Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years

Predicting pregnancy-related pelvic girdle pain using machine learning

BACKGROUND

Pregnancy-related pelvic girdle pain (PPGP) is a common complication during gestation which negatively influences pregnant women's quality of life. There are numerous risk factors associated with PPGP, however, there is limited information about being able to predict the diagnosis of PPGP.

OBJECTIVE

To compare machine learning (ML) and traditional predictive modelling to predict the clinical diagnosis of PPGP.

METHODS

This study reanalysed data from 780 pregnant women attending a tertiary hospital. ML algorithms, including Logistic Regression (LR), Random Forest, Xtreme Gradient Boost (XGBoost), and K-Nearest Neighbors, were used to predict the clinical diagnosis of PPGP. Feature selection methods and cross-validation were employed to optimize model performance, with the Area Under the Receiver Operating Characteristic Curve (AUROC) as the primary outcome measure.

RESULTS

The ML models, particularly XGBoost and LR, demonstrated high levels of predictive accuracy (AUROC = 0.70). Key predictive factors were a history of low back pain/pelvic girdle pain (LBP/PGP) in previous pregnancies, family history, gestational age, and a longer duration of standing during the day. The history of LBP/PGP in previous pregnancies emerged as the most significant predictor.

CONCLUSIONS

This study highlighted the potential of ML models to enhance the ability to predict PPGP and offers a more accurate and comprehensive approach to identifying women at risk of PPGP. The integration of ML techniques into clinical practice could improve early identification and inform preventative and intervention strategies, potentially reducing the impact of PPGP on pregnant women.

Predicting the Risk of Lumbar Prolapsed Disc: A Gene Signature-Based Machine Learning Analysis

INTRODUCTION

Lumbar prolapsed disc (LPD) is a leading cause of low back pain, contributing significantly to global disability and healthcare burden. This study aimed to develop machine learning models to predict the risk of LPD by analysing gene expression profiles for early detection.

METHODS

Transcriptomic data from peripheral blood samples were obtained from the Gene Expression Omnibus (GEO) database, with dataset GSE150408 used for training and GSE124272 for testing. The training dataset included 17 patients with sciatica resulting from LPD, all of whom had magnetic resonance imaging confirmation of single-level LPD at either the L4/5 or L5/S1 levels. Data from 17 healthy volunteers were used as controls. Recursive feature elimination (RFE) was employed to identify the most relevant gene signatures among 23 pain-related genes. Machine learning models, including support vector machine (SVM), random forest, k-nearest neighbours (KNN), logistic regression, and Extreme Gradient Boosting (XGBoost), were trained and evaluated. Model performance was assessed using accuracy, area under the curve (AUC), F1 score, and Matthews correlation coefficient (MCC).

RESULTS

Eight key gene signatures were identified as significant predictors of LPD, with MMP9 exhibiting the highest importance score. Most of these genes were differentially expressed between patients with LPD and healthy controls (p < 0.05). Among the models, random forest demonstrated the highest accuracy (0.80, 95% CI 0.73-0.85) and MCC (0.64, 95% CI 0.53-0.76), followed by KNN, XGBoost, and SVM. Overall, the random forest model exhibited the most robust performance in predicting the risk of LPD.

CONCLUSION

The results of our study suggest that machine learning models based on pain-related gene signatures may identify patients at high risk of developing LPD with reasonably high accuracy. These prediction models could perhaps be integrated into clinical diagnostic tools to enhance early diagnosis and prevention.

Integrating multidimensional data analytics for precision diagnosis of chronic low back pain

Low back pain (LBP) is a leading cause of disability worldwide, with up to 25% of cases become chronic (cLBP). Whilst multi-factorial, the relative importance of contributors to cLBP remains unclear. We leveraged a comprehensive multi-dimensional data-set and machine learning-based variable importance selection to identify the most effective modalities for differentiating whether a person has cLBP. The dataset included questionnaire data, clinical and functional assessments, and spino-pelvic magnetic resonance imaging (MRI), encompassing a total of 144 parameters from 1,161 adults with (n = 512) and without cLBP (n = 649). Boruta and random forest were utilised for variable importance selection and cLBP classification respectively. A multimodal model including questionnaire, clinical, and MRI data was the most effective in differentiating people with and without cLBP. From this, the most robust variables (n = 9) were psychosocial factors, neck and hip mobility, as well as lower lumbar disc herniation and degeneration. This finding persisted in an unseen holdout dataset. Beyond demonstrating the importance of a multi-dimensional approach to cLBP, our findings will guide the development of targeted diagnostics and personalized treatment strategies for cLBP patients.

Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis

Background and objective. Low back pain (LBP) is considered the most common and challenging disorder in health care. Although its incidence increases with age, a student's sedentary behavior could contribute to this risk. Through machine learning (ML), advanced algorithms can analyze complex patterns in health data, enabling accurate prediction and targeted prevention of medical conditions such as LBP. This study aims to detect the factors associated with LBP among health sciences students. Methods. A self-administered modified version of the Standardized Nordic Questionnaire was completed by 222 freshman health sciences students from May to June 2022. A supervised random forest algorithm was utilized to analyze data and prioritize the importance of variables related to LBP. The model's predictive capability was further visualized using a decision tree to identify high-risk patterns and associations. Results. A total of 197/222 (88.7%) students participated in this study, most of whom (75%) were female. Their mean age and body mass index were 23 ± 3.8 and 23 ± 3.5, respectively. In this group, 46% (n = 90) of the students reported having experienced LBP in the last month, 15% (n = 30) were smokers, and 60% (n = 119) were involved in prolonged sitting (more than 3 h per day). The decision tree of ML revealed that a history of pain (score = 1), as well as disability (score= 0.34) and physical activity (score = 0.21), were significantly associated with LBP. Conclusions. Approximately 46% of the health science students reported LBP in the last month, and a machine-learning approach highlighted a history of pain as the most significant factor related to LBP.

Comparing ChatGPT 3.5 and 4.0 in Low Back Pain Patient Education: Addressing Strengths, Limitations, and Psychosocial Challenges

BACKGROUND

Artificial intelligence tools like ChatGPT have gained attention for their potential to support patient education by providing accessible, evidence-based information. This study compares the performance of ChatGPT 3.5 and ChatGPT 4.0 in answering common patient questions about low back pain, focusing on response quality, readability, and adherence to clinical guidelines, while also addressing the models' limitations in managing psychosocial concerns.

METHODS

Thirty frequently asked patient questions about low back pain were categorized into 4 groups: Diagnosis, Treatment, Psychosocial Factors, and Management Approaches. Responses generated by ChatGPT 3.5 and 4.0 were evaluated on 3 key metrics: 1) response quality: rated on a scale of 1 (excellent) to 4 (unsatisfactory); 2) DISCERN criteria: evaluating reliability and adherence to clinical guidelines, with scores ranging from 1 (low reliability) to 5 (high reliability; and 3) readability: assessed using 7 readability formulas, including Flesch-Kincaid and Gunning Fog Index.

RESULTS

ChatGPT 4.0 significantly outperformed ChatGPT 3.5 in response quality across all categories, with a mean score of 1.03 compared to 2.07 for ChatGPT 3.5 (P < 0.001). ChatGPT 4.0 also demonstrated higher DISCERN scores (4.93 vs. 4.00, P < 0.001). However, both versions struggled with psychosocial factor questions, where responses were rated lower than for Diagnosis, Treatment, and Management questions (P = 0.04).

CONCLUSIONS

ChatGPT 3.5 and 4.0 limitations in addressing psychosocial concerns highlight the need for clinician oversight, particularly for emotionally sensitive issues. Enhancing artificial intelligence's capability in managing psychosocial aspects of patient care should be a priority in future iterations.

Predicting Pain Response to a Remote Musculoskeletal Care Program for Low Back Pain Management: Development of a Prediction Tool

BACKGROUND

Low back pain (LBP) presents with diverse manifestations, necessitating personalized treatment approaches that recognize various phenotypes within the same diagnosis, which could be achieved through precision medicine. Although prediction strategies have been explored, including those employing artificial intelligence (AI), they still lack scalability and real-time capabilities. Digital care programs (DCPs) facilitate seamless data collection through the Internet of Things and cloud storage, creating an ideal environment for developing and implementing an AI predictive tool to assist clinicians in dynamically optimizing treatment.

OBJECTIVE

This study aims to develop an AI tool that continuously assists physical therapists in predicting an individual's potential for achieving clinically significant pain relief by the end of the program. A secondary aim was to identify predictors of pain nonresponse to guide treatment adjustments.

METHODS

Data collected actively (eg, demographic and clinical information) and passively in real-time (eg, range of motion, exercise performance, and socioeconomic data from public data sources) from 6125 patients enrolled in a remote digital musculoskeletal intervention program were stored in the cloud. Two machine learning techniques, recurrent neural networks (RNNs) and light gradient boosting machine (LightGBM), continuously analyzed session updates up to session 7 to predict the likelihood of achieving significant pain relief at the program end. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), precision-recall curves, specificity, and sensitivity. Model explainability was assessed using SHapley Additive exPlanations values.

