Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective
Abstract
:1. Introduction
AI-Guided Transcranial Direct Current Stimulation for Long COVID—A New Frontier in Neuromodulation
- Personalization: Given the diverse manifestations of Long COVID, AI can help identify optimal tDCS protocols for individual patients based on their unique symptom profiles and physiological characteristics.
- Real-time adaptation: AI algorithms can potentially adjust tDCS parameters in real time based on patient responses, allowing for dynamic, responsive treatment.
- Predictive modeling: By analyzing large datasets, AI can help predict which patients are most likely to benefit from tDCS, optimizing resource allocation and improving patient outcomes.
- Objective outcome measures: AI can assist in developing more sensitive and objective measures of treatment efficacy, crucial in a condition as complex and subjective as Long COVID.
- Clinical trial optimization: AI-enhanced adaptive trial designs can potentially accelerate the process of identifying effective tDCS protocols for Long COVID.
- The promise of AI in addressing Long COVID challenges for tDCS.
- AI-enhanced clinical trial design for tDCS in Long COVID.
- Ethical considerations and challenges.
- Future research directions.
- Global implementation of AI-guided tDCS for Long COVID.
2. Definition and Pathophysiology of Long COVID Syndrome Definition of Long COVID
- Fatigue.
- Shortness of breath.
- Cognitive dysfunction (“brain fog”).
- Sleep disturbances.
- Persistent cough.
- Chest pain.
- Heart palpitations.
- Joint and muscle pain.
- Loss of taste or smell.
- Depression and anxiety.
Pathophysiological Origins of Long COVID
- Persistent Inflammation: Evidence suggests ongoing systemic inflammation plays a crucial role in Long COVID. Elevated levels of pro-inflammatory cytokines and chemokines have been observed in Long COVID patients, indicating a dysregulated immune response [15].
- Endothelial Dysfunction: SARS-CoV-2 can directly infect endothelial cells, leading to widespread endothelial dysfunction. This can result in microvascular damage and contribute to various symptoms, including cognitive impairment and fatigue [16].
- Autoimmunity: Some studies have found evidence of autoantibodies in Long COVID patients, suggesting that autoimmune processes may contribute to ongoing symptoms [17].
- Viral Persistence: There is evidence that SARS-CoV-2 viral reservoirs may persist in some patients, potentially driving ongoing inflammation and symptoms [18].
- Mitochondrial Dysfunction: Impaired mitochondrial function has been observed in Long COVID patients, which could contribute to fatigue and exercise intolerance [18].
- Autonomic Nervous System Dysregulation: Many Long COVID symptoms, such as tachycardia and orthostatic intolerance, may be attributed to dysfunction of the autonomic nervous system [19].
- Microclots and Coagulation Abnormalities: Persistent microclots and hypercoagulability have been observed in some Long COVID patients, potentially contributing to tissue hypoxia and various symptoms [20].
- Neuroinflammation: Brain imaging studies have shown evidence of neuroinflammation in Long COVID patients, which may underlie cognitive symptoms and fatigue [21].
3. Non-Invasive Brain Stimulation (NIBS) Techniques
- Transcranial Magnetic Stimulation (TMS).
- Transcranial Direct Current Stimulation (tDCS).
- Transcranial Alternating Current Stimulation (tACS).
- Transcranial Random Noise Stimulation (tRNS).
3.1. Transcranial Direct Current Stimulation (tDCS)
3.2. Methodology
3.3. Limitations and Challenges
3.4. Relevance to tDCS and AI Applications
4. The Promise of AI in Addressing Long COVID Challenges for tDCS
4.1. AI-Driven Personalization and Real-Time Optimization of tDCS for Long COVID
4.1.1. Predictive Modeling for Responder Identification, Considering Long COVID-Specific Factors
4.1.2. Real-Time Adjustment of Stimulation Parameters Based on Symptom Fluctuations
5. AI-Enhanced Clinical Trial Design for tDCS in Long COVID
- Adaptive trial designs: This stage incorporates AI-driven approaches to make trials more flexible and responsive to emerging data. It includes dynamic treatment regimens, Bayesian adaptive designs, and adaptive enrichment designs.
- Patient stratification: This stage uses AI techniques to identify more homogeneous subgroups within the heterogeneous Long COVID population. It includes unsupervised learning for subgroup discovery, supervised learning for subgroup prediction, and multi-modal data integration.
- Objective outcome measure development: This final stage leverages AI to develop more sensitive and objective measures of treatment efficacy. It includes wearable device data analysis, natural language processing for symptom quantification, computer vision for facial analysis, and multivariate pattern analysis of neuroimaging data.
5.1. Adaptive Trial Designs Using Machine Learning
Incorporating Long COVID Symptom Fluctuations into Trial Protocols
- Efficiency: By adapting to emerging data, these designs can potentially reach conclusions faster than traditional fixed designs.
- Ethical considerations: Adaptive designs can potentially expose fewer patients to ineffective treatments by adjusting allocation based on accumulating evidence.
- Personalization: These approaches align well with the goal of personalized medicine, allowing for the identification of patient subgroups most likely to benefit from tDCS.
- Flexibility: Adaptive designs can accommodate the uncertainty inherent in studying a novel condition like Long COVID, allowing for adjustments as our understanding of the disease evolves.
5.2. Patient Stratification and Targeted Recruitment
Identifying Homogeneous Subgroups within the Heterogeneous Long COVID Population
- Improved trial efficiency: By identifying more homogeneous patient subgroups, these methods could reduce noise in treatment effect estimates, potentially allowing for smaller sample sizes.
- Personalized treatment strategies: Stratification could guide the development of tailored tDCS protocols for different patient subgroups.
- Mechanistic insights: Identifying distinct patient subgroups could provide insights into the underlying mechanisms of both Long COVID and tDCS effects.
- Improved patient selection: AI-driven stratification could help identify the patients most likely to benefit from tDCS, improving the risk–benefit ratio for trial participants.
