Line‐field confocal optical coherence tomography coupled with artificial intelligence algorithms as tool to investigate wound healing: A prospective, randomized, single‐blinded pilot study
Open accessAbstract
Background: Ablative fractional photothermolysis serves as an excellent in vivo model for studying wound healing. The advent of non‐invasive imaging devices, such as line‐field confocal optical coherence tomography (LC‐OCT), enhances this model by enabling detailed monitoring of skin wound healing over time. Additionally, artificial intelligence (AI)‐based algorithms are revolutionizing the evaluation of clinical images by providing detailed analyses that are unfeasible manually. Objectives: This study aims to assess the value of combining LC‐OCT and AI for evaluating the acute wound healing process in the skin. Methods: The forearms of participating volunteers were ablated with a CO 2 laser in a fractional pattern (7.5 mJ/MTZ) (ClinicalTrials.gov identifier: NCT05614557). To induce observable wound healing differences, two different approved silicone‐based formulations were randomly assigned to two test sites, with a third site left untreated. In vivo LC‐OCT images were obtained at predefined intervals post‐laser treatment, ranging from 1 to 7 days. These images were further analysed using AI algorithms. Results: LC‐OCT visualization allows for the characterization of the structural reorganization of the skin during wound healing. The additional integration of AI algorithms significantly enhances the evaluation of the efficacy of wound care interventions by providing a deeper understanding of how these interventions improve wound healing. This is particularly valuable for primary care providers and dermatologists, as AI algorithms have proven useful in observing, characterizing and understanding keratinocyte behaviour. Conclusions: The combination of AI and high‐resolution imaging represents a promising tool for better understanding wound healing, evaluating the efficacy of current wound care interventions and analysing keratinocyte behaviour in detail during the wound healing process. ClinicalTrials.gov Identifier: NCT05614557.
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Applying tomography and AI to reconstruct 3D epidermal cross-sections to indicate a lesion or an anomalous vascularization is possible.
“the possibility of applying tomography and AI to reconstruct 3D epidermal cross-sections to indicate a lesion [54,55,56] or an anomalous vascularization [57,58].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [56]
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Line-Field Confocal Optical Coherence Tomography (LC-OCT) for Skin Imaging in Dermatology
Latriglia, F., Ogien, J., Tavernier, C., Fischman, S., Suppa, M., Perrot, J.-L., & Dubois, A. (2023) · Life · doi:10.3390/life13122268 · Cited by 74 · see citing works
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Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“…studies highlight the possibility of combining the processing of histopathology images with sequential dermoscopic ones [53], as well as the possibility of applying tomography and AI to reconstruct 3D epidermal cross-sections to indicate a lesion [54–56] or an anomalous vascularization [57,58].”
- Authors
- del Río‐Sancho, S., Christen‐Zaech, S., Martinez, D. A., Pünchera, J., Guerrier, S., & Laubach, H. J.
- Affiliations
- Laser Dermatology Consultation, Division of Dermatology and Venereology Geneva University Hospitals Geneva Switzerland
- Pediatric Dermatology Unit, Department of Dermatology & Venereology, University Hospital Lausanne University of Lausanne Lausanne Switzerland
- University Hospital of Geneva (CH)
- University of Geneva (CH)
- Corresponding author
- Laubach, Hans J.
- ORCID
- Venue
- Journal of the European Academy of Dermatology and Venereology
- Publisher
- Wiley
- Published
- 2024-12-17
- Volume
- 39
- Issue
- 8
- Pages
- 1481-1488
- ISSN
- 0926-9959 (ISSN-L)
- 1468-3083
- OpenAlex
- W4405500302
- Cited by
- 4
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the European Academy of Dermatology and Venereology (publishedVersion)
- PubMed (publishedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Stratpharma Switzerland
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Optical Coherence Tomography Applications
- Health Sciences › Medicine › Rehabilitation › Wound Healing and Treatments
- Health Sciences › Medicine › Dermatology › Dermatologic Treatments and Research
- Keywords
- line-field confocal optical coherence tomography, wound healing, artificial intelligence, fractional photothermolysis, skin wound healing, non-invasive skin imaging
- MeSH terms
- Adult, Algorithms, Artificial Intelligence, Female, Humans, Male, Middle Aged, Pilot Projects, Prospective Studies, Skin, Wound Healing, Single-Blind Method, Tomography, Optical Coherence, Lasers, Gas
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