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Related Concept Videos

Assessment of the Mouth01:26

Assessment of the Mouth

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A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
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The inspection begins with visually examining the mouth for symmetry, color, and size.
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The oral cavity, or the mouth, is a complex structure in humans that plays a vital role in our day-to-day lives. Its role is not only in chewing and swallowing food; it also plays a role in speech and facial expressions.
Teeth: The teeth are the hardest structures in our bodies. Humans have two sets of teeth throughout their lifetime: deciduous (baby) teeth and permanent teeth. Each tooth consists of several parts: the crown (visible part), the root (embedded in the jaw), enamel (hard outer...
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  3. Detecting Oral Cancer Using Tabular Deep Learning.
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Detecting Oral Cancer Using Tabular Deep Learning.

Zhiyun Xue1, Zhaohui Liang1, Sivaramakrishnan Rajaraman1

  • 1Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

IEEE International Conference on Omni-Layer Intelligent Systems : COINS. IEEE International Conference on Omni-Layer Intelligent Systems
|September 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Early oral cancer detection is vital. This study explores deep learning on tabular data for classifying cancerous and precancerous lesions, showing promising results for improved patient outcomes.

Keywords:
deep learningoral cancer screeningoral lesion characteristicstabular data

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Area of Science:

  • Biomedicine
  • Artificial Intelligence
  • Machine Learning

Background:

  • Oral cancer has a low survival rate, necessitating early detection for effective treatment.
  • Current diagnostic methods primarily rely on image analysis, leaving potential in tabular clinical data unexplored.
  • Deep learning applications for oral cancer prediction using structured text data are understudied.

Purpose of the Study:

  • To investigate the efficacy of deep learning models in predicting cancerous versus precancerous oral lesions using tabular clinical data.
  • To compare the performance of two deep learning methods against a conventional algorithm for oral cancer risk assessment.
  • To analyze the interpretability of the models and identify key predictive features.

Main Methods:

  • Utilized a subset of 1791 patients from an ongoing oral cancer study, focusing on structured text (tabular) clinical data.
  • Implemented and compared two distinct deep learning architectures designed for tabular data.
  • Benchmarked deep learning models against a traditional machine learning algorithm for classification accuracy.

    • Main Results:

    • All evaluated models demonstrated strong predictive performance on a hold-out test set, achieving a Youden index greater than 0.6 and an Area Under the Curve (AUC) greater than 0.9.
    • Model interpretability analysis revealed that lesion characteristics are critical factors in predicting oral cancer risk.
    • The study confirms the potential of AI/ML in analyzing clinical tabular data for oral cancer detection.

    Conclusions:

    • Deep learning approaches applied to tabular clinical data show significant promise for the early detection of oral cancer.
    • Lesion characteristics are identified as key indicators for differentiating between cancerous and precancerous oral lesions.
    • This research contributes valuable insights for applying AI/ML in biomedical applications, particularly in oral oncology.