Artificial intelligence-assisted smartphone thermal imaging for pedicled facial and head-neck flaps: An exploratory feasibility study using ΔT-based proxy perfusion grading.
5 affiliations
- 1Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Augsburg, Sauerbruchstraße 6, Augsburg, 86179, Germany.
- 2Department of Otorhinolaryngology, University of Heidelberg, Heidelberg, Germany.
- 3Department for Otorhinolaryngology, Head and Neck, Plastic Operations, SLK Kliniken, Heilbronn, Germany.
- 4Chair of Information Infrastructures, School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany.
- 5Tongji University, Shanghai, China.
Abstract
This pilot study evaluates the feasibility of artificial intelligence (AI)-assisted analysis of smartphone-based thermal imaging (SBTI) for perfusion assessment in pedicled facial, head and neck flaps. Because SBTI provides only false-color, non-radiometric thermal maps, ΔT-based grading relies on subjective human interpretation. We therefore investigate whether AI-derived perfusion probability maps were explored in relation to this manually generated proxy reference. Twenty-six patients undergoing reconstruction with pedicled skin flaps were examined with SBTI at four perioperative timepoints. ΔT-values were visually extracted from the FLIR color scale across predefined flap zones and translated into a five-grade perfusion score. A fine-tuned ResNet-50 model generated image-level probabilistic classification outputs regarding thermal similarity to patterns labeled as perfused versus non-perfused and uncertainty metrics. Analyses focused on T3 (end of surgery). Thermal patterns varied substantially by flap type, thickness, and anatomical region. Random-pattern glabellar, nasal, and alar flaps showed physiologically lower surface temperatures and correspondingly reduced ΔT- and AI-derived prediction probabilities despite uneventful healing. No statistically significant correlation was observed between AI-derived perfusion probabilities and manually derived ΔT grades. AI-assisted SBTI is technically feasible but did not demonstrate meaningful quantitative agreement with manually derived ΔT-based grading in this small exploratory cohort. The findings primarily highlight the methodological limitations of false-color thermal interpretation and the need for radiometric imaging, validated perfusion references, and larger anatomically stratified datasets.
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BibTeX
@article{ls2026artificial,
title = {{Artificial intelligence-assisted smartphone thermal imaging for pedicled facial and head-neck flaps: An exploratory feasibility study using ΔT-based proxy perfusion grading.}},
author = {Fiedler Ls and Veitinger A and Leiser F and Ness N and Thiebes S and Sunyaev A},
journal = {JPRAS open},
year = {2026},
month = oct,
doi = {10.1016/j.jpra.2026.10.016},
url = {https://europepmc.org/article/MED/42997025},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence-assisted smartphone thermal imaging for pedicled facial and head-neck flaps: An exploratory feasibility study using ΔT-based proxy perfusion grading.
AU - Fiedler Ls
AU - Veitinger A
AU - Leiser F
AU - Ness N
AU - Thiebes S
AU - Sunyaev A
PY - 2026
DA - 2026/10/21
JO - JPRAS open
DO - 10.1016/j.jpra.2026.10.016
UR - https://europepmc.org/article/MED/42997025
AB - This pilot study evaluates the feasibility of artificial intelligence (AI)-assisted analysis of smartphone-based thermal imaging (SBTI) for perfusion assessment in pedicled facial, head and neck flaps. Because SBTI provides only false-color, non-radiometric thermal maps, ΔT-based grading relies on subjective human interpretation. We therefore investigate whether AI-derived perfusion probability maps were explored in relation to this manually generated proxy reference. Twenty-six patients undergoing reconstruction with pedicled skin flaps were examined with SBTI at four perioperative timepoints. ΔT-values were visually extracted from the FLIR color scale across predefined flap zones and translated into a five-grade perfusion score. A fine-tuned ResNet-50 model generated image-level probabilistic classification outputs regarding thermal similarity to patterns labeled as perfused versus non-perfused and uncertainty metrics. Analyses focused on T3 (end of surgery). Thermal patterns varied substantially by flap type, thickness, and anatomical region. Random-pattern glabellar, nasal, and alar flaps showed physiologically lower surface temperatures and correspondingly reduced ΔT- and AI-derived prediction probabilities despite uneventful healing. No statistically significant correlation was observed between AI-derived perfusion probabilities and manually derived ΔT grades. AI-assisted SBTI is technically feasible but did not demonstrate meaningful quantitative agreement with manually derived ΔT-based grading in this small exploratory cohort. The findings primarily highlight the methodological limitations of false-color thermal interpretation and the need for radiometric imaging, validated perfusion references, and larger anatomically stratified datasets.
SN - 2352-5878
ER -