Evidence map›Paper›PMID 39622207›Full record

ArticleSkin pharmacology and physiology2024

In vivo Study to Evaluate an Intelligent Algorithm for Time Efficient Detection of Malignant Melanoma Using Dermatofluoroscopy.

Karl Weihmann, Johannes Schleusener, Thomas K Eigentler, Franziska C Ghoreschi, Rose K C Moritz, Lukasz Szyc, Eduard Dronnik, Martina C Meinke

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Article in Skin pharmacology and physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Karl WeihmannDepartment of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Johannes SchleusenerDepartment of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Thomas K EigentlerDepartment of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Franziska C GhoreschiDepartment of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Rose K C MoritzDepartment of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Lukasz SzycMagnosco GmbH, Berlin, Germany.
Eduard DronnikMagnosco GmbH, Berlin, Germany.
Martina C MeinkeDepartment of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDermatofluoroscopy is an optical noninvasive method of melanoma/nevus differentiation that has shown 89% sensitivity and 45% specificity in clinical trials, but long measurement duration hinders clinical use. An intelligent algorithm was developed to shorten the measurement time without compromising its diagnostic accuracy. It uses dermoscopic images of the skin lesions to be measured to select measurement points based on the assessment of color values.

methods27 patients with a total of 29 lesions suggestive of cutaneous melanoma were included in a clinical study and measured with both methods, conventional dermatofluoroscopy and the newly developed intelligent algorithm. The results were compared to the independent findings of two histopathologists to evaluate diagnostic accuracy and time saved.

resultsThere was a median reduction of measurement points from 265 to 158 (40%). Meanwhile, the intelligent algorithm showed a higher diagnostic accuracy than conventional dermatofluoroscopy (area under the curve of 72% vs. 63%).

conclusionThe intelligent algorithm did not perform inferior to the conventional method while saving 40% of time. However, measurement times remain long compared to other noninvasive methods of diagnosing malignant melanoma. Further studies are needed to evaluate clinical suitability.

introductionDermatofluoroscopy is an optical noninvasive method of melanoma/nevus differentiation that has shown 89% sensitivity and 45% specificity in clinical trials, but long measurement duration hinders clinical use. An intelligent algorithm was developed to shorten the measurement time without compromising its diagnostic accuracy. It uses dermoscopic images of the skin lesions to be measured to select measurement points based on the assessment of color values.

methods27 patients with a total of 29 lesions suggestive of cutaneous melanoma were included in a clinical study and measured with both methods, conventional dermatofluoroscopy and the newly developed intelligent algorithm. The results were compared to the independent findings of two histopathologists to evaluate diagnostic accuracy and time saved.

resultsThere was a median reduction of measurement points from 265 to 158 (40%). Meanwhile, the intelligent algorithm showed a higher diagnostic accuracy than conventional dermatofluoroscopy (area under the curve of 72% vs. 63%).

conclusionThe intelligent algorithm did not perform inferior to the conventional method while saving 40% of time. However, measurement times remain long compared to other noninvasive methods of diagnosing malignant melanoma. Further studies are needed to evaluate clinical suitability.

Indexed as

AlgorithmsDermoscopyMelanomaSkin NeoplasmsAdultAgedCutaneous Malignant MelanomaFemaleHumansMaleMiddle AgedSensitivity and SpecificityColor clusteringDermatofluoroscopyMachine learningMalignant melanoma

Identifiers

PMID39622207
PMCPMC11797919

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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.