Evidence map›Paper›PMID 34795202›Full record

ArticleLight, science & applications2021

Biopsy-free in vivo virtual histology of skin using deep learning.

Jingxi Li, Jason Garfinkel, Xiaoran Zhang, Di Wu, Yijie Zhang, Kevin de Haan, Hongda Wang, Tairan Liu, Bijie Bai, Yair Rivenson and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Light, science & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed
10.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

52 citing papers in PubMed, 106 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. AI-Powered Spectral Imaging for Virtual Pathology Staining.Bioengineering (Basel, Switzerland) · 2025
    Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 2 countries.

Jingxi Li *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0001-6595-8680
Jason Garfinkel *Dermatology and Laser Centre, Studio City, CA, 91604, USA.
Xiaoran ZhangElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Di WuComputer Science Department, University of California, Los Angeles, CA, 90095, USA.
Yijie ZhangElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Kevin de HaanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0002-7802-3857
Hongda WangElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0003-0110-5624
Tairan LiuElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Bijie BaiElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Yair RivensonElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Gennady RubinsteinDermatology and Laser Centre, Studio City, CA, 91604, USA. grmd@sbcglobal.net.
Philip O ScumpiaDivision of Dermatology, University of California, Los Angeles, CA, 90095, USA. pscumpia@mednet.ucla.edu.
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X
California NanoSystems Institute · USUniversity of California, Los Angeles · USCentro Laser · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

Identifiers

PMID34795202
PMCPMC8602311
OpenAlexW3214082102

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.