• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Combining Automated Lesion Risk and Change Assessment Improves Melanoma Detection: A Retrospective Accuracy Study.

作者信息

Rutjes Chantal, Mothershaw Adam, D'Alessandro Brian M, Primiero Clare A, McInerney-Leo Aideen, Soyer H Peter, Janda Monika, Betz-Stablein Brigid

机构信息

Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia.

Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia; Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.

出版信息

J Invest Dermatol. 2025 Mar;145(3):703-706.e1. doi: 10.1016/j.jid.2024.07.027. Epub 2024 Sep 7.

DOI:10.1016/j.jid.2024.07.027
PMID:39182563
Abstract
摘要

相似文献

1
Combining Automated Lesion Risk and Change Assessment Improves Melanoma Detection: A Retrospective Accuracy Study.结合自动病变风险与变化评估可提高黑色素瘤检测水平：一项回顾性准确性研究
J Invest Dermatol. 2025 Mar;145(3):703-706.e1. doi: 10.1016/j.jid.2024.07.027. Epub 2024 Sep 7.
2
Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?在真实环境下，二维和三维全身摄影及卷积神经网络的人工智能在黑色素瘤高危人群中的诊断性能：皮肤癌筛查的新纪元？
Eur J Cancer. 2023 Sep;190:112954. doi: 10.1016/j.ejca.2023.112954. Epub 2023 Jun 24.
3
3D Whole-body skin imaging for automated melanoma detection.用于自动检测黑色素瘤的3D全身皮肤成像。
J Eur Acad Dermatol Venereol. 2023 May;37(5):945-950. doi: 10.1111/jdv.18924. Epub 2023 Mar 24.
4
Clinician's Ability to Identify Non-Melanoma Skin Cancer on 3D-Total Body Photography Sectors that Were Initially Identified during In-Person Skin Examination with Dermoscopy.临床医生在3D全身摄影区域识别非黑色素瘤皮肤癌的能力，这些区域最初是在使用皮肤镜进行的面对面皮肤检查中确定的。
Dermatology. 2024;240(1):142-151. doi: 10.1159/000535031. Epub 2023 Nov 6.
5
Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.基于 3D 全身摄影和卷积神经网络的可重现痣计数。
Dermatology. 2022;238(1):4-11. doi: 10.1159/000517218. Epub 2021 Jul 8.
6
[The Rise of Artificial Intelligence - High Prediction Accuracy in Early Detection of Pigmented Melanoma].[人工智能的崛起——色素性黑色素瘤早期检测中的高预测准确率]
Laryngorhinootologie. 2023 Jul;102(7):496-503. doi: 10.1055/a-1949-3639. Epub 2022 Dec 29.
7
Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance.自动化全身黑素细胞病变检测算法对临床相关黑素细胞病变检测性能的评估。
Eur J Cancer. 2024 May;202:114026. doi: 10.1016/j.ejca.2024.114026. Epub 2024 Mar 19.
8
The Additive Value of 3D Total Body Imaging for Sequential Monitoring of Skin Lesions: A Case Series.3D 全身成像在皮肤病变连续监测中的附加价值：病例系列。
Dermatology. 2022;238(1):12-17. doi: 10.1159/000517900. Epub 2021 Aug 11.
9
Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study.患者和皮肤科医生对用于黑色素瘤筛查的人工智能的看法：一项前瞻性研究。
J Eur Acad Dermatol Venereol. 2024 Dec;38(12):2240-2249. doi: 10.1111/jdv.19905. Epub 2024 Feb 27.
10
A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography.人工智能全身摄影图像分析的机遇与挑战：叙事性回顾
J Invest Dermatol. 2024 Jun;144(6):1200-1207. doi: 10.1016/j.jid.2023.11.007. Epub 2024 Jan 16.

引用本文的文献

1
A Comparison of Skin Lesions' Diagnoses Between AI-Based Image Classification, an Expert Dermatologist, and a Non-Expert.基于人工智能的图像分类、皮肤科专家和非专家对皮肤病变诊断的比较
Diagnostics (Basel). 2025 Apr 28;15(9):1115. doi: 10.3390/diagnostics15091115.