RESULTS

At each session, the model provided a prediction about the potential of being a pain responder, with performance improving over time (P<.001). By session 7, the RNN achieved an ROC-AUC of 0.70 (95% CI 0.65-0.71), and the LightGBM achieved an ROC-AUC of 0.71 (95% CI 0.67-0.72). Both models demonstrated high specificity in scenarios prioritizing high precision. The key predictive features were pain-associated domains, exercise performance, motivation, and compliance, informing continuous treatment adjustments to maximize response rates.

CONCLUSIONS

This study underscores the potential of an AI predictive tool within a DCP to enhance the management of LBP, supporting physical therapists in redirecting care pathways early and throughout the treatment course. This approach is particularly important for addressing the heterogeneous phenotypes observed in LBP.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04092946; https://clinicaltrials.gov/ct2/show/NCT04092946 and NCT05417685; https://clinicaltrials.gov/ct2/show/NCT05417685.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey

This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults aged ≥50 years (Korea National Health and Nutrition Examination Survey), we explored 119 factors to detect LBP in individuals who reported experiencing LBP for at least 30 days within the previous 3 months. Our primary model, model 1, employed eXtreme Gradient Boosting (XGBoost) and selected primary factors (PFs) based on their feature importance scores. To extend this, we introduced additional factors, such as lumbar X-ray findings, physical activity, sitting time, and nutrient intake levels, which were available only during specific survey periods, into models 2 to 4. Model performance was evaluated using the area under the curve, with predicted probabilities explained by SHapley Additive exPlanations. Eleven PFs were identified, and model 1 exhibited an enhanced area under the curve .8 (.77-.84, 95% confidence interval). The factors had varying impacts across individuals, underscoring the need for personalized assessment. Hip and knee joint pain were the most significant PFs. High levels of physical activity were found to have a negative association with LBP, whereas a high intake of omega-6 was found to have a positive association. Notably, we identified factor clusters, including hip joint pain and female sex, potentially linked to osteoarthritis. In summary, this study successfully developed effective XGBoost models for LBP detection, thereby providing valuable insight into LBP-related factors. Comprehensive LBP management, particularly in women with osteoarthritis, is crucial given the presence of multiple factors. PERSPECTIVE: This article introduces XGBoost models designed to detect LBP and explores the multifactorial aspects of LBP through the application of SHapley Additive exPlanations and network analysis on the 4 developed models. The utilization of this analytical system has the potential to aid in devising personalized management strategies to address LBP.

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Applications of artificial intelligence in anesthesia: A systematic review

This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.

Identifying Predictors of Neck Disability in Patients with Cervical Pain Using Machine Learning Algorithms: A Cross-Sectional Correlational Study

(1) Background: Neck pain intensity, psychosocial factors, and physical function have been identified as potential predictors of neck disability. Machine learning algorithms have shown promise in classifying patients based on their neck disability status. So, the current study was conducted to identify predictors of neck disability in patients with neck pain based on clinical findings using machine learning algorithms. (2) Methods: Ninety participants with chronic neck pain took part in the study. Demographic characteristics in addition to neck pain intensity, the neck disability index, cervical spine contour, and surface electromyographic characteristics of the axioscapular muscles were measured. Participants were categorised into high disability and low disability groups based on the median value (22.2) of their neck disability index scores. Several regression and classification machine learning models were trained and assessed using a 10-fold cross-validation method; also, MANCOVA was used to compare between the two groups. (3) Results: The multilayer perceptron (MLP) revealed the highest adjusted R2 of 0.768, while linear discriminate analysis showed the highest receiver characteristic operator (ROC) area under the curve of 0.91. Pain intensity was the most important feature in both models with the highest effect size of 0.568 with p < 0.001. (4) Conclusions: The study findings provide valuable insights into pain as the most important predictor of neck disability in patients with cervical pain. Tailoring interventions based on pain can improve patient outcomes and potentially prevent or reduce neck disability.

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.

A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

BACKGROUND

Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.

METHODS

The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively.

RESULT

The Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated.

CONCLUSIONS

We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors

BACKGROUND

Low back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP.

OBJECTIVE

The purpose of this study was to develop and compare efficient low back pain prediction models using easily obtainable demographic and lifestyle factors.

