ReviewSkin 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)
New Frontiers of Non-Invasive Detection in Scalp and Hair Diseases: A Review of the Application of Novel Detection Techniques.
Review in 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). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Emerging non-invasive tools for hair follicle assessment: a narrative review on hyperspectral and terahertz imaging.Lasers in medical science · 2026Review
- Rethinking pattern hair loss classification in the era of trichoscopy and artificial intelligence.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this paper is to review the current status and recent progress of the application of novel non-invasive testing technologies in the diagnosis, treatment, and care of scalp hair diseases in recent years, and to discuss their contribution to the improvement of scalp hair health management.
methodsThrough a literature review, the principles and current status of application of technologies such as trichoscopy, microbiological testing, scalp skin physiological function testing, hair physiological index testing, and hair product patch testing, and their specific roles in the management of scalp hair diseases were systematically sorted out.
resultsTrichoscopic intelligent analysis technology can quantitatively assess the hair growth status and provide an objective assessment of the efficacy of hair diseases; microbiological testing provides a laboratory reference for the rapid diagnosis of scalp diseases and clinical use of medication; scalp skin physiological function testing and hair physiological index testing are essential for a comprehensive assessment of the scalp environment and hair health. The patch test can effectively detect allergenic substances, which can help to safely select topical drugs and hair care products, avoid medical risks, and facilitate safety evaluation.
conclusionNovel non-invasive testing techniques provide powerful technical support for accurate diagnosis, personalized treatment, and safe care of scalp hair diseases, and show a broad application prospect. Future research should focus on improving the accuracy and detection efficiency of these techniques to meet the growing demand for scalp hair health management and to promote the development of this field in a more scientific and refined direction.
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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.