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

• Published in: Journal of oral pathology & medicine Vol. 49; no. 9; pp. 849 - 856
• Main Authors: Sultan, Ahmed S., Elgharib, Mohamed A., Tavares, Tiffany, Jessri, Maryam, Basile, John R.
• Format: Journal Article
• Language: English
• Published: Denmark Wiley Subscription Services, Inc 01-10-2020
• Subjects: Artificial Intelligence; Classification systems; computational pathology; Computer applications; Deep Learning; Dentistry; digital pathology; Head & neck cancer; head and neck cancer; Health care; Histopathology; Humans; Learning algorithms; Machine Learning; Neoplasm Recurrence, Local; oncologic histopathology; Oncology; Oral cancer; Oral carcinoma; Oral squamous cell carcinoma; Pathology; Prediction models; deep learning; computational pathology; head and neck cancer; oncologic histopathology; digital pathology; machine learning; artificial intelligence
• ISSN: 0904-2512; 1600-0714
• DOI: 10.1111/jop.13042

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.