while recognising some limitations. The main focus was placed on clinical research in setting using machine learning for LBP rehabilitation and not on technical issues or proof of concept papers, which excluded many engineering papers. The inclusion of only articles written in English may contribute to a publication bias. Five databases were included but more databases should be included in future research. Few clinical studies were found and most of them were exploratory studies, with small sample numbers. None of the located studies was a RCT study, which is the “gold standard” for experimental study design standard of clinical research. A fact perhaps related with the recent application of ML in the Rehabilitation field. The Cochrane seven domains of risk of bias assessment [50], used in randomized trials, were not possible to check due to missing data. For non-randomized studies there are other tools for assessing quality, such as the ROBINS-I [51], but most of the studies were focused on ML solutions feasibility and not in studies quality, making it difficult to apply them. The heterogeneity of studies did not allow a statistical analysis and a rigorous comparison between them.
It was clear that the focus of the studies was on presenting new solutions and comparing the results of novel approaches with conventional assessment and not on the quality of the clinical research and study design. This is common in the early stages of new technical solutions, where feasibility and pilot studies gradually move towards higher quality studies, in which RCT are included [52]. In clinical research of medical devices, RCTs, and especially ensuring blindness for the participants, is not always easy nor feasible as [53] has highlighted. Nonetheless researchers should strive towards running better quality clinical studies with less bias and hopefully this paper can help them as a steppingstone in this fascinating interface between AI and the human body.
Based on the scientific literature, it seems that supervised machine learning approaches applied in rehabilitation could help health professionals and LBP patients to manage this condition that affects a significant percentage of the active population. This could contribute to improving the quality of life of people with LBP and to decrease in work absenteeism, that could ultimately represent economic and social gains. ML is a promising tool for personalized rehabilitation as it contributes to a new paradigm of healthcare in which interventions are based on individual patient characteristics. ML applied to support clinical decisions has been explored. Some studies used mobile phones apps to help patients self-manage their LBP remotely, by giving them education material and exercises guides and by monitoring the performance and adherence, being a promising method to use in the telerehabilitation field.
Although all the studies included had better results than conventional approaches, and therefore, provide indicate ML to be a useful tool for the future of LBP rehabilitation, the small number of studies, their heterogeneity in methodology, in outcome measurements, and in quality (for instance no RCT was found), leave to the investigators the opportunity and responsibility to pursue the path towards the strengthening of evidence. A multidisciplinary approach on research in artificial intelligence applied in Health is very useful, as it gives a holistic view and accelerates innovation, but future clinical investigation should be more rigorous. “More and better-quality studies, involving larger samples, using a control group (ideally RCT), and which methodology is rigorously described and follows the guidelines and best practices in research are needed in the future”. Engagement is an important issue to be considered in assessing home rehabilitation programmes adherence and this should also be further investigated in future studies. What will make the AI solutions acceptable for clinical use depends on, but is not limited to, the evidence of how the sensors/cameras capture the motions reliably, how the recommendations from ML are clinically relevant/appropriate, and the ease of use of incorporating the ML recommendations into the exercise. These issues should be kept in mind for future studies.
The databases generated during the current study are available from the corresponding author on reasonable request.
The additional file for this article can be found as follows:
Supplement File.AMSTAR 2 Results. DOI: https://doi.org/10.29337/ijdh.34.s1
AI – Artificial Intelligence
CBT – Cognitive Behavioural Therapy
ICF – International Classification of Functioning, Disability and Health
LBP – Low Back Pain
ML – Machine Learning
RCT – Randomized Controlled Trials
sEMG – Surface Electromyography
TR – Telerehabilitation
The authors have no competing interests to declare.
The search equation was proposed by the first author and debated with the co-authors until a consensus was reached. The first author, Paula Amorim, has made substantial contributions to the conception, design of the work, acquisition of data, analysis and interpretation of data and has drafted the work. The second author, João Ruivo Paulo, the third author, Paula Alexandra Silva, and fourth author, Paulo Peixoto have made substantial contributions to the drafting, analysis and interpretation of data. The other authors, Miguel Castelo-Branco and Henrique Martins, have made substantial contributions to revising it critically for important intellectual content and final approval of the version to be submitted, identification of limitations and conclusions.
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