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.
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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
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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.
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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
1.3 Appendix 3: IRT Protocols in Included Studies
1.4 Appendix 4: Relationship Temperature, VAS and Disease Duration for HZ. Study-Specific Breakdown
1.5 Appendix 5: Relationship Temperature, VAS and Disease Duration for PHN. Study-Specific Breakdown
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
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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
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