Skip to main content

The Future of Herpes Zoster Care: Ai-Powered Thermal Imaging for Accurate Diagnosis and PHN Prediction

  • Conference paper
  • First Online:
Artificial Intelligence over Infrared Images for Medical Applications (AIIIMA 2024)

Abstract

Infrared thermography (IRT) combined with advanced artificial intelligence (AI) algorithms has emerged as a promising non-invasive tool for assessing and managing diseases. Herpes zoster (HZ) and postherpetic neuralgia (PHN), a chronic neuropathic pain is condition that often follows HZ infection. This scoping review synthesizes current knowledge on the integration of IRT and AI in understanding the pathophysiology, predicting the development, and guiding the treatment of PHN. A comprehensive literature search was conducted in multiple databases from inception to May 20, 2024. Studies investigating the use of infrared thermography in herpes zoster and postherpetic neuralgia were included, with focus on recent advancements in AI applications. The review encompassed 1177 participants across various studies. We analyzed research utilizing machine learning techniques, including support vector machines, logistic regression, random forests, and deep learning models, to address the limitations identified in current HZ/PHN management practices. The review adhered to PRISMA-ScR guidelines. Findings suggest that patients with PHN exhibit distinct thermal patterns, with asymmetry between affected and unaffected dermatomes correlating more with disease duration than pain intensity. Temperature differences greater than 0.5 ℃ between affected and unaffected dermatomes were associated with a significantly increased risk of PHN development. IRT has shown promise as a predictor of PHN development in acute HZ patients and for assessing treatment response. This review introduces innovative AI approaches to standardize thermal imaging in HZ and PHN management. Novel biomarkers - Thermal Asymmetry Index (TAI), Persistent Thermal Asymmetry Index (PTAI), and Thermal Normalization Index (TNI) - are proposed, along with an Adjusted Risk Index (ARI) incorporating Age and Pain Adjustment Factors. These elements are integrated into the AI THERMO-Z protocol for standardized assessment. While requiring further validation, this framework aims to enhance the scientific rigor of thermal imaging in HZ and PHN management. IRT shows promise as a biomarker for predicting PHN in acute HZ. The proposed AI THERMO-Z protocol, integrating novel biomarkers and risk indices, aims to standardize thermal imaging assessment. Large-scale studies are needed to validate its clinical utility in HZ and PHN management. By leveraging AI, particularly machine learning models, the accuracy of IRT in detecting subtle thermal anomalies can be enhanced, providing clinicians with more precise predictive analytics and personalized treatment strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Data Availability Statement

No additional data are available for this scoping review. All data analyzed in this study are included in the published article and its supplementary materials.

Abbreviations

HZ::

Herpes Zoster

PHN::

Postherpetic Neuralgia

IRT::

Infrared Thermography

TAI::

Thermal Asymmetry Index

PTAI::

Persistent Thermal Asymmetry Index

TNI::

Thermal Normalization Index

ARI::

Adjustment Risk Index

AAF::

Age Adjustment Factor

PAF::

Pain Adjustment Factor

VAS::

Visual Analog Scale

ROI::

Region of Interest

BTT::

Brain Tunnel Temperature

PRISMA-ScR::

Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

RCT::

Randomized Controlled Trial

NOS::

Newcastle-Ottawa Scale

SANRA:

Scale for the Assessment of Narrative Review Articles

JBI::

Joanna Briggs Institute

AI::

Artificial Intelligence

ML::

Machine Learning

RF::

Random Forest

References

  1. Rowbotham, M.C., Fields, H.L.: Post-herpetic neuralgia: the relation of pain complaint, sensory disturbance and skin temperature. Pain 39(2), 129–144 (1989)

    Article  Google Scholar 

  2. Rowbotham, M.C., Fields, H.L.: The relationship of pain, allodynia and thermal sensation in post-herpetic neuralgia. Brain 119(2), 347–354 (1996)

    Article  Google Scholar 

  3. Torradeflot, G.C.: Evaluación de la temperatura facial y ocular en herpes zóster y neuralgia postherpética oftálmica. Invest. Ophthalmol. Vis. Sci. 37(3), S49 (1996)

    Google Scholar 

  4. Gross, G.E., Eisert, L., Doerr, H.W., Fickenscher, H., Knuf, M., Maier, P., et al.: S2k guidelines for the diagnosis and treatment of herpes zoster and postherpetic neuralgia. J. Deutsche Derma Gesell. 18(1), 55–78 (2020)

    Google Scholar 

  5. Han, S.S., Jung, C.H., Lee, S.C., Jung, H.J., Kim, Y.H.: Does skin temperature difference as measured by infrared thermography within 6 months of acute herpes zoster infection correlate with pain level? Skin Res. Technol. 16(2), 198–201 (2010)

