与18位国际专家病理学家相比，人工智能对组织学黑色素瘤识别的诊断性能。 - Suppr

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists.

作者信息

Brinker Titus J, Schmitt Max, Krieghoff-Henning Eva I, Barnhill Raymond, Beltraminelli Helmut, Braun Stephan A, Carr Richard, Fernandez-Figueras Maria-Teresa, Ferrara Gerardo, Fraitag Sylvie, Gianotti Raffaele, Llamas-Velasco Mar, Müller Cornelia S L, Perasole Antonio, Requena Luis, Sangueza Omar P, Santonja Carlos, Starz Hans, Vale Esmeralda, Weyers Wolfgang, Hekler Achim, Kather Jakob N, Fröhling Stefan, Krahl Dieter, Holland-Letz Tim, Utikal Jochen S, Saggini Andrea, Kutzner Heinz

机构信息

Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.

出版信息

J Am Acad Dermatol. 2022 Mar;86(3):640-642. doi: 10.1016/j.jaad.2021.02.009. Epub 2021 Feb 11.

DOI:10.1016/j.jaad.2021.02.009

PMID:33581189

Abstract

摘要

相似文献

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists.与18位国际专家病理学家相比，人工智能对组织学黑色素瘤识别的诊断性能。

J Am Acad Dermatol. 2022 Mar;86(3):640-642. doi: 10.1016/j.jaad.2021.02.009. Epub 2021 Feb 11.

Early recognition at last.终于及早识别出来了。

Arch Dermatol. 2008 Apr;144(4):533-4. doi: 10.1001/archderm.144.4.533.

Artificial Intelligence in Melanoma Dermatopathology: A Review of Literature.黑色素瘤皮肤病理学中的人工智能：文献综述

Am J Dermatopathol. 2024 Feb 1;46(2):83-94. doi: 10.1097/DAD.0000000000002593. Epub 2023 Nov 14.

Basic Elements of Artificial Intelligence Tools in the Diagnosis of Cutaneous Melanoma.人工智能工具在皮肤黑色素瘤诊断中的基本要素。

Crit Rev Oncog. 2023;28(3):37-41. doi: 10.1615/CritRevOncog.2023050220.

Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma.系统评价皮肤镜检查和数字皮肤镜检查/人工智能在黑色素瘤诊断中的应用。

Br J Dermatol. 2009 Sep;161(3):591-604. doi: 10.1111/j.1365-2133.2009.09093.x. Epub 2009 Mar 19.

Using artificial intelligence-based technologies to support the diagnosis and early detection of melanoma in primary care.利用基于人工智能的技术辅助基层医疗中黑色素瘤的诊断和早期检测。

Br J Dermatol. 2024 Jun 20;191(1):13. doi: 10.1093/bjd/ljae094.

International pathologists congruence survey on quantitation of malignant melanoma.国际病理学家关于恶性黑色素瘤定量的一致性调查。

Pathology. 1980 Apr;12(2):245-53. doi: 10.3109/00313028009060079.

The diagnostic performance of expert dermoscopists vs a computer-vision system on small-diameter melanomas.专家皮肤镜医师与计算机视觉系统对小直径黑色素瘤的诊断性能比较。

Arch Dermatol. 2008 Apr;144(4):476-82. doi: 10.1001/archderm.144.4.476.

Histologic mimics of malignant melanoma.恶性黑色素瘤的组织学模仿物。

Singapore Med J. 2018 Nov;59(11):602-607. doi: 10.11622/smedj.2018041. Epub 2018 May 18.

The clinical recognition and prognostic factors of primary cutaneous malignant melanoma.原发性皮肤恶性黑色素瘤的临床识别与预后因素

Med Clin North Am. 1986 Jan;70(1):39-55. doi: 10.1016/s0025-7125(16)30967-1.

引用本文的文献

Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.利用细胞核形态和空间组织进行黑色素瘤检测的机器学习

Sci Rep. 2025 Jul 1;15(1):21594. doi: 10.1038/s41598-025-02913-z.

Discordance, accuracy and reproducibility study of pathologists' diagnosis of melanoma and melanocytic tumors.病理学家对黑色素瘤和黑素细胞肿瘤诊断的不一致性、准确性及可重复性研究

Nat Commun. 2025 Jan 17;16(1):789. doi: 10.1038/s41467-025-56160-x.

[State of digitalization in dermatopathology].[皮肤病理学中的数字化状态]

Pathologie (Heidelb). 2025 Mar;46(2):101-107. doi: 10.1007/s00292-024-01401-x. Epub 2025 Jan 3.

The Role of Artificial Intelligence in the Diagnosis of Melanoma.人工智能在黑色素瘤诊断中的作用。

Cureus. 2024 Sep 20;16(9):e69818. doi: 10.7759/cureus.69818. eCollection 2024 Sep.

Comparison of Three Deep Learning Models in Accurate Classification of 770 Dermoscopy Skin Lesion Images.三种深度学习模型对770张皮肤镜检查皮肤病变图像进行准确分类的比较。

AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:46-53. eCollection 2024.

Developing an efficient method for melanoma detection using CNN techniques.利用卷积神经网络技术开发一种高效的黑色素瘤检测方法。

J Egypt Natl Canc Inst. 2024 Feb 26;36(1):6. doi: 10.1186/s43046-024-00210-w.

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.基于免疫组织化学和常规组织学评估深度学习在黑色素瘤分类中的应用：一项三中心研究。

PLoS One. 2024 Jan 19;19(1):e0297146. doi: 10.1371/journal.pone.0297146. eCollection 2024.

Advances in melanoma: epidemiology, diagnosis, and prognosis.黑色素瘤的进展：流行病学、诊断与预后

Front Med (Lausanne). 2023 Nov 22;10:1268479. doi: 10.3389/fmed.2023.1268479. eCollection 2023.

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.使用机器学习技术的皮肤病变分类与检测：一项系统综述。

Diagnostics (Basel). 2023 Oct 7;13(19):3147. doi: 10.3390/diagnostics13193147.

Molecular Classifiers in Skin Cancers: Challenges and Promises.皮肤癌中的分子分类器：挑战与前景

Cancers (Basel). 2023 Sep 7;15(18):4463. doi: 10.3390/cancers15184463.

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型，支持多种主流文档格式。

立即体验

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists.

作者信息

机构信息

出版信息

相似文献

引用本文的文献

文献AI研究员

用中文搜PubMed

文档翻译

Suppr 超能文献

相似文献

引用本文的文献