Song Bofan, Liang Rongguang
Wyant College of Optical Sciences, The University of Arizona, Tucson, AZ, 85721, USA.
Biosens Bioelectron. 2025 Mar 1;271:116982. doi: 10.1016/j.bios.2024.116982. Epub 2024 Nov 21.
Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detection of different cancer types and highlights the advancements of AI for in vivo cancer detection in smartphone-based imaging. However, these compact smartphone systems face challenges like low imaging quality and restricted computing power. The use of advanced AI algorithms to address the optical and computational limitations of smartphone-based imaging systems provides promising solutions. AI-based cancer detection also faces challenges. Transparency and reliability are critical factors in gaining the trust and acceptance of AI algorithms for clinical application, explainable and uncertainty-aware AI breaks the black box and will shape the future AI development in early cancer detection. The challenges and solutions for improving AI accuracy, transparency, and reliability are general issues in AI applications, the AI technologies, limitations, and potentials discussed in this paper are applicable to a wide range of biomedical imaging diagnostics beyond smartphones or cancer-specific applications. Smartphone-based multimodal imaging systems and deep learning algorithms for multimodal data analysis are also growing trends, as this approach can provide comprehensive information about the tissue being examined. Future opportunities and perspectives of AI-integrated smartphone imaging systems will be to make cutting-edge diagnostic tools more affordable and accessible, ultimately enabling early cancer detection for a broader population.
癌症是一项重大的全球健康挑战，全球近六分之一的死亡与之相关。早期诊断能显著提高生存率和患者预后，但在资源有限的地区，医疗资源匮乏常导致癌症晚期才被诊断出来。将人工智能（AI）与基于智能手机的成像系统相结合，可为早期癌症检测提供便携、经济高效且广泛可用的工具，从而提供了一个很有前景的解决方案。本文介绍了先进的基于智能手机的成像系统，该系统利用各种成像方式对不同类型的癌症进行体内检测，并着重介绍了人工智能在基于智能手机成像的体内癌症检测方面的进展。然而，这些紧凑的智能手机系统面临着成像质量低和计算能力受限等挑战。使用先进的人工智能算法来解决基于智能手机成像系统的光学和计算限制，提供了很有前景的解决方案。基于人工智能的癌症检测也面临挑战。透明度和可靠性是人工智能算法在临床应用中获得信任和认可的关键因素，可解释且能感知不确定性的人工智能打破了黑箱，将塑造早期癌症检测中人工智能的未来发展。提高人工智能准确性、透明度和可靠性的挑战与解决方案是人工智能应用中的普遍问题，本文讨论的人工智能技术、局限性和潜力适用于智能手机或癌症特定应用之外的广泛生物医学成像诊断。基于智能手机的多模态成像系统以及用于多模态数据分析的深度学习算法也是发展趋势，因为这种方法可以提供有关被检查组织的全面信息。人工智能集成智能手机成像系统未来的机遇和前景将是使前沿诊断工具更经济实惠且易于使用，最终让更多人能够进行早期癌症检测。