    Article  Google Scholar 

  6. Kanai, A., Wang, G., Hoshi, K., Okamoto, H.: Effects of intravenous prostaglandin E1 on pain and body temperature in patients with post-herpetic neuralgia. Pain Med. 11(4), 609–616 (2010)

    Google Scholar 

  7. Pan, C.X., Lee, M.S., Nambudiri, V.E.: Global herpes zoster incidence, burden of disease, and vaccine availability: a narrative review. Therapeutic Adv. Vaccin. Immunother. 10, 251513552210845 (2022)

    Article  Google Scholar 

  8. Wang, X.X., Zhang, Y., Fan, B.F.: Predicting postherpetic neuralgia in patients with herpes zoster by machine learning: a retrospective study. Pain Ther. 9(2), 627–635 (2020)

    Article  Google Scholar 

  9. Tricco, A.C., Lillie, E., Zarin, W., O’Brien, K.K., Colquhoun, H., Levac, D., et al.: PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med. 169(7), 467–473 (2018)

    Article  Google Scholar 

  10. Ammer, K., Schartelmueller, T., Melnizky, P.: Thermal imaging in acute herpes zoster or post-zoster neuralgia. Skin Res. Technol. 7(4), 219–222 (2001)

    Article  Google Scholar 

  11. Ammer, K.: Thermological studies in rehabilitation and rheumatology using computerised infrared imaging [Internet] [PhD]. University of Glamorgan/Prifysgol Morgannwg (2000). https://pure.southwales.ac.uk/en/studentTheses/thermological-studies-in-rehabilitation-and-rheumatology-using-co

  12. Cojocaru, I.M., Cojocaru, M., Voiculescu, V., Bozdoc-Ionescu, O., Cartog, A., Giurcaneanu, C.: Thermal patterns in zoster. Int. J. Infect. Dis. 8(3), 346–349 (2015)

    Google Scholar 

  13. Kim, J.H., Lee, C.S., Han, W.K., Sim, J.B., Nahm, F.S.: Determining the definitive time criterion for postherpetic neuralgia using infrared thermographic imaging. Pain Ther. 11(2), 591–600 (2022)

    Article  Google Scholar 

  14. da Alves, A.S.: Acupuntura na Dor Neuropática, 512 p. Editora Atheneu, Rio de Janeiro, RJ (2022)

    Google Scholar 

  15. Ahn, E.K., Yang, J.Y., Cho, J.G., Kim, J., Chon, S., Yoo, E.S., et al.: Significance of infrared thermal imaging in herpes zoster patients. Korean J. Anesthesiol. 47(4), 505 (2004)

    Article  Google Scholar 

  16. Park, J., Jang, W.S., Park, K.Y., Li, K., Seo, S.J., Hong, C.K., et al.: Thermography as a predictor of postherpetic neuralgia in acute herpes zoster patients: a preliminary study. Skin Res. Technol. 18(1), 88–93 (2012)

    Article  Google Scholar 

  17. Ko, E.J., No, Y.A., Park, K.Y., Li, K., Seo, S.J., Hong, C.K.: The clinical significance of infrared thermography for the prediction of postherpetic neuralgia in acute herpes zoster patients. Skin Res. Technol. 22(1), 108–114 (2016)

    Article  Google Scholar 

  18. Fan, H.R., Zhang, E.M., Fei, Y., Huang, B., Yao, M.: Early diagnosis of herpes zoster neuralgia: a narrative review. Pain Ther. 12(4), 893–901 (2023)

    Google Scholar 

  19. Hu, H., Cheng, Y., Wu, L., Han, D., Ma, R.: Investigating the therapeutic effect of intradermal acupuncture for acute herpes zoster and assessing the feasibility of infrared thermography for early prediction of postherpetic neuralgia: study protocol for a randomized, sham-controlled. Clin. Trial. JPR. 16, 1401–1413 (2023)

    Google Scholar 

  20. Zhang, W., He, C.: Clinical efficacy of pulsed radiofrequency combined with intravenous lidocaine infusion in the treatment of subacute herpes zoster neuralgia. In: Taiar, R. (ed.) Pain Research and Management, pp. 1–14 (2022)

    Google Scholar 

  21. Liao, Y.M., Lu, H.F., Xie, P., Zhao, Y., Han, Q., Zhang, Q.X., et al.: Thermographic follow-up of postherpetic neuralgia (PHN) subsequent to Ramsay Hunt syndrome with multicranial nerve (V, VII, VIII and IX) involvement: a case report. BMC Neurol. 21(1), 39 (2021)

