A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks

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.

Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review

To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.

A Systematic Review and Meta-Analysis of the Prevalence of Small Fibre Impairment in Patients with Fibromyalgia

Converging evidence shows that patients with fibromyalgia syndrome have signs of small fibre impairment, possibly leading to pain and autonomic symptoms, with a frequency that has not yet been systematically evaluated. To fill this gap, our review aims to define the frequency of somatic and autonomic small fibre damage in patients with fibromyalgia syndrome, as assessed by objective small fibre-related testing. We found 360 articles on somatic and autonomic small fibre assessment in patients with fibromyalgia. Out of the 88 articles assessed for eligibility, 20 were included in the meta-analysis, involving 903 patients with fibromyalgia. The estimated prevalence of somatic small fibre impairment, as assessed with skin biopsy, corneal confocal microscopy, and microneurography, was 49% (95% confidence interval (CI): 39–60%, I² = 89%), whereas the estimated prevalence of autonomic small fibre impairment, as assessed with heart rate variability, sympathetic skin response, skin conductance, and tilt testing, was 45% (95% CI: 25–65%, I² = 91%). Our study shows that a considerable proportion of patients with fibromyalgia have somatic and autonomic small fibre impairment, as assessed by extensive small fibre-related testing. Nevertheless, the heterogeneity and inconsistencies across studies challenge the exact role of small fibre impairment in fibromyalgia symptoms.

Neurophysiological and ultrasonographic comparative study of autonomous nervous system in patients suffering from fibromyalgia and generalized anxiety disorder

BACKGROUND

Fibromyalgia (FM) and generalized anxiety disorder (GAD) share common clinical features: they both affect women more than men, their diagnosis is based solely on clinical criteria, and some of the symptoms such as anxiety, aches and muscle tension, sleep disorders, and cognitive dysfunction occur in both diseases. For both conditions, an underlying dysregulation of the autonomic nervous system (ANS) has been proposed.

OBJECTIVE

The aims of this study were to investigate ANS dysfunction in FM and GAD and compare them with controls.

METHODS

Sympathetic skin response (SSR) from palm and sole and cross-sectional area (CSA) of bilateral vagus nerves (VN) were measured in 28 healthy controls, 21 FM patients, and 24 GAD patients.

RESULTS

CSA of VN was significantly smaller in FM patients (right: 1.97 ± 0.74mm², left: 1.75 ± 0.65 mm2) and GAD patients (right: 2.12 ± 0.97mm², left: 1.71 ± 0.86 mm²) compared to controls (right: 3.21 ± 0.75 mm², left: 2.65 ± 1.13 mm², p < 0.001, but did not differ between the two patient groups. SSR parameters were similar between patients and controls. SSR latency correlated to clinical scales (FM Widespread Pain Index) in the FM group (r = 0.515, p = 0.02 and r = 0.447, p = 0.05) for the upper and lower limbs respectively, but no other correlation between clinical and neurophysiological parameters was identified.

CONCLUSION

This study confirms similar ANS abnormalities in FM and GAD that fairly distinguish them from controls and support the hypothesis of a common pathophysiological substrate underlying both conditions.

Mathematical and computational models for pain: a systematic review

OBJECTIVE

There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain.

METHODS

The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant.

RESULTS

Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment.

CONCLUSIONS

Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.

Pain Processing and Vegetative Dysfunction in Fibromyalgia: A Study by Sympathetic Skin Response and Laser Evoked Potentials

BACKGROUND

A dysfunction of pain processing at central and peripheral levels was reported in fibromyalgia (FM). We aimed to correlate laser evoked potentials (LEPs), Sympathetic Skin Response (SSR), and clinical features in FM patients.

METHODS

Fifty FM patients and 30 age-matched controls underwent LEPs and SSR by the right hand and foot. The clinical evaluation included FM disability (FIQ) and severity scores (WPI), anxiety (SAS) and depression (SDS) scales, and questionnaires for neuropathic pain (DN4).

RESULTS

The LEP P2 latency and amplitude and the SSR latency were increased in FM group. This latter feature was more evident in anxious patients. The LEPs habituation was reduced in FM patients and correlated to pain severity scores. In a significant number of patients (32%) with higher DN4 and FIQ scores, SSR or LEP responses were absent.

CONCLUSIONS

LEPs and SSR might contribute to clarifying the peripheral and central nervous system involvement in FM patients.

Ion channels expression and function are strongly modified in solid tumors and vascular malformations

Background

Several cellular functions relate to ion-channels activity. Physiologically relevant chains of events leading to angiogenesis, cell cycle and different forms of cell death, require transmembrane voltage control. We hypothesized that the unordered angiogenesis occurring in solid cancers and vascular malformations might associate, at least in part, to ion-transport alteration.

Methods

The expression level of several ion-channels was analyzed in human solid tumor biopsies. Expression of 90 genes coding for ion-channels related proteins was investigated within the Oncomine database, in 25 independent patients-datasets referring to five histologically-different solid tumors (namely, bladder cancer, glioblastoma, melanoma, breast invasive-ductal cancer, lung carcinoma), in a total of 3673 patients (674 control-samples and 2999 cancer-samples). Furthermore, the ion-channel activity was directly assessed by measuring in vivo the electrical sympathetic skin responses (SSR) on the skin of 14 patients affected by the flat port-wine stains vascular malformation, i.e., a non-tumor vascular malformation clinical model.

Results

Several ion-channels showed significantly increased expression in tumors (p < 0.0005); nine genes (namely, CACNA1D, FXYD3, FXYD5, HTR3A, KCNE3, KCNE4, KCNN4, CLIC1, TRPM3) showed such significant modification in at least half of datasets investigated for each cancer type. Moreover, in vivo analyses in flat port-wine stains patients showed a significantly reduced SSR in the affected skin as compared to the contralateral healthy skin (p < 0.05), in both latency and amplitude measurements.

Conclusions

All together these data identify ion-channel genes showing significantly modified expression in different tumors and cancer-vessels, and indicate a relevant electrophysiological alteration in human vascular malformations. Such data suggest a possible role and a potential diagnostic application of the ion–electron transport in vascular disorders underlying tumor neo-angiogenesis and vascular malformations.