Sergio Del Río-Sancho, Stephanie Christen-Zaech, David Alvarez Martinez, Jöri Pünchera, Stéphane Guerrier, Hans J Laubach
{"title":"Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms as tool to investigate wound healing: A prospective, randomized, single-blinded pilot study.","authors":"Sergio Del Río-Sancho, Stephanie Christen-Zaech, David Alvarez Martinez, Jöri Pünchera, Stéphane Guerrier, Hans J Laubach","doi":"10.1111/jdv.20478","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>This study aims to assess the value of combining LC-OCT and AI for evaluating the acute wound healing process in the skin.</p><p><strong>Methods: </strong>The forearms of participating volunteers were ablated with a CO<sub>2</sub> 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Clinicaltrials: </strong></p><p><strong>Gov identifier: </strong>NCT05614557.</p>","PeriodicalId":17351,"journal":{"name":"Journal of the European Academy of Dermatology and Venereology","volume":" ","pages":""},"PeriodicalIF":8.4000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the European Academy of Dermatology and Venereology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jdv.20478","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DERMATOLOGY","Score":null,"Total":0}
Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms as tool to investigate wound healing: A prospective, randomized, single-blinded pilot study.
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 CO2 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.
期刊介绍：
The Journal of the European Academy of Dermatology and Venereology (JEADV) is a publication that focuses on dermatology and venereology. It covers various topics within these fields, including both clinical and basic science subjects. The journal publishes articles in different formats, such as editorials, review articles, practice articles, original papers, short reports, letters to the editor, features, and announcements from the European Academy of Dermatology and Venereology (EADV).
The journal covers a wide range of keywords, including allergy, cancer, clinical medicine, cytokines, dermatology, drug reactions, hair disease, laser therapy, nail disease, oncology, skin cancer, skin disease, therapeutics, tumors, virus infections, and venereology.
The JEADV is indexed and abstracted by various databases and resources, including Abstracts on Hygiene & Communicable Diseases, Academic Search, AgBiotech News & Information, Botanical Pesticides, CAB Abstracts®, Embase, Global Health, InfoTrac, Ingenta Select, MEDLINE/PubMed, Science Citation Index Expanded, and others.
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