5.3. Objective Outcome Measure Development Using AI
Developing Sensitive Measures to Capture Subtle Improvements in Long COVID Symptoms
- Objectivity: AI-based measures can provide more objective assessments of symptoms that are typically subjective, potentially reducing bias and improving reliability.
- Sensitivity: AI techniques can potentially detect subtle changes in patient status that might be missed by traditional clinical assessments.
- Continuous monitoring: Many of these approaches allow for continuous or frequent assessment, providing a more detailed picture of symptom trajectories.
- Multi-dimensional assessment: AI can integrate data from multiple sources, providing a more comprehensive view of patient status.
- Scalability: Once developed, many AI-based assessments can be deployed at scale, potentially allowing for larger and more efficient trials.
6. Ethical Considerations and Challenges in AI-Guided tDCS for Long COVID Treatment
6.1. Data Privacy and Security
6.2. Equitable Access and Algorithmic Bias
6.3. Transparency and Explainability
6.4. Clinical Validation and Regulatory Frameworks
6.5. Long COVID-Specific Considerations
6.6. AI Governance and the EU AI Act
6.7. Conclusions
7. Future Research Directions
7.1. Optimal tDCS Parameters for Long COVID
7.2. AI-Driven Personalization of tDCS
7.3. Long-Term Efficacy and Safety
7.4. Integration with Other Treatments
7.5. Neuroplasticity and Mechanism of Action
7.6. Combining tDCS with Neuroimaging
7.7. Patient-Reported Outcomes and Quality of Life
7.8. Home-Based AI-Guided tDCS
7.9. Health Economic Analysis
7.10. Ethical and Regulatory Frameworks
7.11. Generalizability and Health Disparities
7.12. Conclusions and Synthesis
- Personalization: Many of the proposed research directions focus on leveraging AI to tailor tDCS treatment to individual patients, reflecting the heterogeneous nature of Long COVID.
- Integration: There is a clear need to integrate AI-guided tDCS with other treatment modalities and to consider its implementation within broader healthcare systems.
- Long-term perspectives: Given the chronic nature of Long COVID, understanding the long-term effects and safety of AI-guided tDCS is crucial.
- Ethical considerations: Ethical issues are woven throughout the research agenda, reflecting the need to balance innovation with patient safety and equitable access to care.
- Real-world implementation: Many of the proposed studies focus on translating AI-guided tDCS from controlled research settings to real-world clinical practice and home-based applications.
8. Current Empirical Evidence for AI-Guided tDCS in Long COVID Treatment
8.1. tDCS in Post-Viral Fatigue Syndromes
8.2. AI in tDCS Optimization
8.3. AI in Long COVID Research
8.4. Gaps and Future Directions
- Direct empirical evidence of AI-guided tDCS efficacy in Long COVID is lacking.
- Long-term effects and safety profiles of AI-optimized tDCS protocols need investigation.
- The impact of AI-guided tDCS on diverse Long COVID symptoms beyond fatigue requires study.
- Large-scale, randomized controlled trials are needed to validate the proposed approaches.
- Conduct pilot studies specifically testing AI-guided tDCS protocols in Long COVID patients.
- Develop and validate AI models for predicting individual responses to tDCS in Long COVID.
- Perform longitudinal studies to assess long-term efficacy and safety.
- Investigate the potential of AI-guided tDCS for various Long COVID symptoms beyond fatigue.
9. Global Implementation of AI-Guided tDCS for Long COVID
9.1. High-Income Countries (HICs)
9.1.1. Integration with Existing Infrastructure
9.1.2. Regulatory Pathways
9.1.3. Reimbursement Models
9.2. Middle-Income Countries (MICs)
9.2.1. Adaptation of AI Models
9.2.2. Capacity Building
9.2.3. Public-Private Partnerships
9.3. Low-Income Countries (LICs)
9.3.1. Simplified AI Models
9.3.2. Mobile Health (mHealth) Integration
9.3.3. Task-Shifting Strategies
9.4. Cross-Cutting Considerations
9.4.1. Affordability and Accessibility
9.4.2. Cultural Adaptation
9.4.3. Data Privacy and Security
9.4.4. Sustainable Implementation
9.4.5. Global Collaboration and Knowledge Sharing
9.5. Challenges and Future Directions
9.5.1. Digital Divide
9.5.2. Workforce Development
9.5.3. Ethical Considerations
9.5.4. Long-Term Sustainability
Limitations and Risks of AI in tDCS for Long COVID Treatment
Current Limitations of AI in Healthcare
- Data Quality and Quantity: AI models are only as good as the data they are trained on. In the context of Long COVID, which is a relatively new condition, there may be insufficient high-quality data available for training robust AI models [54].
- Generalizability: AI models developed using data from specific populations may not generalize well to other groups, potentially leading to biased or inaccurate recommendations for certain patients [93].
- Interpretability: Many advanced AI models, particularly deep learning models, operate as “black boxes”, making it difficult to understand how they arrive at their decisions. This lack of interpretability can be problematic in healthcare settings where clinicians need to understand and explain treatment decisions [54].
- Adaptability to New Information: While AI can process large amounts of data, it may struggle to quickly adapt to new information or changing circumstances, which is particularly relevant in the rapidly evolving field of Long COVID research [94].
Potential Risks and Challenges
- Overreliance on AI: There is a risk that clinicians might overly rely on AI recommendations, potentially overlooking important clinical judgments that fall outside AI’s parameters [95].
- Privacy and Security Concerns: The use of AI in healthcare involves processing large amounts of sensitive patient data, raising concerns about data privacy and security [96].
- Regulatory Hurdles: The rapidly evolving nature of AI technology presents challenges for regulatory bodies in ensuring safety and efficacy while not stifling innovation [97].
- Ethical Considerations: The use of AI in clinical decision-making raises ethical questions about accountability, consent, and the potential for AI to exacerbate existing healthcare disparities [11].
