Tag: artificial intelligence

Use of artificial intelligence and machine learning for the management of fibromyalgia: a scoping review

Abstract:

Background: Fibromyalgia (FM) is a complex and multifactorial syndrome characterized by widespread pain, fatigue, cognitive impairment, and other systemic symptoms. The absence of specific biomarkers and the heterogeneous clinical presentation pose significant diagnostic challenges.

Objective: This scoping review aims to explore the current applications of artificial intelligence (AI) and machine learning (ML) in the diagnosis and clinical management of FM.

Methods: A systematic search was conducted in PubMed, EMBASE, and the Cochrane Library using defined keywords related to FM and AI/ML. Studies were included if they addressed ML applications in FM patients. Following PRISMA-ScR guidelines, 43 studies published between 2011 and 2024 were included and analyzed for ML techniques used, diagnostic targets, data types, and clinical relevance.

Results: As expected, the majority of studies done so far focused on improving diagnostic accuracy through supervised algorithms such as support vector machines, neural networks, and ensemble models, as well as unsupervised clustering and dimensionality reduction techniques. Notable findings include the identification of neurophysiological signatures via fMRI, gene expression patterns, retinal imaging changes, and metabolomic biomarkers that distinguish FM patients from controls. For instance, one study investigating circulating microRNAs used a Random Forest model to identify 11 microRNAs (e.g. hsa-miR-28-5p, hsa-miR-29a-3p, hsa-miR-150-5p) capable of differentiating patients with FM, ME/CFS, and healthy controls, suggesting their potential as biomarkers for more accurate diagnoses. Reported model accuracies ranged from 82% to 100%, although most studies were pilot-based with small and imbalanced samples, limiting generalizability.

Conclusion: AI and ML offer promising tools to overcome longstanding limitations in FM diagnosis and treatment. While current findings demonstrate significant potential, larger, multicenter studies with rigorous validation protocols are essential to finally establish these approaches as clinically reliable solutions.

Source: Clempi Almeida E Silva AL, Reis VHPF, Lamoglia ASA, Souza Desidério C, Freire Oliveira CJ. Use of artificial intelligence and machine learning for the management of fibromyalgia: a scoping review. J Man Manip Ther. 2026 Feb 17:1-17. doi: 10.1080/10669817.2026.2630999. Epub ahead of print. PMID: 41700030. https://pubmed.ncbi.nlm.nih.gov/41700030/

Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements

Abstract:

Background: An artificial intelligence (AI) pipeline was used to differentiate patients suffering from Chronic Fatigue Syndrome (CFS) from healthy controls (HC) based on high-frequency, large-scale data obtained using beat-to-beat measurement of the autonomic nervous system (ANS) and cardiovascular function.

Methods: This prospective, case-control study included a cohort of 112 CFS patients and 61 HCs examined. Heart rate (HR), high-frequency R-to-R interval (HF RRI), diastolic blood pressure (dBP), stroke volume (SV), and SV index (SV/FFM) were measured using the Task Force Monitor. A novel sequential learning approach was applied: first, a Transformer model was trained, followed by an XGBoost classifier that learned from the errors of the Transformer. Matthews correlation coefficient (MCC), accuracy, and Area Under the Receiver Operating Characteristic Curve (ROC AUC) were assessed. Model classifications were explained globally.

Results: The applied classifier achieved a subject-level accuracy of 0.89, an MCC of 0.79, and an AUC of 1.00. Lower values of beat-to-beat difference in HR and raw HF RRI (indicating reduced cardiac vagal tone) and higher values of dBP difference (more beat-to-beat increases, indicating higher sympathetic vascular tone) were related to being more likely classified as CFS patients. Low values of SV difference and low values of SV/FFM (both indicating less effective cardiac hemodynamics) were related to being more likely classified as CFS patients.

Conclusions: The AI-driven classifier demonstrates remarkable proficiency in distinguishing between patients with CFS and HC. By leveraging this automated pipeline, beat-to-beat measurements of the ANS can significantly enhance the objective assessment of CFS diagnosis.

Source: Kujawski S, Tabisz H, Morten KJ, Modlińska A, Słomko J, Zalewski P. Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements. J Transl Med. 2025 Dec 23;23(1):1413. doi: 10.1186/s12967-025-07433-y. PMID: 41437251; PMCID: PMC12729017. https://pmc.ncbi.nlm.nih.gov/articles/PMC12729017/ (Full text)

Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence

Abstract:

We studied a generalized question: chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-omics dataset for ME/CFS to date.

