Evidence map›Paper›PMID 40900360›Full record

SynthesisDermatologie (Heidelberg, Germany)2025

[Line-field confocal optical coherence tomography and artificial intelligence].

Oliver Mayer, Hanna Wirsching, Janis Thamm, Julia Welzel, Sandra Schuh

Abstract readEnglish AbstractMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Dermatologie (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Oliver MayerKlinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg Medizincampus Süd, Sauerbruchstr. 6, 86179, Augsburg, Deutschland.
Hanna WirschingKlinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg Medizincampus Süd, Sauerbruchstr. 6, 86179, Augsburg, Deutschland.
Janis ThammKlinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg Medizincampus Süd, Sauerbruchstr. 6, 86179, Augsburg, Deutschland.
Julia WelzelKlinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg Medizincampus Süd, Sauerbruchstr. 6, 86179, Augsburg, Deutschland.
Sandra SchuhKlinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg Medizincampus Süd, Sauerbruchstr. 6, 86179, Augsburg, Deutschland. sandra.schuh@uk-augsburg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe diagnosis of actinic keratosis (AK), basal cell carcinoma (BCC), and psoriasis may present a challenge in everyday dermatological practice. Clinical and dermoscopic assessments often reach their limits, especially in ambiguous or anatomically difficult-to-access lesions. Biopsies are often impractical, and objective tools for treatment monitoring are lacking.

objectiveTo investigate the potential of line-field confocal optical coherence tomography (LC-OCT) combined with artificial intelligence (AI) for noninvasive diagnosis, differentiation, and longitudinal monitoring. MATERIALS AND

methodsAnalysis and evaluation of LC-OCT imaging data from various studies. Application of AI-based algorithms for the detection of vascular patterns, epidermal changes, and BCC identification using heatmap-supported decision tools. RESULTS AND DISCUSSION: The LC-OCT enables high-resolution, real-time visualization of dermoepidermal structures as well as vascular architecture. In combination with AI, objective parameters such as PRO score, atypia, and vascular morphology can be quantified and monitored over time. AI-assisted diagnostics significantly improve diagnostic accuracy-especially in BCC and among less experienced users. However, implementation requires clear guidelines, standardization, and well-defined legal and ethical frameworks.

conclusionThe LC-OCT combined with AI is a promising tool for more precise, standardized, and personalized dermatological diagnostics. Particularly in AK, BCC, and psoriasis, it has the potential to enhance care, reduce the need for invasive procedures, and provide novel insights into tumor and inflammation biology.

Indexed as

Artificial IntelligenceBasal Cell CarcinomaKeratosis, ActinicPsoriasisSkin NeoplasmsTomography, Optical CoherenceAlgorithmsHumansSkinActinic keratosisBasal cell carcinomaDiagnostic accuracyNoninvasive diagnosticsPsoriasis

Identifiers

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.