An Artificial Neural Network Approach for Predicting Functional Outcome in Fibromyalgia Syndrome after Multidisciplinary Pain Program

Moving towards the use of artificial intelligence in pain management

BACKGROUND AND OBJECTIVE

While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

DATABASES AND DATA TREATMENT

This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.

RESULTS

From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.

CONCLUSION

There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.

SIGNIFICANCE

This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Machine learning methods in physical therapy: A scoping review of applications in clinical context

BACKGROUND

Machine learning (ML) efficiently processes large datasets, showing promise in enhancing clinical practice within physical therapy.

OBJECTIVE

The aim of this scoping review is to provide an overview of studies using ML approaches in clinical settings of physical therapy.

DATA SOURCES

A scoping review was performed in PubMed, EMBASE, PEDro, Cochrane, Web of Science, and Scopus.

SELECTION CRITERIA

We included studies utilizing ML methods. ML was defined as the utilization of computational systems to encode patterns and relationships, enabling predictions or classifications with minimal human interference.

DATA EXTRACTION AND DATA SYNTHESIS

Data were extracted regarding methods, data types, performance metrics, and model availability.

RESULTS

Forty-two studies were included. The majority were published after 2020 (n = 25). Fourteen studies (33.3%) were in the musculoskeletal physical therapy field, nine (21.4%) in neurological, and eight (19%) in sports physical therapy. We identified 44 different ML models, with random forest being the most used. Three studies reported on model availability. We identified several clinical applications for ML-based tools, including diagnosis (n = 14), prognosis (n = 7), treatment outcomes prediction (n = 7), clinical decision support (n = 5), movement analysis (n = 4), patient monitoring (n = 3), and personalized care plan (n = 2).

LIMITATION

Model performance metrics, costs, model interpretability, and explainability were not reported.

CONCLUSION

This scope review mapped the emerging landscape of machine learning applications in physical therapy. Despite the growing interest, the field still lacks high-quality studies on validation, model availability, and acceptability to advance from research to clinical practice.

Artificial neural networks in neurorehabilitation: A scoping review

BACKGROUND

Advances in medical technology produce highly complex datasets in neurorehabilitation clinics and research laboratories. Artificial neural networks (ANNs) have been utilized to analyze big and complex datasets in various fields, but the use of ANNs in neurorehabilitation is limited.

OBJECTIVE

To explore the current use of ANNs in neurorehabilitation.

METHODS

PubMed, CINAHL, and Web of Science were used for the literature search. Studies in the scoping review (1) utilized ANNs, (2) examined populations with neurological conditions, and (3) focused on rehabilitation outcomes. The initial search identified 1,136 articles. A total of 19 articles were included.

RESULTS

ANNs were used for prediction of functional outcomes and mortality (n = 11) and classification of motor symptoms and cognitive status (n = 8). Most ANN-based models outperformed regression or other machine learning models (n = 11) and showed accurate performance (n = 6; no comparison with other models) in predicting clinical outcomes and accurately classifying different neurological impairments.

CONCLUSIONS

This scoping review provides encouraging evidence to use ANNs for clinical decision-making of complex datasets in neurorehabilitation. However, more research is needed to establish the clinical utility of ANNs in diagnosing, monitoring, and rehabilitation of individuals with neurological conditions.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Occupational Therapy's Unique Contribution to Chronic Pain Management: A Scoping Review

Occupational therapy (OT) makes a unique contribution to chronic pain (CP) management due to its overarching focus on occupation. The aim of this scoping review was to describe current knowledge about this contribution by documenting OT roles, models, assessments, and intervention methods used with adults living with CP. A systematic search exploring 10 databases and gray literature from 2006 to 2017 was conducted. Fifty-two sources were retained and analysed. Results bring forward the main role of OT being improving activities and participation (76.9 %), the Canadian Model of Occupational Performance (9.6 %), and the Canadian Occupational Performance Measure (21.2 %). Within the 30 reported interventions, 73.3% related directly to the person, 20% pertained to occupation (activities and participation), and 6.7% addressed environmental factors. The distinction and complementarity between the bottom-up and the top-down approaches to OT intervention were discussed. This review highlights OT specificity in adult CP management.

Differences in Multidisciplinary and Interdisciplinary Treatment Programs for Fibromyalgia: A Mapping Review

Fibromyalgia is a multifaceted chronic pain syndrome and the integration of different health disciplines is strongly recommended for its care. The interventions based on this principle are very heterogeneous and the difference across their structures has not been extensively studied, leading to incorrect conclusions when their outcomes are pooled. The objective of this mapping review was to summarize the characteristics of these programs, with particular focus on the integration of their components. We performed a search of the literature about treatments for fibromyalgia involving multiple disciplines on PubMed and Scopus. Starting from 560 articles, we included 22 noncontrolled studies, 10 controlled studies, and 17 RCTs evaluating the effects of 38 multidisciplinary or interdisciplinary interventions. The average quality of the studies was low. Their outcomes were usually pain intensity, quality of life, and psychological variables. We created a map of the programs based on the degree of integration of the included disciplines, which ranged from a juxtaposition of few components to a complex harmonization of different perspectives obtained through teamwork strategies. The rehabilitation programs were then thoroughly described with regard to the duration, setting, therapeutic components, and professionals included. The implications for future quantitative reviews are discussed.