Exploring the business aspects of digital pathology, deep learning in cancers

Intelligence-based medicine Pub Date : 2024-01-01 DOI:10.1016/j.ibmed.2024.100172

Arjun Reddy , Darnell K. Adrian Williams , Gillian Graifman , Nowair Hussain , Maytal Amiel , Tran Priscilla , Ali Haider , Bali Kumar Kavitesh , Austin Li , Leael Alishahian , Nichelle Perera , Corey Efros , Myoungmee Babu , Mathew Tharakan , Mill Etienne , Benson A. Babu

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Abstract

Introduction

Cancer remains one of the leading causes of morbidity and mortality worldwide. Deep learning in digital pathology has the potential to improve operational efficiency, costs, and care.

Methods

We searched Web of Science, Arxiv, MedRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, and Cochrane databases for whole slide imaging and deep learning articles published between 2019 and 2023. The final six articles were selected from 776 articles identified through an inclusion criterion.

Conclusion

Digital pathology services that utilize deep learning have the potential to enhance clinical workflow efficiencies and can have a positive impact on business activities. We anticipate cost reductions as deep learning technology advances and more companies enter the digital pathology ecosystem. However, the limited availability of business use cases, primarily due to publication bias, poses a challenge in medicine without clear examples to learn from.

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探索数字病理学的业务方面，癌症中的深度学习

导言癌症仍然是全球发病率和死亡率的主要原因之一。方法我们在Web of Science、Arxiv、MedRxiv、Embase、PubMed、DBLP、Google Scholar、IEEE Xplore和Cochrane数据库中搜索了2019年至2023年间发表的全切片成像和深度学习文章。结论利用深度学习的数字化病理服务有可能提高临床工作流程的效率，并对业务活动产生积极影响。随着深度学习技术的发展和更多公司进入数字病理生态系统，我们预计成本将会降低。然而，主要由于出版物的偏见，商业用例的可用性有限，这给没有明确实例可借鉴的医学界带来了挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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