METHODS

Data from adult men and women aged 50 years or older collected from the Korean National Health and Nutrition Examination Survey (KNHANES) were used. The dataset included 22 predictor variables, including demographic, physical activity, occupational, and lifestyle factors. Four machine learning algorithms, including XGBoost, LGBM, CatBoost, and RandomForest, were used to develop predictive models.

RESULTS

All models achieved an accuracy greater than 0.8, with the LGBM model outperforming the others with an accuracy of 0.830. The CatBoost model had the highest sensitivity (0.804), while the LGBM model showed the highest specificity (0.884) and F1-Score (0.821). Feature importance analysis revealed that EQ-5D was the most critical variable across all models.

CONCLUSION

In this study, an efficient LBP prediction model was developed using easily accessible variables. Using this model, it may be helpful to identify the risk of LBP in advance or establish prevention strategies in subjects who have difficulty accessing medical facilities.

Predictive value of texture analysis on lumbar MRI in patients with chronic low back pain

PURPOSE

The aim of this study was to determine whether MRI texture analysis could predict the prognosis of patients with non-specific chronic low back pain.

METHODS

A prospective observational study was conducted on 100 patients with non-specific chronic low back pain, who underwent a conventional MRI, followed by rehabilitation treatment, and revisited after 6 months. Sociodemographic variables, numeric pain scale (NPS) value, and the degree of disability as measured by the Roland-Morris disability questionnaire (RMDQ), were collected. The MRI analysis included segmentation of regions of interest (vertebral endplates and intervertebral disks from L3-L4 to L5-S1, paravertebral musculature at the L4-L5 space) to extract texture variables (PyRadiomics software). The classification random forest algorithm was applied to identify individuals who would improve less than 30% in the NPS or would score more than 4 in the RMDQ at the end of the follow-up. Sensitivity, specificity, and the area under the ROC curve were calculated.

RESULTS

The final series included 94 patients. The predictive model for classifying patients whose pain did not improve by 30% or more offered a sensitivity of 0.86, specificity 0.57, and area under the ROC curve 0.71. The predictive model for classifying patients with a RMDQ score 4 or more offered a sensitivity of 0.83, specificity of 0.20, and area under the ROC curve of 0.52.

CONCLUSION

The texture analysis of lumbar MRI could help identify patients who are more likely to improve their non-specific chronic low back pain through rehabilitation programs, allowing a personalized therapeutic plan to be established.

Construction and Validation of Machine Learning Algorithms to Predict Chronic Post-Surgical Pain Among Patients Undergoing Total Knee Arthroplasty

BACKGROUND

Chronic post-surgical pain (CPSP) is a common but undertreated condition with a high prevalence among patients undergoing total knee arthroplasty (TKA). An effective model for CPSP prediction has not been established yet.

AIMS

To construct and validate machine learning models for the early prediction of CPSP among patients undergoing TKA.

DESIGN

A prospective cohort study.

PARTICIPANTS/SUBJECTS

A total of 320 patients in the modeling group and 150 patients in the validation group were recruited from two independent hospitals between December 2021 and July 2022. They were followed up for 6 months to determine the outcomes of CPSP through telephone interviews.

METHODS

Four machine learning algorithms were developed through 10-fold cross-validation for five times. In the validation group, the discrimination and calibration of the machine learning algorithms were compared by the logistic regression model. The importance of the variables in the best model identified was ranked.

RESULTS

The incidence of CPSP in the modeling group was 25.3%, and that in the validation group was 27.6%. Compared with other models, the random forest model achieved the best performance with the highest C-statistic of 0.897 and the lowest Brier score of 0.119 in the validation group. The top three important factors for predicting CPSP were knee joint function, fear of movement, and pain at rest in the baseline.

CONCLUSIONS

The random forest model demonstrated good discrimination and calibration capacity for identifying patients undergoing TKA at high risk for CPSP. Clinical nurses would screen out high-risk patients for CPSP by using the risk factors identified in the random forest model, and efficiently distribute preventive strategy.

Machine learning framework for atherosclerotic cardiovascular disease risk assessment

Introduction

Atherosclerotic cardiovascular disease (ASCVD) is the first leading cause of mortality globally. To identify the individual risk factors of ASCVD utilizing the machine learning (ML) approaches.

Materials & methods

This cohort-based cross-sectional study was conducted on data of 500 participants with ASCVD among Tabriz University Medical Sciences employees, during 2020. The data with ML methods were developed and validated to predict ASCVD risk with naive Bayes (NB), spurt vesture machines (SVM), regression tree (RT), k-nearest neighbors (KNN), artificial neural networks (ANN), generalized additive models (GAM), and logistic regression (LR).

