Reflectance confocal microscopy in pediatric dermatology: A state‐of‐the‐art review

2021 ◽  
Author(s):  
Randy F. Ko ◽  
Aimee C. Smidt ◽  
John R. Durkin
Keyword(s):  
Confocal Microscopy ◽  
State Of The Art ◽  
Reflectance Confocal Microscopy ◽  
Pediatric Dermatology

scholarly journals Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alican Bozkurt ◽  
Kivanc Kose ◽  
Jaume Coll-Font ◽  
Christi Alessi-Fox ◽  
Dana H. Brooks ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
State Of The Art ◽  
Reflectance Confocal Microscopy ◽  
Convolutional Networks ◽  
Non Invasive ◽  
Extensive Training ◽  
Fold Reduction ◽  
Previous State ◽  
Microscopy Images ◽  
Field View

AbstractReflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4–5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved $$88.07\%$$ 88.07 % classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.

scholarly journals Reflectance confocal microscopy in the diagnosis of pigmented macules of the face: differential diagnosis and margin definition

2019 ◽  
Vol 18 (5) ◽  
pp. 963-969 ◽  
Author(s):  
Francesca Farnetani ◽  
Marco Manfredini ◽  
Johanna Chester ◽  
Silvana Ciardo ◽  
Salvador Gonzalez ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Confocal Microscopy ◽  
State Of The Art ◽  
Correct Diagnosis ◽  
The State ◽  
Reflectance Confocal Microscopy ◽  
Diagnostic Algorithms ◽  
Diagnosis And Management ◽  
The Face ◽  
Standardized Terminology

Here we review the state-of-the-art of RCM morphologic descriptors, standardized terminology, and diagnostic algorithms for the assessment of pigmented macules of the face, for their correct diagnosis and management.

Usefulness of in vivo reflectance confocal microscopy in Morbihan syndrome

2021 ◽  
Author(s):  
Arianna Rizzo ◽  
Diletta Fiorani ◽  
Laura Lazzeri ◽  
Paolo Taddeucci ◽  
Pietro Rubegni ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Reflectance Confocal Microscopy

scholarly journals In vivo reflectance confocal microscopy for the diagnosis of scabies

2021 ◽  
Vol 14 (1) ◽  
pp. e240507
Author(s):  
Mihai Lupu ◽  
Vlad Mihai Voiculescu ◽  
Cristina Vajaitu ◽  
Olguta Anca Orzan
Keyword(s):  
Confocal Microscopy ◽  
Reflectance Confocal Microscopy

Monitoring vulvar melanoma response to combined immunotherapy and radiotherapy with in vivo reflectance confocal microscopy

2021 ◽  
Author(s):  
Cristian Navarrete‐Dechent ◽  
Miguel Cordova ◽  
Saud Aleissa ◽  
Alexander Shoushtari ◽  
Travis J. Hollmann ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Reflectance Confocal Microscopy ◽  
Vulvar Melanoma ◽  
Combined Immunotherapy

Evaluating residual melanocytic atypia in a post‐excision scar using in vivo reflectance confocal microscopy

2021 ◽  
Author(s):  
Samavia Khan ◽  
Nadiya Chuchvara ◽  
Jennifer Cucalon ◽  
Attiya Haroon ◽  
Babar Rao
Keyword(s):  
Confocal Microscopy ◽  
Reflectance Confocal Microscopy
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