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Artificial Intelligence Techniques in Health Informatics for Oral Cancer Detection

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Connected e-Health

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1021))

Abstract

Oral cancer is indeed a lethal disease with a complicated aetiology and a high mortality rate. Artificial Amongst the most notable technological advancements in dentistry sciences is artificial intelligence (AI). In recent years, deep learning and machine learning, both subsets of artificial intelligence, have generated greater interest in prediction medicine. Deep learning and machine learning methods have been used to analyses imaging and radionics and create models that can assist clinicians in making educated and directed decisions that help improve health care. Enhanced prognosis of oral squamous cell carcinoma (OSCC), leukoplakia, and tumour will have a significant impact on clinical oral cancer management. This study provides a comprehensive and clear overview of the work done utilizing machine learning and deep learning to identify oral squamous cell carcinoma (OSCC), oral leukoplakia, and tumors. This review looks at the latest developments in OSCC prognostication using deep learning and machine learning. The researcher used a variety of technologies to conduct the review. In aspects of sensitivity and efficiency in diagnosis and prediction, AI outperforms current clinical techniques and traditional statistics such as cox regression analysis and logistic regression.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

    Khushboo Bansal

  2. Department of Computer Science and Application, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

    R. K. Batla

  3. Department of Computer Science and Engineering, Indus Institute of Technology & Engineering, Indus University, Ahmedabad, Gujarat, India

    Yogesh Kumar

  4. Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdul University, Wadi Ad-Dwasir, 11991, Saudi Arabia

    Jana Shafi

Authors
  1. Khushboo Bansal
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  2. R. K. Batla
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  3. Yogesh Kumar
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  4. Jana Shafi
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Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Sushruta Mishra

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. KIET Group of Institutions, Ghaziabad, Delhi-NCR, India

    Akash Kumar Bhoi

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  5. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Juan M. Corchado

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Bansal, K., Batla, R.K., Kumar, Y., Shafi, J. (2022). Artificial Intelligence Techniques in Health Informatics for Oral Cancer Detection. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_11

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