This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-omics to a symptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification.

We also created the first connectivity map of these omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.

Source: Xiong R. Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence. ArXiv [Preprint]. 2025 Jun 18:arXiv:2506.15761v1. PMID: 40980765; PMCID: PMC12447721. https://pmc.ncbi.nlm.nih.gov/articles/PMC12447721/ (Full text available as PDF file)

AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data and detailed clinical symptoms.

By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct a unique connectivity map spanning the microbiome, immune system and plasma metabolome in health and ME/CFS adjusted for age, gender and additional clinical factors.

This map uncovers altered associations between microbial metabolism (for example, short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFN-γ and GzA.

Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing unique mechanisms—specifically, how multi-omics dynamics are associated to the disease’s heterogeneous symptoms.

Source: Xiong, R., Aiken, E., Caldwell, R. et al. AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome. Nat Med (2025). https://doi.org/10.1038/s41591-025-03788-3  https://www.nature.com/articles/s41591-025-03788-3

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

Abstract:

Chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-‘omics dataset for ME/CFS to date.

This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-‘omics to asymptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these ‘omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.

Thus, we proposed several innovative mechanistic hypotheses for ME/CFS: Disrupted microbial functions – SCFA (butyrate), BCAA (amino acid), tryptophan, benzoate – lost connection with plasma lipids and bile acids, and activated inflammatory and mucosal immune cells (MAIT, γδT cells) with INFγ and GzA secretion. These abnormal dynamics are linked to key disease symptoms, including gastrointestinal issues, fatigue, and sleep problems.

Source: Xiong R, Fleming E, Caldwell R, Vernon SD, Kozhaya L, Gunter C, Bateman L, Unutmaz D, Oh J. BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome. bioRxiv [Preprint]. 2024 Jun 28:2024.06.24.600378. doi: 10.1101/2024.06.24.600378. PMID: 38979186; PMCID: PMC11230215. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11230215/ (Full text available as PDF file)

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Abstract:

Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression.

The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education.

The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69-0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment.

Source: Lacasa M, Prados F, Alegre J, Casas-Roma J. A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires. Sci Rep. 2023 Aug 31;13(1):14256. doi: 10.1038/s41598-023-40364-6. PMID: 37652910; PMCID: PMC10471690. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10471690/ (Full text)

A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with a significant global prevalence of over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. A multidisciplinary approach is essential for diagnosing, treating, and managing this complex disease.

The current study aims at employing explainable artificial intelligence (XAI) and machine learning (ML) techniques to identify discriminative metabolites for ME/CFS.

Material and Methods: The present study used a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.

Random forest-based feature selection and Bayesian Approach based-hyperparameter optimization were implemented on the target data. Four different ML algorithms [Gaussian Naive Bayes (GNB), Gradient Boosting Classifier (GBC), Logistic regression (LR) and Random Forest Classifier (RFC)] were used to classify individuals as ME/CFS patients and healthy individuals. XAI approaches were applied to clinically explain the prediction decisions of the optimum model. Performance evaluation was performed using the indices of accuracy, precision, recall, F1 score, Brier score, and AUC.

Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.

The RFC learning model outperformed GNB, GBC, and LR in ME/CFS prediction using the 1000 iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC.

Conclusion: RFC model proposed in this study correctly classified and evaluated ME/CFS patients through the selected biomarker candidate metabolites. The methodology combining ML and XAI can provide a clear interpretation of risk estimation for ME/CFS, helping physicians intuitively understand the impact of key metabolomics features in the model.

Source: Yagin, F.H., Alkhateeb, A., Raza, A., Samee, N.A., Mahmoud, N.F., Colak, C., & Yagin, B. (2023). A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Preprints. https://doi.org/10.20944/preprints202307.1585.v1 https://www.preprints.org/manuscript/202307.1585/v1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706650/ (Full text of completed study)

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

Abstract:

SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”. Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes.

In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.

Source: Cau R, Faa G, Nardi V, Balestrieri A, Puig J, Suri JS, SanFilippo R, Saba L. Long-COVID diagnosis: From diagnostic to advanced AI-driven models. Eur J Radiol. 2022 Jan 19;148:110164. doi: 10.1016/j.ejrad.2022.110164. Epub ahead of print. PMID: 35114535; PMCID: PMC8791239. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791239/ (Full text)