ArticleThe Journal of investigative dermatology2020
Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.
Article in The Journal of investigative dermatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- Article
- Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.Diagnostics (Basel, Switzerland) · 2023Review
- Utilizing deep learning for dermal matrix quality assessment on in vivo line-field confocal optical coherence tomography images.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Article
- MxIF Q-score: Biology-Informed Quality Assurance for Multiplexed Immunofluorescence Imaging.Medical optical imaging and virtual microscopy image analysis : first International Workshop, MOVI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. MOVI (Workshop) (1st : 2022 : Singapore) · 2022Article
- Exploring reflectance confocal microscopy as a non-invasive diagnostic tool for genital lichen sclerosus.Experimental and therapeutic medicine · 2022Article
- Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm.The Journal of investigative dermatology · 2022Article
- In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.Journal of biomedical optics · 2022Article
- Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology.Journal of clinical medicine · 2022Review
- Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.The Journal of investigative dermatology · 2022Article
- A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology.Lasers in surgery and medicine · 2021Review
- Automated Extraction of Skin Wound Healing Biomarkers From In Vivo Label-Free Multiphoton Microscopy Using Convolutional Neural Networks.Lasers in surgery and medicine · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Automated Quantitative Analysis of Wound Histology Using Deep-Learning Neural Networks.The Journal of investigative dermatology · 2021Article
- Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels.Scientific reports · 2021Article
- Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net).Medical image analysis · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is expanding from real-time diagnosis at the bedside to include a capture, store, and forward model with image interpretation and diagnosis occurring offsite, similar to radiology. As the patient may no longer be present at the time of image interpretation, quality assurance is key during image acquisition. Herein, we introduce a quality assurance process by means of automatically quantifying diagnostically uninformative areas within the lesional area by using RCM and coregistered dermoscopy images together. We trained and validated a pixel-level segmentation model on 117 RCM mosaics collected by international collaborators. The model delineates diagnostically uninformative areas with 82% sensitivity and 93% specificity. We further tested the model on a separate set of 372 coregistered RCM-dermoscopic image pairs and illustrate how the results of the RCM-only model can be improved via a multimodal (RCM + dermoscopy) approach, which can help quantify the uninformative regions within the lesional area. Our data suggest that machine learning-based automatic quantification offers a feasible objective quality control measure for RCM imaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.