A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine
Tekijät: Alabi Rasheed Omobolaji, Almangush Alhadi, Elmusrati Mohammed, Mäkitie Antti A.
Kustantaja: Frontiers Media
Julkaisuvuosi: 2021
Journal: Frontiers in Oral Health
Tietokannassa oleva lehden nimi: Frontiers in oral health
Lehden akronyymi: Front Oral Health
Vuosikerta: 2
ISSN: 2673-4842
eISSN: 2673-4842
DOI: https://doi.org/10.3389/froh.2021.794248
Verkko-osoite: https://www.doi.org/10.3389/froh.2021.794248
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/178536638
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
