利用深度学习为指甲银屑病严重程度指数提供可靠、易用的计算工具。

IF 3.5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

NPJ Systems Biology and Applications Pub Date : 2024-11-07 DOI:10.1038/s41540-024-00458-x

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

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引用次数: 0

摘要

由于指甲银屑病会限制患者的日常活动，因此基于可靠、可重复评估的治疗干预至关重要。指甲银屑病严重程度指数（NAPSI）是一种经过验证的评分工具，但其实用性受到观察者之间差异性的限制。本研究旨在利用深度学习技术开发一种可靠、准确的 NAPSI 评分工具。该工具 "NAPSI计算器 "包括两部分：从图像中检测指甲和NAPSI评分。NAPSI 由九名美甲专家进行注释，他们都是在甲病专科门诊工作过并拥有丰富经验的注册皮肤科医生。在最终测试集中，"NAPSI 计算器 "正确定位了 137/138 枚指甲，并对 NAPSI 进行了评分，准确率高于六位未获得医学会认证的住院医师：83.9% vs 65.7%; P = 0.008，以及四位获得医学会认证的非指甲专家皮肤科医生：83.9% vs 73.0%; P = 0.005。NAPSI 计算器 "可随时用于临床，有助于提高甲银屑病的医疗实践水平。

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

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.

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.