Evidence map›Paper›PMID 39511184›Full record

ArticleNPJ systems biology and applications2024

Reliable and easy-to-use calculating tool for the Nail Psoriasis Severity Index using deep learning.

Hiroto Horikawa, Keiji Tanese, Naoki Nonaka, Jun Seita, Masayuki Amagai, Masataka Saito

Abstract read
In one paragraph

Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

6 authors.

Hiroto HorikawaDepartment of Dermatology, Keio University School of Medicine. 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Keiji TaneseDepartment of Dermatology, Keio University School of Medicine. 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Naoki NonakaAdvanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
Jun SeitaAdvanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.ORCID 0000-0002-3008-3615
Masayuki AmagaiDepartment of Dermatology, Keio University School of Medicine. 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
Masataka SaitoDepartment of Dermatology, Keio University School of Medicine. 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. masataka@keio.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since nail psoriasis restricts the patient's daily activities, therapeutic intervention based on reliable and reproducible evaluation is critical. The Nail Psoriasis Severity Index (NAPSI) is a validated scoring tool, but its usefulness is limited by interobserver variability. This study aimed to develop a reliable and accurate NAPSI scoring tool using deep learning. The tool "NAPSI calculator" includes two parts: nail detection from images and NAPSI scoring. NAPSI was annotated by nine nail experts who are board-certified dermatologists with sufficient experience in a specialized clinic for nail diseases. In the final test set, the "NAPSI calculator" correctly located 137/138 nails and scored NAPSI with higher accuracy than the compared six non-board-certified residents: 83.9% vs 65.7%; P = 0.008 and four board-certified non-nail expert dermatologists: 83.9% vs 73.0%; P = 0.005. The "NAPSI calculator" can be readily used in a clinical situation, contributing to raising the medical practice level for nail psoriasis.

Indexed as

Deep LearningNail DiseasesPsoriasisSeverity of Illness IndexAdultFemaleHumansMaleMiddle AgedNailsObserver VariationReproducibility of Results

Identifiers

PMID39511184
PMCPMC11544089

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Registered trials

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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.