ArticleBiomedical optics express2021
Deep learning automated pathology in ex vivo microscopy.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- S1 guideline for imaging diagnostics for skin diseases.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2025Guideline
- Article
- The Diagnostic Accuracy of Ex Vivo Confocal Laser Scanning Microscopy for Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Mohs Micrographic Surgery: A Narrative Review of Current Practices, Emerging Trends, and Case-Based Insights.Advances in therapy · 2025Review
- [Ex vivo confocal laser scanning microscopy and artificial intelligence for skin tumors].Dermatologie (Heidelberg, Germany) · 2025Article
- [Digital pathology using ex vivo confocal laser scanning microscopy].Dermatologie (Heidelberg, Germany) · 2025Review
- Breast tissue imaging atlas using ultra-fast confocal microscopy to identify cancer lesions.Virchows Archiv : an international journal of pathology · 2025Article
- Perspective on the use of fluorescence molecular imaging for peripheral and deepJournal of biomedical optics · 2025Review
- New Instant Digital Pathology for EUS/EBUS Samples: The Last Advance in Bedside Diagnostics for Lung Carcinoma.Cancers · 2024Article
- Article
- High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.Journal of biophotonics · 2024Article
- Harnessing the power of optical microscopy for visualization and analysis of histopathological images.Biomedical optics express · 2023Review
- Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care.Scientific reports · 2023Article
- Article
- Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.Journal of biomedical optics · 2022Article
- [Artificial intelligence in ex vivo confocal laser scanning microscopy].Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete · 2021Review
- Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.Cancers · 2021Article
- Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 2 countries.
Funding
Abstract
Standard histopathology is currently the gold standard for assessment of margin status in Mohs surgical removal of skin cancer. Ex vivo confocal microscopy (XVM) is potentially faster, less costly and inherently 3D/digital compared to standard histopathology. Despite these advantages, XVM use is not widespread due, in part, to the need for pathologists to retrain to interpret XVM images. We developed artificial intelligence (AI)-driven XVM pathology by implementing algorithms that render intuitive XVM pathology images identical to standard histopathology and produce automated tumor positivity maps. XVM images have fluorescence labeling of cellular and nuclear biology on the background of endogenous (unstained) reflectance contrast as a grounding counter-contrast. XVM images of 26 surgical excision specimens discarded after Mohs micrographic surgery were used to develop an XVM data pipeline with 4 stages: flattening, colorizing, enhancement and automated diagnosis. The first two stages were novel, deterministic image processing algorithms, and the second two were AI algorithms. Diagnostic sensitivity and specificity were calculated for basal cell carcinoma detection as proof of principal for the XVM image processing pipeline. The resulting diagnostic readouts mimicked the appearance of histopathology and found tumor positivity that required first collapsing the confocal stack to a 2D image optimized for cellular fluorescence contrast, then a dark field-to-bright field colorizing transformation, then either an AI image transformation for visual inspection or an AI diagnostic binary image segmentation of tumor obtaining a diagnostic sensitivity and specificity of 88% and 91% respectively. These results show that video-assisted micrographic XVM pathology could feasibly aid margin status determination in micrographic surgery of skin cancer.
Identifiers
What OpenQuestion holds
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.