Evidence map›Paper›PMID 35752277›Full record

ArticleJournal of the American Academy of Dermatology2023

Real-time, in vivo skin cancer triage by laser-induced plasma spectroscopy combined with a deep learning-based diagnostic algorithm.

Sung Hyun Pyun, Wanki Min, Boncheol Goo, Samuel Seit, Anthony Azzi, David Yu-Shun Wong, Girish S Munavalli, Chang-Hun Huh, Chong-Hyun Won, Minsam Ko

Open access · hybridAbstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Journal of the American Academy of Dermatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 3 pooled it
6.6field-weighted citation impact, top 4% 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

8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 26 citations in OpenAlex.

  1. S1 guideline for imaging diagnostics for skin diseases.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG · 2025
    Guideline
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 3 countries.

Sung Hyun PyunR&D Center, Speclipse, Inc, Sunnyvale, California. Electronic address: ceo@speclipse.com.
Wanki MinR&D Center, Speclipse, Inc, Sunnyvale, California.
Boncheol GooR&D Center, Speclipse, Inc, Sunnyvale, California.
Samuel SeitThe Skin Cancer & Cosmetic Clinic, Neutral Bay, New South Wales, Australia.
Anthony AzziNewcastle Skin Check, Charlestown, New South Wales, Australia.
David Yu-Shun WongEastern Suburbs Dermatology, Bondi Junction, New South Wales, Australia.
Girish S MunavalliDermatology, Laser & Vein Specialists of the Carolinas, Charlotte, North Carolina.
Chang-Hun HuhDepartment of Dermatology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea.
Chong-Hyun WonDepartment of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Minsam KoDepartment of Human-Computer Interaction, Hanyang University, Seoul, South Korea.
Dermatology, Laser & Vein Specialists of the Carolinas · USHanyang University · KRSeoul National University Bundang Hospital · KRSouth Eastern Sydney Local Health District · AUUlsan College · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough various skin cancer detection devices have been proposed, most of them are not used owing to their insufficient diagnostic accuracies. Laser-induced plasma spectroscopy (LIPS) can noninvasively extract biochemical information of skin lesions using an ultrashort pulsed laser.

objectiveTo investigate the diagnostic accuracy and safety of real-time noninvasive in vivo skin cancer diagnostics utilizing nondiscrete molecular LIPS combined with a deep neural network (DNN)-based diagnostic algorithm.

methodsIn vivo LIPS spectra were acquired from 296 skin cancers (186 basal cell carcinomas, 96 squamous cell carcinomas, and 14 melanomas) and 316 benign lesions in a multisite clinical study. The diagnostic performance was validated using 10-fold cross-validations.

resultsThe sensitivity and specificity for differentiating skin cancers from benign lesions using LIPS and the DNN-based algorithm were 94.6% (95% CI: 92.0%-97.2%) and 88.9% (95% CI: 85.5%-92.4%), respectively. No adverse events, including macroscopic or microscopic visible marks or pigmentation due to laser irradiation, were observed. LIMITATIONS: The diagnostic performance was evaluated using a limited data set. More extensive clinical studies are needed to validate these results.

conclusionsThis LIPS system with a DNN-based diagnostic algorithm is a promising tool to distinguish skin cancers from benign lesions with high diagnostic accuracy in real clinical settings.

Indexed as

Basal Cell CarcinomaDeep LearningSkin NeoplasmsAlgorithmsHumansSensitivity and SpecificitySpectrum AnalysisTriagebasal cell carcinomadeep neural network (DNN)laser-induced plasma spectroscopy (LIPS)melanomaskin cancer diagnosissquamous cell carcinoma

Identifiers

PMID35752277
OpenAlexW4283329415

What OpenQuestion holds

Textmetadata
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.