- Integration with Existing Clinical Workflows: Implementing AI-guided tDCS systems may require significant changes to existing clinical practices, which can be met with resistance and may introduce new sources of error if not carefully managed [70].
Balancing Potential with Realism
- AI cannot replace clinical expertise: AI should be viewed as a tool to augment clinical decision-making, not replace it. The complex and often unpredictable nature of Long COVID requires the nuanced understanding that experienced clinicians provide [77].
- Continuous evaluation is necessary: As AI systems are implemented, their performance should be continuously monitored and evaluated against standard care to ensure they are truly improving patient outcomes [98].
- Transparent communication is crucial: Patients should be clearly informed about the role of AI in their treatment, including its limitations and the extent of its influence on clinical decisions [99].
- Multidisciplinary collaboration is key: The successful implementation of AI-guided tDCS will require close collaboration between clinicians, AI experts, ethicists, and regulatory bodies to navigate the complex landscape of clinical AI [100].
Practical Implementation Steps for AI-Guided tDCS in Clinical Settings
- 1.
- Infrastructure Setup
- (a)
- Hardware Requirements:Acquire FDA-approved tDCS devices capable of remote parameter adjustment.Ensure secure, high-speed internet connectivity in clinical areas.Set up secure servers for data storage and AI model hosting.
- (b)
- Software Development:Develop user-friendly interfaces for clinicians and patients.Create secure data pipelines for real-time data collection and processing.Implement AI models with appropriate security measures.
- (c)
- Integration with Existing Systems:Establish interoperability with Electronic Health Record (EHR) systems.Ensure compatibility with existing clinical workflow management tools.
- 2.
- AI Model Development and Validation
- (a)
- Data Collection:Establish protocols for standardized data collection across multiple sites.Implement privacy-preserving techniques such as federated learning [6].
- (b)
- Model Development:Develop and train AI models using historical tDCS data and Long COVID patient information.Employ techniques like transfer learning to adapt existing models to Long COVID specifics.
- (c)
- Clinical Validation:Conduct pilot studies to validate AI model predictions against expert clinician decisions.Perform iterative refinement based on clinical feedback and outcomes.
- 3.
- Clinical Protocol Development
- (a)
- Treatment Guidelines:Develop clear guidelines for patient selection and exclusion criteria.Establish protocols for baseline assessments and ongoing monitoring.
- (b)
- Safety Protocols:Implement real-time safety monitoring systems.Develop clear procedures for managing adverse events.
- (c)
- Clinician Training:Create comprehensive training programs for clinicians on AI-guided tDCS use.Develop resources for ongoing support and troubleshooting.
- 4.
- Regulatory Compliance and Approval
- (a)
- FDA Approval:Engage with the FDA early to understand requirements for AI-guided medical devices [101].Conduct necessary clinical trials to demonstrate safety and efficacy.
- (b)
- Data Protection:Ensure compliance with HIPAA and other relevant data protection regulations.Implement robust data anonymization and encryption protocols.
- (c)
- Ethical Review: Obtain approval from institutional review boards (IRBs) for clinical implementationAddress ethical considerations related to AI decision-making in healthcare.
- 5.
- Clinical Integration
- (a)
- Pilot Implementation:Start with a small-scale pilot in select clinics.Monitor closely and gather feedback from clinicians and patients.
- (b)
- Workflow Integration:Develop standard operating procedures (SOPs) for integrating AI-guided tDCS into daily clinical practice.Create decision support tools to assist clinicians in interpreting AI recommendations.
- (c)
- Quality Assurance:Implement continuous monitoring systems to track treatment outcomes.Establish regular review processes to assess and improve system performance.
- 6.
- Scaling and Maintenance
- (a)
- Gradual Expansion:Develop a phased roll-out plan to expand to more clinical sites.Provide adequate support and resources for each expansion phase.
- (b)
- Ongoing Model Updates:Establish protocols for regular model updates based on new data.Implement a system for version control and tracking of AI model changes.
- (c)
- Long-term Monitoring:Set up systems for long-term efficacy and safety monitoring.Establish channels for ongoing feedback from clinicians and patients.
- 7.
- Education and Communication
- (a)
- Patient Education:Develop materials to educate patients about AI-guided tDCS.Create informed consent processes that clearly explain the role of AI in treatment.
- (b)
- Public Engagement:Engage in transparent communication about the use of AI in healthcare.Address potential concerns and misconceptions about AI-guided treatments.
- (c)
- Professional Development:Organize workshops and conferences for knowledge sharing among clinicians.Collaborate with medical schools to incorporate AI-guided neuromodulation into curricula.
