The use of artificial intelligence, machine learning and deep learning in oncologic histopathology
| Autor: | Ahmed S. Sultan, Mohamed Elgharib, Maryam Jessri, John R. Basile, Tiffany Tavares |
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| Rok vydání: | 2020 |
| Předmět: |
Cancer Research
Machine learning computer.software_genre Pathology and Forensic Medicine Terminology Machine Learning 03 medical and health sciences Computational pathology 0302 clinical medicine Deep Learning Artificial Intelligence Health care Medicine Humans In patient business.industry Deep learning Digital pathology Patient survival 030206 dentistry stomatognathic diseases Otorhinolaryngology 030220 oncology & carcinogenesis Whole slide image Periodontics Artificial intelligence Oral Surgery Neoplasm Recurrence Local business computer |
| Zdroj: | Journal of oral pathologymedicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral PathologyREFERENCES. 49(9) |
| ISSN: | 1600-0714 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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