Skin dark spot mapping and evaluation of brightening product efficacy using Line-field Confocal Optical Coherence Tomography (LC-OCT).

BACKGROUND: Facial dark spots remain a significant challenge for the cosmetic industry, in terms of providing effective treatment. Using Line-field Confocal Optical Coherence Tomography (LC-OCT), we investigated the internal structural features of photo-aging spot areas and evaluated the efficacy of a skin-brightening cosmetic product. MATERIALS AND METHODS: Twenty-six Asian female volunteers, aged between 29 and 65 years, applied a cosmetic product on their entire face twice a day for 2 months. LC-OCT was used to evaluate the dermal-epidermal junction (DEJ) undulation and the volume density of melanin in the epidermis at D0 and D56. Skin brightening and redness were also assessed by photography (SkinCam). RESULTS: Using LC-OCT technology, various microscopic dark spot morphologies, spanning from minimally deformed DEJ to complex DEJ patterns, were identified. Dark spots characterized by slight deformities in the DEJ were predominantly observed in the youngest age group, while older volunteers displayed a wavier pattern. Furthermore, a total of 44 spots were monitored to evaluate the brightening product efficacy. A statistically significant reduction in melanin volumetric density of 7.3% in the spots and 12.3% in their surrounding area was observed after 56 days of product application. In line with these results, an analysis of color parameters using SkinCam reveals a significant increase in brightening and decrease in redness in both pigmented spots and the surrounding skin following application. CONCLUSIONS: LC-OCT proves to be a valuable tool for in-depth dark spots characterization and assessment of skin brightening products, enabling various applications in the field of dermatological sciences.

Line-Field Confocal Optical Coherence Tomography (LC-OCT) for Skin Imaging in Dermatology.

Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive optical imaging technique based on a combination of the principles of optical coherence tomography and reflectance confocal microscopy with line-field illumination, which can generate cell-resolved images of the skin in vivo. This article reports on the LC-OCT technique and its application in dermatology. The principle of the technique is described, and the latest technological innovations are presented. The technology has been miniaturized to fit within an ergonomic handheld probe, allowing for the easy access of any skin area on the body. The performance of the LC-OCT device in terms of resolution, field of view, and acquisition speed is reported. The use of LC-OCT in dermatology for the non-invasive detection, characterization, and therapeutic follow-up of various skin pathologies is discussed. Benign and malignant melanocytic lesions, non-melanocytic skin tumors, such as basal cell carcinoma, squamous cell carcinoma and actinic keratosis, and inflammatory and infectious skin conditions are considered. Dedicated deep learning algorithms have been developed for assisting in the analysis of LC-OCT images of skin lesions.

Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms to identify quantitative biomarkers of facial skin ageing.

Quantitative biomarkers of facial skin ageing were studied from one hundred healthy Caucasian female volunteers, aged 20-70 years, using in vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging coupled with Artificial Intelligence (AI)-based quantification algorithms. Layer metrics, i.e. stratum corneum thickness (SC), viable epidermal thickness and Dermal-Epidermal Junction (DEJ) undulation, as well as cellular metrics were measured for the temple, cheekbone and mandible. For all three investigated facial areas, minimal age-related variations were observed in the thickness of the SC and viable epidermis layers. A flatter and more homogeneous epidermis (decrease in the standard deviation of the number of layers means), a less dense cellular network with fewer cells per layer (decrease in cell surface density), and larger and more heterogeneous nuclei within each layer (increase in nuclei volume and their standard deviation) were found with significant variations with age. The higher atypia scores further reflected the heterogeneity of nuclei throughout the viable epidermis. The 3D visualisation of fine structures in the skin at the micrometric resolution and the 1200 µm × 500 µm field of view achieved with LC-OCT imaging enabled to compute relevant quantitative biomarkers for a better understanding of skin biology and the ageing process in vivo.

Line-field confocal optical coherence tomography (LC-OCT): principles and practical use.

Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive optical imaging technique based on a combination of the optical principles of optical coherence tomography and reflectance confocal microscopy with line-field illumination, which can generate cell-resolved images of the skin, in vivo, in vertical section, horizontal section and in three dimensions. This article reviews the optical principles of LC-OCT, including low coherence interferometry, confocal filtering and line-field arrangement. The optical setup allowing for the acquisition of color images of the skin surface in parallel with LC-OCT images, without compromising LC-OCT performance, is also presented. Practical use of LC-OCT is demonstrated through an overview of the workflow of examining a patient using a commercial handheld LC-OCT probe (deepLive™, DAMAE Medical), from creating the patient record in the software, acquiring the images, to reviewing and interpreting the images. LC-OCT can generate a significant amount of data, making automated deep learning algorithms particularly relevant for assisting in the analysis of LC-OCT images. A review of algorithms developed for skin layer segmentation, keratinocyte nuclei segmentation, and automatic detection of atypical keratinocyte nuclei is provided.

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning.

Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.

Line-field confocal optical coherence tomography as a tool for three-dimensional in vivo quantification of healthy epidermis: A pilot study.

Epidermal three-dimensional (3D) topography/quantification has not been completely characterized yet. The recently developed line-field confocal optical coherence tomography (LC-OCT) provides real-time, high-resolution, in-vivo 3D imaging of the skin. This pilot study aimed at quantifying epidermal metrics (epidermal thicknesses, dermal-epidermal junction [DEJ] undulation and keratinocyte number/shape/size) using 3D LC-OCT. For each study participant (8 female, skin-type-II, younger/older volunteers), seven body sites were imaged with LC-OCT. Epidermal metrics were calculated by segmentations and measurements assisted by artificial intelligence (AI) when appropriate. Thicknesses of epidermis/SC, DEJ undulation and keratinocyte nuclei volume varied across body sites. Evidence of keratinocyte maturation was observed in vivo: keratinocyte nuclei being small/spherical near the DEJ and flatter/elliptical near the skin surface. Skin microanatomy can be quantified by combining LC-OCT and AI. This technology could be highly relevant to understand aging processes and conditions linked to epidermal disorders. Future clinical/research applications are to be expected in this scenario.