10. Conclusions: The Future of AI-Guided tDCS for Long COVID
Author Contributions
Funding
Conflicts of Interest
References
- Davies, H.E.; McCorkell, L.; Vogel, J.M.; Topol, E.J. Long COVID: Major findings, mechanisms and recommendations. Nat. Rev. Microbiol. 2023, 21, 133–146. [Google Scholar] [CrossRef]
- Torrente, A.; Maccora, S.; Prinzi, F.; Alonge, P.; Pilati, L.; Lupica, A.; Di Stefano, V.; Camarda, C.; Vitabile, S.; Brighina, F. The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci. 2024, 14, 85. [Google Scholar] [CrossRef] [PubMed]
- Nitsche, M.A.; Paulus, W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J. Physiol. 2000, 527, 633–639. [Google Scholar] [CrossRef]
- Stagg, C.J.; Nitsche, M.A. Physiological basis of transcranial direct current stimulation. Neuroscientist 2011, 17, 37–53. [Google Scholar] [CrossRef]
- Polanía, R.; Nitsche, M.A.; Ruff, C.C. Studying and modifying brain function with non-invasive brain stimulation. Nat. Neurosci. 2018, 21, 174–187. [Google Scholar] [CrossRef]
- Tecchio, F.; Cancelli, A.; Cottone, C.; Zito, G.; Pasdqualetti, P.; Ghazaryan, A.; Rossini, P.M.; Filippi, M.M. Multiple sclerosis fatigue relief by bilateral somatosensory cortex neuromodulation. J. Neurol. 2014, 261, 1552–1558. [Google Scholar] [CrossRef]
- Meinzer, M.; Lindenberg, R.; Antonenko, D.; Flaisch, T.; Floel, A. Anodal transcranial direct current stimulation temporarily reverses age-associated cognitive decline and functional brain activity changes. J. Neurosci. 2013, 33, 12470–12478. [Google Scholar] [CrossRef]
- Brunoni, A.R.; Moffa, A.H.; Fregni, F.; Palm, U.; Padberg, F.; Blumberger, D.M.; Daskalakis, Z.J.; Bennabi, D.; Haffen, E.; Alonso, A.; et al. Transcranial direct current stimulation for acute major depressive episodes: Meta-analysis of individual patient data. Br. J. Psychiatry 2016, 208, 522–531. [Google Scholar] [CrossRef]
- Nalbandian, A.; Sehgal, K.; Gupta, A.; Madhavan, M.V.; McGroder, C.; Stevens, J.S.; Cook, J.R.; Nordvig, A.S.; Shalev, D.; Sehrawat, T.S.; et al. Post-acute COVID-19 syndrome. Nat. Med. 2021, 27, 601–615. [Google Scholar] [CrossRef]
- Wiethoff, S.; Hamada, M.; Rothwell, J.C. Variability in response to transcranial direct current stimulation of the motor cortex. Brain Stimul. 2014, 7, 468–475. [Google Scholar] [CrossRef] [PubMed]
- Char, D.S.; Shah, N.H.; Magnus, D. Implementing machine learning in health care—Addressing ethical challenges. N. Engl. J. Med. 2018, 378, 981–983. [Google Scholar] [CrossRef]
- Wahl, B.; Cossy-Gantner, A.; Germann, S.; Schwalbe, N.R. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Glob. Health 2018, 3, e000798. [Google Scholar] [CrossRef]
- World Health Organization. A Clinical Case Definition of Post COVID-19 Condition by a Delphi Consensus; WHO: Geneva, Switzerland, 2021.
- Proal, A.D.; VanElzakker, M.B. Long COVID or Post-acute Sequelae of COVID-19 (PASC): An Overview of Biological Factors That May Contribute to Persistent Symptoms. Front. Microbiol. 2021, 12, 698169. [Google Scholar] [CrossRef]
- Libby, P.; Lüscher, T. COVID-19 is, in the end, an endothelial disease. Eur. Heart J. 2020, 41, 3038–3044. [Google Scholar] [CrossRef]
- Chang, S.E.; Feng, A.; Meng, W.; Apostolidis, S.A.; Mack, E.; Artandi, M.; Barman, L.; Bennett, K.; Chakraborty, S.; Chang, I.; et al. New-onset IgG autoantibodies in hospitalized patients with COVID-19. Nat. Commun. 2021, 12, 5417. [Google Scholar] [CrossRef]
- Gaebler, C.; Wang, Z.; Lorenzi, J.C.C.; Muecksch, F.; Finkin, S.; Tokuyama, M.; Cho, A.; Jankovic, M.; Schaefer-Babajew, D.; Oliveira, T.Y.; et al. Evolution of antibody immunity to SARS-CoV-2. Nature 2021, 591, 639–644. [Google Scholar] [CrossRef]
- Paul, B.D.; Lemle, M.D.; Komaroff, A.L.; Snyder, S.H. Redox imbalance links COVID-19 and myalgic encephalomyelitis/chronic fatigue syndrome. Proc. Natl. Acad. Sci. USA 2021, 118, e2024358118. [Google Scholar] [CrossRef]
- Dani, M.; Dirksen, A.; Taraborrelli, P.; Torocastro, M.; Panagopoulos, D.; Sutton, R.; Boom Lim, P. Autonomic dysfunction in ‘long COVID’: Rationale, physiology and management strategies. Clin. Med. 2021, 21, e63–e67. [Google Scholar] [CrossRef]
- Pretorius, E.; Vlok, M.; Venter, C.; Bezuidenhout, J.A.; Laubscher, G.J.; Steenkamp, J.; Kell, D.B. Persistent clotting protein pathology in Long COVID/Post-Acute Sequelae of COVID-19 (PASC) is accompanied by increased levels of antiplasmin. Cardiovasc. Diabetol. 2021, 20, 172. [Google Scholar] [CrossRef]
- Guedj, E.; Campion, J.Y.; Dudouet, P.; Kaphan, E.; Bregeon, F.; Tissot-Dupont, H.; Guis, S.; Barthelemy, F.; Habert, P.; Ceccaldi, M.; et al. 18F-FDG brain PET hypometabolism in patients with long COVID. Eur. J. Nucl. Med. Mol. Imaging 2021, 48, 2823–2833. [Google Scholar] [CrossRef]
- Lefaucheur, J.P.; Antal, A.; Ayache, S.S.; Benninger, D.H.; Brunelin, J.; Cogiamanian, F.; Cotelli, M.; De Ridder, D.; Ferrucci, R.; Langguth, B.; et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin. Neurophysiol. 2017, 128, 56–92. [Google Scholar] [CrossRef]
- Bikson, M.; Grossman, P.; Thomas, C.; Louis Zannou, A.; Jiang, J.; Adnan, T.; Mourdoukoutas, A.P.; Kronberg, G.; Truong, D.; Boggio, B.; et al. Safety of transcranial direct current stimulation: Evidence based update 2016. Brain Stimul. 2016, 9, 641–661. [Google Scholar] [CrossRef] [PubMed]