    Article  Google Scholar 

  22. Lee, J.W., Kim, D.H., Lee, H.I., Han, T.Y., Li, K., Seo, S.J., et al.: Thermographic follow-up of a mild case of herpes zoster. Arch. Dermatol. [Internet] 146(9) (2010).http://archderm.jamanetwork.com/article.aspx?doi=10.1001/archdermatol.2010.231

  23. DeSouza, A.: Thermal images in the assessment of post-herpetic neuralgia: a case study. BJSTR [Internet] 28(4) (2020). https://biomedres.us/fulltexts/BJSTR.MS.ID.004685.php

  24. Marri, S.S., Albadri, W., Hyder, M.S., Janagond, A.B., Inamadar, A.C.: Efficacy of an artificial intelligence App (Aysa) in dermatological diagnosis: cross-sectional analysis. JMIR Dermatol. 2(7), e48811 (2024)

    Article  Google Scholar 

  25. Lanera, C., Baldi, I., Francavilla, A., Barbieri, E., Tramontan, L., Scamarcia, A., et al.: A deep learning approach to estimate the incidence of infectious disease cases for routinely collected ambulatory records: the example of varicella-zoster. IJERPH. 19(10), 5959 (2022)

    Article  Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

Authors

Contributions

BB conceptualized and designed the study, developed the search strategy, performed the study selection, extracted the data, assessed the risk of bias, synthesized the results, conducted the analyses, and drafted the manuscript. MB provided expert insights and guidance throughout the entire process, contributing to the conceptualization, methodology, and interpretation of findings. BO and BH from Temple University provided scholarly review of the manuscript. GB contributed with formatting assistance.

Corresponding author

Correspondence to Belén Borja .

Editor information

Editors and Affiliations

Ethics declarations

The authors declare no conflict of interest.

Appendix

Appendix

1.1 Appendix 1: Summary of Risk of Bias Assessment and Quality for all Included (analyzed by JBI, NOS: Newcastle-Ottawa Scale, SANRA: Scale for the Assessment of Narrative Review Articles, JBI: Joanna Briggs Institute and ROB2 Scores)

1.2 Appendix 2: Characteristics of Studies included in this review, investigating IRT for Predicting and Assessing HZ and PHN

  1. Legend: Comorbidities: FP: Facial Paralysis, TMJ: Temporomandibular Joint, NS: Nasal Sinus, HA: Headache, DM: Diabetes Mellitus, C: Cancer, CD: Crohn's Disease, CV: Cardiovascular, H: Hepatitis, RHS: Ramsay Hunt Syndrome, HT: Hypertension, ID: Immune Deficiency Skin Signs: R: Rash, SL: Skin Lesions, A: Allodynia Treatment: L: Lidocaine, A: Amitriptyline, D: Desipramine, C: Carbamazepine, V: Valproic Acid, AV: Antiviral, IV: Intravenous, EI: Epidural Infusion, SNB: Selective Nerve Block, MT: Medical Therapy, M: Medication, PGE: Prostaglandin E, S: Saline, G: Gabapentin, PRF: Pulsed Radiofrequency, NIR: Near-Infrared, LI: Lidocaine Infusion, V: Vaxilovir, IDA: Intradermal Acupuncture, AI: Anti-inflammatory, TCA: Tricyclic Antidepressants, O: Opioids, NB: Nerve Blocks, SWT: Shockwave Therapy, EA: Electroacupuncture

1.3 Appendix 3: IRT Protocols in Included Studies

1.4 Appendix 4: Relationship Temperature, VAS and Disease Duration for HZ. Study-Specific Breakdown

  1. *Estimated average and SD for disease duration, assuming 6 months = 180 days for study [5]

1.5 Appendix 5: Relationship Temperature, VAS and Disease Duration for PHN. Study-Specific Breakdown

  1. *Estimated average and SD for disease duration, converting all to months using minimum values for studies [16]

1.6 Appendix 6: Relationship Temperature, VAS for HZ and PHN at Different Time Points. Aggregated Data from Multiple Studies

1.7 Appendix 7: Temperature, Age and Pain focused quantitative analysis Results

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Borja, B., Brioschi, M.L., Brioschi, G.C., OÝoung, B., Habibi, B.A. (2025). The Future of Herpes Zoster Care: Ai-Powered Thermal Imaging for Accurate Diagnosis and PHN Prediction. In: Kakileti, S.T., Manjunath, G., Schwartz, R.G., Ng, E.Y.K. (eds) Artificial Intelligence over Infrared Images for Medical Applications. AIIIMA 2024. Lecture Notes in Computer Science, vol 15279. Springer, Cham. https://doi.org/10.1007/978-3-031-76584-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-76584-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-76583-4

  • Online ISBN: 978-3-031-76584-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics