Seminars in cutaneous medicine and surgery最新文献

In the past decade, machine learning and artificial intelligence have made significant advancements in pattern analysis, including speech and natural language processing, image recognition, object detection, facial recognition, and action categorization. Indeed, in many of these applications, accuracy has reached or exceeded human levels of performance. Subsequently, a multitude of studies have begun to examine the application of these technologies to health care, and in particular, medical image analysis. Perhaps the most difficult subdomain involves skin imaging because of the lack of standards around imaging hardware, technique, color, and lighting conditions. In addition, unlike radiological images, skin image appearance can be significantly affected by skin tone as well as the broad range of diseases. Furthermore, automated algorithm development relies on large high-quality annotated image data sets that incorporate the breadth of this circumstantial and diagnostic variety. These issues, in combination with unique complexities regarding integrating artificial intelligence systems into a clinical workflow, have led to difficulty in using these systems to improve sensitivity and specificity of skin diagnostics in health care networks around the world. In this article, we summarize recent advancements in machine learning, with a focused perspective on the role of public challenges and data sets on the progression of these technologies in skin imaging. In addition, we highlight the remaining hurdles toward effective implementation of technologies to the clinical workflow and discuss how public challenges and data sets can catalyze the development of solutions.

Advancements in smartphone technologies and the use of specialized health care applications offer an exciting new era to promote melanoma awareness to the public and improve education and prevention strategies. These applications also afford an opportunity to power meaningful research aimed at improving image diagnostics and early melanoma detection. Here, we summarize our experience associated with developing and managing the implementation of MoleMapper™, a research-based application that not only provides an efficient way for users to digitally track images of moles and facilitate skin self-examinations but also provides a platform to crowdsource research participants and the curation of mole images in efforts to advance melanoma research. Obtaining electronic consent, safeguarding participant data, and employing a framework to ensure collection of meaningful data represent a few of the inherent difficulties associated with orchestrating such a wide-scale research enterprise. In this review, we discuss strategies to overcome these and other challenges leading to the implementation of MoleMapper™.

Atypical fibroxanthoma (AFX) is a dermal spindle-cell sarcoma that is considered a superficial and clinically benign presentation of pleomorphic dermal sarcoma, malignant fibrous histiocytoma, and undifferentiated pleomorphic sarcoma. AFX appears clinically as a discrete red or pink nodule or papule, most commonly on the head and neck region of sun-damaged elderly patients. Histologic findings on routine hematoxylin and eosin staining reveal spindle-shaped, large, and pleomorphic tumor cells throughout the dermis. Immunohistochemistry is not specific for AFX, and the diagnosis is generally one of exclusion. AFX is best treated by complete surgical excision, with Mohs micrographic surgery considered the treatment of choice. Metastasis rarely occurs, but there is a high rate of local recurrence, especially in patients who are immunosuppressed.

In this chapter, we present the use of whole slide imaging (WSI) and dermoscopy in the field of dermatology. Image digitization has allowed for increasing computer-assisted clinical decision-making. An introduction to common digital imaging data sources such as WSI and dermoscopy is provided. We also review some commonly used image quantification methods and their potential applications in dermatology. Finally, we review how machine learning approaches utilize novel large dermatology image datasets.

Bioinformatics uses computationally intensive approaches to make sense of complex biological data sets. Here we review the role of bioinformatics in 3 areas of biology: genetics, transcriptomics, and microbiomics. Examples of bioinformatics in each area are given with respect to psoriasis and psoriatic arthritis, related inflammatory disorders at the forefront of bioinformatic research in dermatology. While bioinformatic technologies and analyses have traditionally been developed and deployed in siloes, the field of integrative omics is on the horizon. Powered by the advent of machine learning, bioinformatic integration of large data sets has the potential to dramatically revolutionize our knowledge of pathogenetic mechanisms and therapeutic targets.

Skinomics is a field of bioinformatics applied specifically to skin biology and, by extension, to dermatology. Skinomics has been expanding into extensive genome-wide association studies, eg, of psoriasis, proteomics, lipidomics, metabolomics, metagenomics, and the studies of the microbiome. Here, the current state of the field of transcriptomics is reviewed, including the studies of the gene expression in human skin under several healthy and disease conditions. Specifically, transcriptional studies of epidermal differentiation, skin aging, effects of cytokines, inflammation with emphases on psoriasis and atopic dermatitis, and wound healing are reviewed. The transition from microarrays to NextGen sequencing is noted and potential future directions suggested.

Pharmacogenomics aims to associate human genetic variability with differences in drug phenotypes in order to tailor drug treatment to individual patients. The massive amount of genetic data generated from large cohorts of patients with variable drug phenotypes have led to advances in this field. Understanding the application of pharmacogenomics in dermatology could inform clinical practice and provide insight for future research. The Pharmacogenomics Knowledge Base and the Clinical Pharmacogenetics Implementation Consortium are among the resources to help clinicians and researchers navigate the many gene-drug associations that have already been discovered. The implementation of clinical pharmacogenomics within health care systems remains an area of ongoing development. This review provides an introduction to the field of pharmacogenomics and to current pharmacogenomics resources using examples of gene-drug associations relevant to the field of dermatology.