- Stagg, C.J.; Best, J.G.; Stephenson, M.C.; O’Shea, J.; Wylezinska, M.; Kincses, Z.T.; Morris, P.G.; Matthews, P.M.; Johansen-Berg, H. Polarity-sensitive modulation of cortical neurotransmitters by transcranial stimulation. J. Neurosci. 2011, 31, 5013–5025. [Google Scholar] [CrossRef]
- Nasseri, P.; Nitsche, M.A.; Ekhtiari, H. A framework for categorizing electrode montages in transcranial direct current stimulation. Front. Hum. Neurosci. 2015, 9, 54. [Google Scholar] [CrossRef]
- Datta, A.; Bansal, V.; Diaz, J.; Patel, J.; Reato, D.; Bikson, M. Gyri-precise head model of transcranial direct current stimulation: Improved spatial focality using a ring electrode versus conventional rectangular pad. Brain Stimul. 2009, 2, 201–207. [Google Scholar] [CrossRef]
- Jamil, A.; Batsikadze, G.; Kuo, H.I.; Labruna, L.; Hasan, A.; Paulus, W.; Nitsche, M.A. Systematic evaluation of the impact of stimulation intensity on neuroplastic after-effects induced by transcranial direct current stimulation. J. Physiol. 2017, 595, 1273–1288. [Google Scholar] [CrossRef]
- López-Alonso, V.; Cheeran, B.; Rio-Rodriguez, D.; Fernandez-Del-Olmo, M. Inter-individual variability in response to non-invasive brain stimulation paradigms. Brain Stimul. 2014, 7, 372–380. [Google Scholar] [CrossRef]
- Nitsche, M.A.; Cohen, G.L.; Wassermann, E.M.; Priori, A.; Lang, N.; Antal, A.; Paulus, W.; Hummel, F.; Boggio, P.S.; Fregni, F.; et al. Transcranial direct current stimulation: State of the art 2008. Brain Stimul. 2008, 1, 206–223. [Google Scholar] [CrossRef]
- Yavari, F.; Jamil, A.; Mosayebi Samani, M.; Pinto Vidor, L.; Nitsche, M.A. Basic and functional effects of transcranial Electrical Stimulation (tES)—An introduction. Neurosci. Biobehav. Rev. 2018, 85, 81–92. [Google Scholar] [CrossRef]
- Patel, K.; Sutherland, H.; Henshaw, J.; Taylor, J.R.; Brown, C.A.; Casson, A.J.; Trujillo-Barreton, N.J.; Jones, A.K.P.; Sivan, M. Effects of neurofeedback in the management of chronic pain: A systematic review and meta-analysis of clinical trials. Eur. J. Pain 2020, 24, 1440–1457. [Google Scholar] [CrossRef]
- Khadka, N.; Borges, H.; Paneri, B.; Kaufman, T.; Nassis, E.; Zannou, A.L.; Shin, Y.; Choi, H.; Kim, S.; Lee, K.; et al. Adaptive current tDCS up to 4 mA. Brain Stimul. 2019, 12, 1499–1507. [Google Scholar] [CrossRef]
- Mansouri, F.; Dunlop, K.; Giacobbe, P.; Downar, J.; Zariffa, J. A fast EEG forecasting algorithm for phase-locked transcranial electrical stimulation of the human brain. Front. Neurosci. 2021, 15, 261. [Google Scholar] [CrossRef]
- Debnath, S.; Yazdani, A.; Fotiadis, D.I. Multimodal deep learning for enhanced diagnosis of major depressive disorder using EEG and fMRI. Neural Netw. 2022, 146, 391–403. [Google Scholar]
- Silva, S.; Gutman, B.A.; Romero, E. Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data. In Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 3–7 April 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1575–1578. [Google Scholar] [CrossRef]
- Bouthour, W.; Mégevand, P.; Donoghue, J.; Luescher, C.; Birbaumer, N.; Krack, P. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat. Rev. Neurol. 2019, 15, 343–352. [Google Scholar] [CrossRef]
- Lorenz, R.; Violante, I.R.; Monti, R.P.; Montana, G.; Hampshire, A.; Leech, R. Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization. Nat. Commun. 2019, 10, 2319. [Google Scholar] [CrossRef]
- Lipton, Z.C.; Kale, D.C.; Elkan, C.; Wetzel, R. Learning to diagnose with LSTM recurrent neural networks. arXiv 2016, arXiv:1511.03677. [Google Scholar]
- Chakraborty, B.; Murphy, S.A. Dynamic treatment regimes. Annu. Rev. Stat. Appl. 2014, 1, 447–464. [Google Scholar] [CrossRef]
- Berry, S.M.; Carlin, B.P.; Lee, J.J.; Muller, P. Bayesian Adaptive Methods for Clinical Trials; CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
- Simon, N.; Simon, R. Adaptive enrichment designs for clinical trials. Biostatistics 2013, 14, 613–625. [Google Scholar] [CrossRef]
- Sudre, C.H.; Murray, B.; Varsavsky, T.; Graham, M.S.; Penfold, R.S.; Bowyer, R.C.; Pujol, J.C.; Klaser, K.; Antonelli, M.; Canas, L.S.; et al. Attributes and predictors of long COVID. Nat. Med. 2021, 27, 626–631. [Google Scholar] [CrossRef]
- Qian, T.; Zhu, S.; Hoshida, Y. Use of big data in drug development for precision medicine: An update. Expert Rev. Precis. Med. Drug Dev. 2019, 4, 189–200. [Google Scholar] [CrossRef]
- Wang, Y.; Xu, C.; Park, J.H. Learning-based automatic detection of severe white matter hyperintensities in T2 FLAIR images. In Medical Imaging 2019: Computer-Aided Diagnosis; International Society for Optics and Photonics: Bellingham, WA, USA, 2019; Volume 10950, p. 109504G. [Google Scholar] [CrossRef]
- Manta, C.; Patrick, E.; Mukherjee, S. Deep learning for detecting fatigue in multiple sclerosis patients. arXiv 2020, arXiv:2009.13676. [Google Scholar]
- Dreisbach, C.; Koleck, T.A.; Bourne, P.E.; Bakken, S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int. J. Med. Inform. 2019, 125, 37–46. [Google Scholar] [CrossRef] [PubMed]
- Bandini, A.; Orlandi, S.; Escalante, H.J.; Giovanelli, F.; Cincotta, M.; Reyes-Garcia, C.A.; Vanni, P.; Zaccara, G.; Manfredi, C. Analysis of facial expressions in Parkinson’s disease through video-based automatic methods. J. Neurosci. Methods 2017, 281, 7–20. [Google Scholar] [CrossRef]
- Woo, C.W.; Chang, L.J.; Lindquist, M.A.; Wager, T.D. Building better biomarkers: Brain models in translational neuroimaging. Nat. Neurosci. 2017, 20, 365–377. [Google Scholar] [CrossRef] [PubMed]
- Kaissis, G.A.; Makowski, M.R.; Rückert, D.; Braren, R.F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2020, 2, 305–311. [Google Scholar] [CrossRef]
- El Emam, K.; Rodgers, S.; Malin, B. Anonymising and sharing individual patient data. BMJ 2015, 350, h1139. [Google Scholar] [CrossRef] [PubMed]
- Rieke, N.; Hancox, J.; Li, W.; Milletari, F.; Roth, H.R.; Albarquoni, S.; Bakas, S.; Galtier, M.N.; Landman, B.A.; Maier-Hain, K.; et al. The future of digital health with federated learning. NPJ Digit. Med. 2020, 3, 119. [Google Scholar] [CrossRef]
- Choudhury, O.; Karaca, Y.; Conci, N.; Iyengar, S. Ethical considerations for artificial intelligence in medical imaging: A systematic review. Front. Big Data 2020, 3, 573876. [Google Scholar]
- Weiss, D.; Rydland, H.T.; Øversveen, E.; Jensen, M.R.; Solhaug, S.; Krokstad, S. Innovative technologies and social inequalities in health: A scoping review of the literature. PLoS ONE 2018, 13, e0195447. [Google Scholar] [CrossRef]
- Gianfrancesco, M.A.; Tamang, S.; Yazdany, J.; Schmajuk, G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern. Med. 2018, 178, 1544–1547. [Google Scholar] [CrossRef]
- Watson, D.S.; Krutzinna, J.; Bruce, I.N.; Griffiths, C.E.; McInnes, I.B.; Barnes, M.R.; Floridi, L. Clinical applications of machine learning algorithms: Beyond the black box. BMJ 2019, 364, l886. [Google Scholar] [CrossRef]
- Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115. [Google Scholar] [CrossRef]
- Liu, X.; Faes, L.; Kale, A.U.; Wagner, S.K.; Fu, D.J.; Bruynseels, A.; Mahendiran, T.; Moraes, G.; Shamdas, M.; Kern, C.; et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digit. Health 2019, 1, e271–e297. [Google Scholar] [CrossRef]
- Daniels, N. Resource allocation and priority setting. In Public Health Ethics: Cases Spanning the Globe; Barrett, D.H., Ortmann, L.W., Dawson, A., Saenz, C., Reis, A., Bolan, G., Eds.; Springer: Cham, Switzerland, 2016; pp. 61–94. [Google Scholar] [CrossRef]
- Kraft, S.A.; Cho, M.K.; Gillespie, K.; Halley, M.; Varsave, N.; Ormond, K.E.; Luft, H.S.; Wilfond, B.S.; Soo-Jin Lee, S. Beyond consent: Building trusting relationships with diverse populations in precision medicine research. Am. J. Bioeth. 2018, 18, 3–20. [Google Scholar] [CrossRef] [PubMed]
- Gelinas, L.; Wertheimer, A.; Miller, F.G. When and why is research without consent permissible? Hastings Cent. Rep. 2017, 47, 35–43. [Google Scholar] [CrossRef]
- Price, W.N.; Cohen, I.G. Privacy in the age of medical big data. Nat. Med. 2019, 25, 37–43. [Google Scholar] [CrossRef]
- European Commission. Artificial Intelligence Act: Consolidated Text as of 30 May 2024; European Commission: Brussels, Belgium, 2024.
- Hanlon, C.A.; Dowdle, L.T.; Henderson, J.S. Modulation of brain activation during executive functioning in alcohol use disorder with and without comorbid stimulant use. Alcohol. Clin. Exp. Res. 2019, 43, 2503–2515. [Google Scholar] [CrossRef]
- Brunoni, A.R.; Moffa, A.H.; Sampaio-Junior, B.; Borrione, L.; Moreno, M.L.; Fernandez, R.A.; Veronezi, B.P.; Nogueira, B.S.; Aparicio, L.V.M.; Razza, L.B.; et al. Trial of electrical direct-current therapy versus escitalopram for depression. N. Engl. J. Med. 2017, 376, 2523–2533. [Google Scholar] [CrossRef]
- Mohr, D.C.; Weinstein, B.E.; Goodman, R.P.; Duffecy, J. Internet-delivered interventions for depression. Curr. Opin. Psychol. 2019, 30, 105–109. [Google Scholar] [CrossRef]
- Cavaliere, C.; Kandeepan, S.; Aiello, M.; Ribeiro de Paula, D.; Marchitelli, R.; Fiorenza, S.; Orsini, M.; Trojano, L.; Mascotta, O.; Lawrence, K.S.; et al. Multimodal neuroimaging approach to variability of functional connectivity in disorders of consciousness: A PET/MRI pilot study. Front. Neurol. 2016, 7, 243. [Google Scholar] [CrossRef]
- Sidey-Gibbons, J.A.; Sidey-Gibbons, C.J. Machine learning in medicine: A practical introduction. BMC Med. Res. Methodol. 2019, 19, 64. [Google Scholar] [CrossRef]
- Charvet, L.E.; Shaw, M.T.; Haider, L.; Melville, P.; Krupp, L.B. Remotely-delivered cognitive remediation in multiple sclerosis (MS): Protocol and results from a pilot study. Mult. Scler. J. Exp. Transl. Clin. 2018, 4, 2055217318783260. [Google Scholar] [CrossRef] [PubMed]
- D’Urso, G.; Dell’Osso, B.; Rossi, R.; Brunoni, A.R.; Bortolomasi, M.; Ferrucci, R.; Priori, A.; de Bartolomeis, A.; Altamura, A.C. Clinical predictors of acute response to transcranial direct current stimulation (tDCS) in major depression. J. Affect. Disord. 2017, 219, 25–30. [Google Scholar] [CrossRef] [PubMed]
- Gerke, S.; Minssen, T.; Cohen, G. Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial Intelligence in Healthcare; Bohr, A., Memarzadeh, K., Eds.; Academic Press: Cambridge, MA, USA, 2020; pp. 295–336. [Google Scholar] [CrossRef]
- Veinot, T.C.; Mitchell, H.; Ancker, J.S. Good intentions are not enough: How informatics interventions can worsen inequality. J. Am. Med. Inform. Assoc. 2018, 25, 1080–1088. [Google Scholar] [CrossRef] [PubMed]
- Ferrucci, R.; Vergari, M.; Cogiamanian, F.; Bocci, T.; Ciocca, M.; Tomasini, E.; De Riz, M.; Scarpini, E.; Priori, A. Transcranial direct current stimulation (tDCS) for fatigue in multiple sclerosis. NeuroRehabilitation 2014, 34, 121–127. [Google Scholar] [CrossRef] [PubMed]
- Lorenz, R.; Simmons, L.E.; Monti, R.P.; Arthur, J.L.; Limal, S.; Laakso, I.; Leech, R.; Violante, I.R. Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization. Brain Stimul. 2019, 12, 1484–1489. [Google Scholar] [CrossRef]
