Swept-Source Optical Coherence Tomography-Supervised Biopsy.

BACKGROUND: Currently, only skin biopsy can provide definitive histological confirmation for the diagnosis of skin diseases. To improve the diagnostic accuracy and to assist the dermatologist, various imaging techniques have been added to the examination of skin. Among all these techniques, the recent advances in optical coherence tomography (OCT) have made it possible to image the skin up to 2 millimeters in depth. OBJECTIVE: To testify the feasibility of OCT imaging in skin biopsy, the authors investigated the OCT imaging for real-time visualization of needle insertion and punch biopsy techniques in both a tissue phantom and biological tissue. MATERIALS AND METHODS: A swept-source OCT with 1,305-nm central wavelength was used in this study. The euthanized mouse was used for real-time visualization of needle insertion. A gelatin phantom with India ink was used to demonstrate the punch biopsy using OCT. RESULTS: Optical coherence tomography can provide guidance for skin injections as well as real-time imaging to assist in the performance of punch biopsy. CONCLUSION: Optical coherence tomography holds potential not only as a diagnostic tool in dermatology. It can also allow for visualization for more accurate drug delivery, and noninvasively assess the response to treatment.

Semi-automated localization of dermal epidermal junction in optical coherence tomography images of skin.

Identifying the location of the dermal epidermal junction (DEJ) in skin images is essential in several clinical applications of dermatology such as epidermal thickness determination in healthy versus unhealthy skins, such as basal cell carcinoma. Optical coherence tomography (OCT) facilitates the visual detection of DEJ in vivo. However, due to the granular texture of speckle and a low contrast between dermis and epidermis, a skin border detection method is required for DEJ localization. Current DEJ algorithms work well for skins with a visible differentiable epidermal layer but not for the skins of different body sites. In this paper, we present a semi-automated DEJ localization algorithm based on graph theory for OCT images of skin. The proposed algorithm is performed in an interactive framework by a graphical representation of an attenuation coefficient map through a uniform-cost search method. For border thinning, a fuzzy-based nonlinear smoothing technique is used. For evaluation, the DEJ detection method is used by several experts, and the results are compared with manual segmentation. The mean thickness error between the proposed algorithm and the experts' opinion in the Bland-Altman plot is computed as 14 µm; this is comparable to the resolution of the OCT. The results suggest that the proposed image processing method successfully detects DEJ.

Meta-Analysis of Genome-Wide Association Studies Reveals Genetic Mechanisms of Supraventricular Arrhythmias.

BACKGROUND: Substantial data support a heritable basis for supraventricular tachycardias, but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood. We sought to identify genetic loci associated with atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular accessory pathways or atrioventricular reciprocating tachycardia (AVAPs/AVRT). METHODS: We performed multiancestry meta-analyses of genome-wide association studies to identify genetic loci for AVNRT (4 studies) and AVAP/AVRT (7 studies). We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and examining prior genome-wide association studies. RESULTS: Analyses comprised 2384 AVNRT cases and 106 489 referents, and 2811 AVAP/AVRT cases and 1,483 093 referents. We identified 2 significant loci for AVNRT, which implicate NKX2-5 and TTN as disease susceptibility genes. A transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of NKX2-5 and AVNRT. We identified 3 significant loci for AVAP/AVRT, which implicate SCN5A, SCN10A, and TTN/CCDC141. Variant associations at several loci have been previously reported for cardiac phenotypes, including atrial fibrillation, stroke, Brugada syndrome, and electrocardiographic intervals. CONCLUSIONS: Our findings highlight gene regions associated with ion channel function (AVAP/AVRT), as well as cardiac development and the sarcomere (AVAP/AVRT and AVNRT) as important potential effectors of supraventricular tachycardia susceptibility.

Evaluation of Research Capacity and Culture of Health Professionals Working with Women, Children and Families at an Australian Public Hospital: A Cross Sectional Observational Study.

BACKGROUND: There is limited evidence for use of the Research Capacity and Culture tool across multidisciplinary health professionals. We explored using the Research Capacity and Culture tool among multidisciplinary health professionals at an Australian secondary hospital. METHODS: A cross-sectional observational study where online and paper-based surveys of the Research Capacity and Culture tool were disseminated between November 2020 and January 2021. Descriptive analyses of demographic variables and Likert scale items were summarized using median and inter-quartile ranges. Differences between organization, team and individual domains were checked using a Friedman test. Post-hoc Wilcoxon signed rank tests determined specific differences between domains. RESULTS: Seventy-six multidisciplinary health professionals (female, 89.3%) reported overall perceptions of research success/skills highest in the organization (median 6), followed by the team (median 5) and individual domains (median 3.5). Only 21.3% agreed that research activities were a part of their role description. Mean scores across professions were highest for Medicine (5.47), Midwifery (4.52), Nursing (4.47) and Allied Health (3.56), respectively, for the team domain. Individual domain scores across all professions were below 50%. Commonly reported barriers to research were "lack of time for research," "other work roles taking priority" and "a lack of skill." "Developing skills" was the most common personal motivator. CONCLUSION: Multidisciplinary health professionals reported the highest overall perception of research success/skills in the organization domain. Medical health professionals perceived research success/skills highest compared to nursing, midwifery and allied health professionals.

Learnable despeckling framework for optical coherence tomography images.

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.

Optical Coherence Tomography Technology and Quality Improvement Methods for Optical Coherence Tomography Images of Skin: A Short Review.

Optical coherence tomography (OCT) delivers 3-dimensional images of tissue microstructures. Although OCT imaging offers a promising high-resolution method, OCT images experience some artifacts that lead to misapprehension of tissue structures. Speckle, intensity decay, and blurring are 3 major artifacts in OCT images. Speckle is due to the low coherent light source used in the configuration of OCT. Intensity decay is a deterioration of light with respect to depth, and blurring is the consequence of deficiencies of optical components. In this short review, we summarize some of the image enhancement algorithms for OCT images which address the abovementioned artifacts.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms.

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.