Line-field confocal optical coherence tomography: Characteristic hints for the diagnosis of scarring alopecia due to lupus erythematodes: A preliminary study.

INTRODUCTION: Lupus erythematosus (LE) is an inflammatory autoimmune disease, that can affect the skin to varying degree. In particular, discoid LE (DLE) and the rare form of lupus panniculitis/profundus are associated with scarring alopecia. The heterogeneity of the clinical, dermatoscopic, and histologic presentation poses a major challenge to the clinician in the diagnosis and differential diagnosis of other forms of scarring alopecia. OBJECTIVE: While noninvasive imaging techniques using optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) have proven to be helpful in the diagnosis of scarring alopecia in the context of LE, this study aimed to investigate line-field confocal OCT (LC-OCT) to identify characteristic features of cicatricial alopecia in LE. METHODS: Fifteen patients with cicatricial alopecia in LE were included and the most affected/inflamed areas of the scalp were prospectively examined. In analogy to histopathology and previously reported criteria in RCM, all images were evaluated according to seven established criteria and underwent descriptive analyses. RESULTS: LC-OCT revealed characteristic features of cicatricial alopecia, such as lymphocytic interface dermatitis (14/15; 93.3%) and basal cell vacuolization (13/15; 86.7%). The most impressive feature was the occurrence of prominent hyperreflective fibers in 14/15 patients (93.3%). CONCLUSION: LC-OCT imaging can noninvasively detect morphologic criteria such as lymphocytic and vacuolar interface dermatitis of cicatricial alopecia due to LE. In particular, the presence of hyperreflective collagen fibers appears to be a characteristic easily recognizable feature that may facilitate differential diagnosis with other forms of cicatricial alopecia. Further studies are mandatory to differentiate other forms of scarring alopecia.

Line-field confocal optical coherence tomography, a novel non-invasive tool for the diagnosis of onychomycosis.

BACKGROUND AND OBJECTIVES: Onychomycosis is common and important to distinguish from other nail diseases. Rapid and accurate diagnosis is necessary for optimal patient treatment and outcome. Non-invasive diagnostic tools have increasing potential for nail diseases including onychomycosis. This study evaluated line-field confocal optical coherence tomography (LC-OCT) as a rapid non-invasive tool for diagnosing onychomycosis as compared to confocal laser scanning microscopy (CLSM), optical coherence tomography (OCT), and conventional methods. PATIENTS AND METHODS: In this prospective study 86 patients with clinically suspected onychomycosis and 14 controls were examined using LC-OCT, OCT, and CLSM. KOH-preparation, fungal culture, PCR, and histopathology were used as comparative conventional methods. RESULTS: LC-OCT had the highest sensitivity and negative predictive value of all methods used, closely followed by PCR and OCT. Specificity and positive predictive value of LC-OCT were as high as with CLSM, while OCT scored much lower. The gold standard technique, fungal culture, showed the lowest sensitivity and negative predictive value. Only PCR and culture allowed species differentiation. CONCLUSIONS: LC-OCT enables quick and non-invasive detection of onychomycosis, with advantages over CLSM and OCT, and similar diagnostic accuracy to PCR but lacking species differentiation. For accurate nail examination, LC-OCT requires well-trained and experienced operators.

Artificial intelligence-based PRO score assessment in actinic keratoses from LC-OCT imaging using Convolutional Neural Networks.

BACKGROUND AND OBJECTIVES: The histological PRO score (I-III) helps to assess the malignant potential of actinic keratoses (AK) by grading the dermal-epidermal junction (DEJ) undulation. Line-field confocal optical coherence tomography (LC-OCT) provides non-invasive real-time PRO score quantification. From LC-OCT imaging data, training of an artificial intelligence (AI), using Convolutional Neural Networks (CNNs) for automated PRO score quantification of AK in vivo may be achieved. PATIENTS AND METHODS: CNNs were trained to segment LC-OCT images of healthy skin and AK. PRO score models were developed in accordance with the histopathological gold standard and trained on a subset of 237 LC-OCT AK images and tested on 76 images, comparing AI-computed PRO score to the imaging experts' visual consensus. RESULTS: Significant agreement was found in 57/76 (75%) cases. AI-automated grading correlated best with the visual score for PRO II (84.8%) vs. PRO III (69.2%) vs. PRO I (66.6%). Misinterpretation occurred in 25% of the cases mostly due to shadowing of the DEJ and disruptive features such as hair follicles. CONCLUSIONS: The findings suggest that CNNs are helpful for automated PRO score quantification in LC-OCT images. This may provide the clinician with a feasible tool for PRO score assessment in the follow-up of AK.

Dietary Patterns in Acne and Rosacea Patients-A Controlled Study and Comprehensive Analysis.

As the relationship between exposome factors and inflammatory skin diseases is gaining increasing attention, the objective of this study was to investigate dietary patterns among acne and rosacea patients and to establish the disease risk attributable to nutrition. In this cross-sectional, controlled study, patients' dietary habits were assessed via subjective ratings of beneficial and trigger foods, followed by standardized food frequency surveys (FFS). Scores for disease-specific risk stratification based on dietary habits were proposed. Clinical assessments, dermatologic examinations, and laboratory analyses were performed. A total of 296 patients (acne group (AG) n = 120, control group (ACG) n = 32; rosacea group (RG) n = 105, control group (RCG) n = 39) were included. The significant impact of diet on disease severity was self-reported by 80.8% of the AG and 70.5% of the RG. Leading dietary triggers were found in both groups, while beneficial food items were identified more clearly by the AG. FFS revealed significant dietary differences between the AG, RG, and control groups. Disease-specific scores showed greater precision for acne (odds ratio 14.5 AG, 5.5 RG). The AG had higher insulin-like growth factor (IGF)-1 levels correlating with dairy intake (p = 0.006). Overall, this study underlines the influence of diet on acne and rosacea, providing valuable disease-specific scores for dietary risk stratification. Consuming vegetables, legumes, oily fish, olive oil, and nuts, and limiting meat, cheese, and alcohol appear to be beneficial for both acne and rosacea. Future studies can build on these data to further improve preventive and therapeutic strategies.

Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation.

Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction's (DEJ's) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts' visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients.

NGLY1 deficiency: estimated incidence, clinical features, and genotypic spectrum from the NGLY1 Registry.

PURPOSE: NGLY1 Deficiency is an ultra-rare, multisystemic disease caused by biallelic pathogenic NGLY1 variants. The aims of this study were to (1) characterize the variants and clinical features of the largest cohort of NGLY1 Deficiency patients reported to date, and (2) estimate the incidence of this disorder. METHODS: The Grace Science Foundation collected genotypic data from 74 NGLY1 Deficiency patients, of which 37 also provided phenotypic data. We analyzed NGLY1 variants and clinical features and estimated NGLY1 disease incidence in the United States (U.S.). RESULTS: Analysis of patient genotypes, including 10 previously unreported NGLY1 variants, showed strong statistical enrichment for missense variants in the transglutaminase-like domain of NGLY1 (p < 1.96E-11). Caregivers reported global developmental delay, movement disorder, and alacrima in over 85% of patients. Some phenotypic differences were noted between males and females. Regression was reported for all patients over 14 years old by their caregivers. The calculated U.S. incidence of NGLY1 Deficiency was ~ 12 individuals born per year. CONCLUSION: The estimated U.S. incidence of NGLY1 indicates the disease may be more common than the number of patients reported in the literature suggests. Given the low frequency of most variants and proportion of compound heterozygotes, genotype/phenotype correlations were not distinguishable.