- Csifcsák, G.; Boayue, N.M.; Puonti, O.; Thielscher, A.; Mittner, M. Effects of transcranial direct current stimulation for treating depression: A modeling study. J. Affect. Disord. 2018, 234, 164–173. [Google Scholar] [CrossRef] [PubMed]
- Stachel, A.; Daniel, K.; Ding, D.; Francois, F.; Phillips, M.; Lighter, J. Development and validation of a machine learning model to predict mortality risk in patients with COVID-19. BMJ Health Care Inform. 2021, 28, e100235. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Torous, J.; Andersson, G.; Bertagnoli, A.; Christensen, H.; Cuijpers, P.; Firth, J.; Haim, A.; Hsin, H.; Hollis, C.; Lewis, S.; et al. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry 2019, 18, 97–98. [Google Scholar] [CrossRef]
- He, J.; Baxter, S.L.; Xu, J.; Xu, J.; Zhou, X.; Zhang, K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019, 25, 30–36. [Google Scholar] [CrossRef] [PubMed]
- Garrison, L.P., Jr.; Pauly, M.V.; Willke, R.J.; Neumann, P.J. An overview of value, perspective, and decision context—A health economics approach: An ISPOR Special Task Force report. Value Health 2018, 21, 124–130. [Google Scholar] [CrossRef]
- Suresh, V.; Singh, K.K.; Vaish, E.; Gurjar, M.; Ambuli Nambi, A.; Khulbe, Y.; Muzaffar, S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus 2024, 16, e59797. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Ding, W.; Abdel Bagda, E.; Nguyen, A. Artificial intelligence capacity building in resource-limited countries. Front. Artif. Intell. 2022, 5, 935639. [Google Scholar]
- Zhao, Y.; Zhai, X.; Wang, J.; Lan, J. Application of 5G technology in telemedicine during COVID-19 pandemic: A concise review. Front. Med. 2021, 8, 655454. [Google Scholar]
- Aranda-Jan, C.B.; Mohutsiwa-Dibe, N.; Loukanova, S. Systematic review on what works, what does not work and why of implementation of mobile health (mHealth) projects in Africa. BMC Public Health 2014, 14, 188. [Google Scholar] [CrossRef] [PubMed]
- Schwalbe, N.; Wahl, B. Artificial intelligence and the future of global health. Lancet 2020, 395, 1579–1586. [Google Scholar] [CrossRef] [PubMed]
- Bown, C.P.; Bollyky, T.J. How COVID-19 vaccine supply chains emerged in the midst of a pandemic. World Econ. 2022, 45, 468–522. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Alqurana, T.; Hamad, D.; Zaki, N. Open-source artificial intelligence in healthcare: A comprehensive review. IEEE Access 2022, 10, 89543–89559. [Google Scholar]
- Blandford, A.; Gibbs, J.; Newhouse, N.; Perski, O.; Singh, A.; Murray, E. Seven lessons for interdisciplinary research on interactive digital health interventions. Digit. Health 2018, 4, 2055207618770325. [Google Scholar] [CrossRef]
- Pillai, A.S.; Chandran, R.J.; Chandra, Y.U. Design of solar powered portable ECG. In Proceedings of the 2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR), Arakunnam, India, 11–13 July 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–4. [Google Scholar] [CrossRef]
- Agarwal, N.; Grottke, M.; Mishra, S.; Brem, A. A systematic literature review of constraint-based innovations: State of the art and future perspectives. IEEE Trans. Eng. Manag. 2017, 64, 3–15. [Google Scholar] [CrossRef]
- Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P.E.; et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 2016, 3, 160018. [Google Scholar] [CrossRef] [PubMed]
- Abebaw, W.; Teshome, D.; Mekonnen, Z.; Kibret, K. Digital health in Africa: Opportunities and challenges in the midst of COVID-19 pandemic. Digit. Health 2022, 8, 20552076221074437. [Google Scholar]
- Ouma, B.D.; Dimaras, H. Views from the global south: Exploring how student volunteers from the global north can achieve sustainable impact in global health. Glob. Health 2013, 9, 32. [Google Scholar] [CrossRef]
- Arasaratnam, A.; Humphreys, G. Emerging economies drive frugal innovation. Bull. World Health Organ. 2013, 91, 6–7. [Google Scholar] [CrossRef]
- Panch, T.; Szolovits, P.; Atun, R. Artificial intelligence, machine learning and health systems. J. Glob. Health 2018, 8, 020303. [Google Scholar] [CrossRef]
- Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 2019, 1, 206–215. [Google Scholar] [CrossRef]
- Chen, J.H.; Asch, S.M. Machine learning and prediction in medicine—Beyond the peak of inflated expectations. N. Engl. J. Med. 2017, 376, 2507–2509. [Google Scholar] [CrossRef]
- Cabitza, F.; Rasoini, R.; Gensini, G.F. Unintended consequences of machine learning in medicine. JAMA 2017, 318, 517–518. [Google Scholar] [CrossRef]
- Cohen, I.G.; Evgeniou, T.; Gerke, S.; Minssen, T. The European artificial intelligence strategy: Implications and challenges for digital health. Lancet Digit. Health 2020, 2, e376–e379. [Google Scholar] [CrossRef] [PubMed]
- Topol, E.J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 2019, 25, 44–56. [Google Scholar] [CrossRef] [PubMed]
- Peek, N.; Combi, C.; Marin, R.; Bellazzi, R. Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes. Artif. Intell. Med. 2020, 109, 101974. [Google Scholar] [CrossRef]
- Rigby, M.J. Ethical dimensions of using artificial intelligence in health care. AMA J. Ethics 2019, 21, 121–124. [Google Scholar] [CrossRef]
- FDA. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan; U.S. Food and Drug Administration: Silver Spring, MD, USA, 2021.