Results

Accuracy of the models ranged from 95.7 to 98.1%, with a sensitivity of 50.0 to 97.3%, specificity of 74.3 to 99.1%, positive predictive value (PPV) of 0.0 to 98.0%, negative predictive value (NPV) of 68.4 to 100.0%, positive likelihood ratio (LR +) of 13.8 to 96.4%, negative likelihood ratio (LR-) of 3.6 to 51.9%, and area under ROC curve (AUC) of 62.5 to 99.4%. The ANN fit the data best with an accuracy of 98.1% (95% CI: 96.5-99.1), a specificity of 99.1% (95% CI: 97.7-99.9), a LR + of 96.4% (95% CI: 36.2-258.8), and AUC of 99.4% (95% CI: 85.2-97.0). Based on the optimal model, sex (females), age, smoking, and metabolic syndrome were shown to be the most important risk factors of ASCVD.

Conclusion

Sex (females), age, smoking, and metabolic syndrome were predictors obtained by ANN. Considering the ANN as the optimal model identified, more accurate prevention planning may be designed.

Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health

Digital therapeutics (DTx, software as a medical device) provide personalized treatments for chronic diseases and expand precision medicine beyond pharmacogenomics-based pharmacotherapies. In this perspective article, we describe how DTx for chronic low back pain (CLBP) can be integrated with pharmaceutical drugs (e.g., NSAIDs, opioids), physical therapy (PT), cognitive behavioral therapy (CBT), and patient empowerment. An example of an FDA-authorized DTx for CLBP is RelieVRx, a prescription virtual reality (VR) app that reduces pain severity as an adjunct treatment for moderate to severe low back pain. RelieVRx is an immersive VR system that delivers at-home pain management modalities, including relaxation, self-awareness, pain distraction, guided breathing, and patient education. The mechanism of action of DTx is aligned with recommendations from the American College of Physicians to use non-pharmacological modalities as the first-line therapy for CLBP. Herein, we discuss how DTx can provide multimodal therapy options integrating conventional treatments with exposome-responsive, just-in-time adaptive interventions (JITAI). Given the flexibility of software-based therapies to accommodate diverse digital content, we also suggest that music-induced analgesia can increase the clinical effectiveness of digital interventions for chronic pain. DTx offers opportunities to simultaneously address the chronic pain crisis and opioid epidemic while supporting patients and healthcare providers to improve therapy outcomes.

Machine learning clinical decision support for interdisciplinary multimodal chronic musculoskeletal pain treatment

Introduction

Chronic musculoskeletal pain is a prevalent condition impacting around 20% of people globally; resulting in patients living with pain, fatigue, restricted social and employment capacity, and reduced quality of life. Interdisciplinary multimodal pain treatment programs have been shown to provide positive outcomes by supporting patients modify their behavior and improve pain management through focusing attention on specific patient valued goals rather than fighting pain.

Methods

Given the complex nature of chronic pain there is no single clinical measure to assess outcomes from multimodal pain programs. Using Centre for Integral Rehabilitation data from 2019-2021 (n = 2,364), we developed a multidimensional machine learning framework of 13 outcome measures across 5 clinically relevant domains including activity/disability, pain, fatigue, coping and quality of life. Machine learning models for each endpoint were separately trained using the most important 30 of 55 demographic and baseline variables based on minimum redundancy maximum relevance feature selection. Five-fold cross validation identified best performing algorithms which were rerun on deidentified source data to verify prognostic accuracy.

Results

Individual algorithm performance ranged from 0.49 to 0.65 AUC reflecting characteristic outcome variation across patients, and unbalanced training data with high positive proportions of up to 86% for some measures. As expected, no single outcome provided a reliable indicator, however the complete set of algorithms established a stratified prognostic patient profile. Patient level validation achieved consistent prognostic assessment of outcomes for 75.3% of the study group (n = 1,953). Clinician review of a sample of predicted negative patients (n = 81) independently confirmed algorithm accuracy and suggests the prognostic profile is potentially valuable for patient selection and goal setting.

Discussion

These results indicate that although no single algorithm was individually conclusive, the complete stratified profile consistently identified patient outcomes. Our predictive profile provides promising positive contribution for clinicians and patients to assist with personalized assessment and goal setting, program engagement and improved patient outcomes.

Using artificial intelligence to improve pain assessment and pain management: a scoping review

CONTEXT

Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.

OBJECTIVES

This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.

METHODS

The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.

RESULTS

This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.

CONCLUSIONS

Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.