Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

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.

Multimodal hypersensitivity derived from quantitative sensory testing predicts pelvic pain outcome: an observational cohort study

ABSTRACT

Multimodal hypersensitivity (MMH)-greater sensitivity across multiple sensory modalities (eg, light, sound, temperature, pressure)-is associated with the development of chronic pain. However, previous MMH studies are restricted given their reliance on self-reported questionnaires, narrow use of multimodal sensory testing, or limited follow-up. We conducted multimodal sensory testing on an observational cohort of 200 reproductive-aged women, including those at elevated risk for chronic pelvic pain conditions and pain-free controls. Multimodal sensory testing included visual, auditory, and bodily pressure, pelvic pressure, thermal, and bladder pain testing. Self-reported pelvic pain was examined over 4 years. A principal component analysis of sensory testing measures resulted in 3 orthogonal factors that explained 43% of the variance: MMH, pressure pain stimulus response, and bladder hypersensitivity. The MMH and bladder hypersensitivity factors correlated with baseline self-reported menstrual pain, genitourinary symptoms, depression, anxiety, and health. Over time, MMH increasingly predicted pelvic pain and was the only component to predict outcome 4 years later, even when adjusted for baseline pelvic pain. Multimodal hypersensitivity was a better predictor of pelvic pain outcome than a questionnaire-based assessment of generalized sensory sensitivity. These results suggest that MMHs overarching neural mechanisms convey more substantial long-term risk for pelvic pain than variation in individual sensory modalities. Further research on the modifiability of MMH could inform future treatment developments in chronic pain.

The Cross-Sectional Area Assessment of Pelvic Muscles Using the MRI Manual Segmentation among Patients with Low Back Pain and Healthy Subjects

The pain pathomechanism of chronic low back pain (LBP) is complex and the available diagnostic methods are insufficient. Patients present morphological changes in volume and cross-sectional area (CSA) of lumbosacral region. The main objective of this study was to assess if CSA measurements of pelvic muscle will indicate muscle atrophy between asymptomatic and symptomatic sides in chronic LBP patients, as well as between right and left sides in healthy volunteers. In addition, inter-rater reliability for CSA measurements was examined. The study involved 71 chronic LBP patients and 29 healthy volunteers. The CSA of gluteus maximus, medius, minimus and piriformis were measured using the MRI manual segmentation method. Muscle atrophy was confirmed in gluteus maximus, gluteus minimus and piriformis muscle for over 50% of chronic LBP patients (p < 0.05). Gluteus medius showed atrophy in patients with left side pain occurrence (p < 0.001). Muscle atrophy occurred on the symptomatic side for all inspected muscles, except gluteus maximus in rater one assessment. The reliability of CSA measurements between raters calculated using CCC and ICC presented great inter-rater reproducibility for each muscle both in patients and healthy volunteers (p < 0.95). Therefore, there is the possibility of using CSA assessment in the diagnosis of patients with symptoms of chronic LBP.

Unraveling schizophrenia replicable functional connectivity disruption patterns across sites

Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal-based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site-effect correction methods in removing the site-related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site-related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo-connectivity within sensory areas (Net1), hypo-connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper-connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set (p < .05). Finally, the three subnet-specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out-of-sample public data set (Z = -1.583, p = .114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet-specific composite FC measures might be replicable neuroimaging markers for schizophrenia.

Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia

BACKGROUND AND HYPOTHESIS

Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis.

STUDY DESIGN

A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure.

STUDY RESULTS

The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P > 0.82) while outperformed the single-site models (Delong test, P < 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients' positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable).

CONCLUSIONS

In summary, the proposed MICI biomarker may provide a simple and explainable way to support clinicians for objectively diagnosing schizophrenia. Finally, we developed an online model share platform to promote biomarker generalization and provide free individual prediction services (http://micc.tmu.edu.cn/mici/index.html).

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.