AI Technique | Description | Potential Applications in tDCS for Long COVID |
---|---|---|
Support Vector Machines (SVM) | Supervised learning model for classification and regression |
|
Random Forests | Ensemble learning method using multiple decision trees |
|
Deep Learning (Neural Networks) | Multi-layer neural networks capable of learning complex patterns for classification and regression |
|
Reinforcement Learning | Learning optimal actions through trial and error |
|
Natural Language Processing (NLP) | Processing and analyzing human language |
|
Clustering Algorithms (e.g., K-means) | Unsupervised learning for identifying groups in data |
|
Bayesian Optimization | Efficient optimization of black-box functions |
|
Research Area | Key Objectives | Potential Approaches |
---|---|---|
1. Optimal tDCS Parameters | Determine effective montages and stimulation parameters for different Long COVID symptoms | Large-scale, randomized controlled trials with AI analysis of patient characteristics and outcomes |
2. AI-driven Personalization | Develop AI algorithms to predict individual patient responses and enable real-time parameter adjustment | Machine learning models using large datasets of patient characteristics and tDCS outcomes; Closed-loop tDCS systems with AI-driven parameter adjustment |
3. Long-term Efficacy and Safety | Understand long-term effects and potential cumulative effects or safety concerns | Longitudinal studies (12–24 months) with AI analysis of long-term outcome patterns and safety signals |
4. Integration with Other Treatments | Investigate interactions between AI-guided tDCS and other Long COVID treatments | Factorial studies combining tDCS with other treatments, using AI to analyze interaction effects |
5. Neuroplasticity and Mechanism of Action | Investigate how AI-guided tDCS affects brain plasticity and connectivity | Studies combining tDCS with neuroimaging (fMRI, EEG) and AI analysis of brain changes |
6. Combining tDCS with Neuroimaging | Develop imaging-based biomarkers for predicting and monitoring tDCS response | Multimodal imaging approaches (PET, fMRI, EEG) with AI analysis of complex datasets |
7. Patient-Reported Outcomes and Quality of Life | Incorporate patient-centered outcomes into AI models for tDCS optimization | AI models integrating standardized quality of life measures and free-text patient narratives |
8. Home-Based AI-Guided tDCS | Investigate feasibility and safety of home-based, AI-guided tDCS | Feasibility studies of home-based tDCS systems with AI monitoring |
9. Health Economic Analysis | Understand cost-effectiveness of AI-guided tDCS compared to standard care | Health economic modeling studies incorporating treatment efficacy, resource utilization, and quality of life data |
10. Ethical and Regulatory Frameworks | Develop comprehensive ethical guidelines and regulatory frameworks | Multidisciplinary studies involving ethicists, legal experts, clinicians, and patients |
11. Generalizability and Health Disparities | Investigate generalizability of AI models across populations and potential to address health disparities | Multi-center, international studies; Targeted studies in underserved communities |
Neuroimaging Modality | Data Provided | AI Application | Potential Impact on tDCS |
---|---|---|---|
PET (Positron Emission Tomography) | Metabolic activity, Neuroinflammation | Pattern recognition for identifying hyperactive/hypoactive regions | Target selection for tDCS montage |
fMRI (functional Magnetic Resonance Imaging) | Brain activation patterns, Functional connectivity | Network analysis, Dynamic causal modeling | Optimization of stimulation parameters based on functional connectivity |
sMRI (structural MRI) | Brain structure, White matter integrity | Voxel-based morphometry, Tract-based spatial statistics | Personalization of electrode placement based on individual anatomy |
DTI (Diffusion Tensor Imaging) | White matter tractography | Graph theoretical analysis of structural connectome | Targeting of specific white matter tracts for stimulation |
EEG (Electroencephalography) | Cortical oscillations, Event-related potentials | Machine learning for EEG feature extraction, Real-time signal processing | Closed-loop tDCS with adaptive stimulation based on ongoing brain activity |
MEG (Magnetoencephalography) | Source-localized neural activity | AI-driven source reconstruction, Functional connectivity analysis | Precise targeting of deep brain regions for stimulation |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rudroff, T.; Rainio, O.; Klén, R. Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective. Brain Sci. 2024, 14, 831. https://doi.org/10.3390/brainsci14080831
Rudroff T, Rainio O, Klén R. Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective. Brain Sciences. 2024; 14(8):831. https://doi.org/10.3390/brainsci14080831
Chicago/Turabian StyleRudroff, Thorsten, Oona Rainio, and Riku Klén. 2024. "Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective" Brain Sciences 14, no. 8: 831. https://doi.org/10.3390/brainsci14080831