Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Oct;49(9):849-856.
doi: 10.1111/jop.13042. Epub 2020 Jun 15.

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

Ahmed S Sultan  1 , Mohamed A Elgharib  2 , Tiffany Tavares  3 , Maryam Jessri  4 , John R Basile  1   5
Affiliations
Review

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

Ahmed S Sultan et al. J Oral Pathol Med. 2020 Oct.

Abstract

Background: Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage.

Discussion: A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed.

Conclusion: Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology.

Keywords: artificial intelligence; computational pathology; deep learning; digital pathology; head and neck cancer; machine learning; oncologic histopathology.

PubMed Disclaimer

References

REFERENCES

    1. Xing F, Yang L. Machine learning and its application in microscopic image analysis. In: Wu G, Shen D, & Sabuncu MR ed. Machine Learning and Medical Imaging. Amsterdam, Netherlands: Elsevier Inc.; 2016:97-127.
    1. Badrick T, Banfi G, Bietenbeck A, Cervinski MA, Loh TP, Sikaris K. Machine learning for clinical chemists. Clin Chem. 2019;65(11):1350-1356.
    1. Kim M, Yun J, Cho Y, et al. Deep learning in medical imaging. Neurospine. 2019;16(4):657-668.
    1. Chang HY, Jung CK, Woo JI, et al. Artificial intelligence in pathology. J Pathol Transl Med. 2019;53(1):1-12.
    1. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, & Weinberger KQ, ed. Advances in Neural Processing Information Systems (NIPS). New York, NY: Curran Associates, Inc.; 2012:1097-1105.

LinkOut - more resources