From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma

Simona Rabinovici-Cohen, Naomi Fridman, Michal Weinbaum, Eli Melul, Efrat Hexter, Michal Rosen-Zvi, Yelena Aizenberg, Dalit Porat Ben Amy

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. Despite numerous advancements in understanding its biology, the mean five-year survival rate of OSCC is still very poor at about 50%, with even lower rates when the disease is detected at later stages. We investigate the use of clinical photographic images taken by common smartphones for the automated detection of OSCC cases and for the identification of suspicious cases mimicking cancer that require an urgent biopsy. We perform a retrospective study on a cohort of 1470 patients drawn from both hospital records and online academic sources. We examine various deep learning methods for the early detection of OSCC cases as well as for the detection of suspicious cases. Our results demonstrate the efficacy of these methods in both tasks, providing a comprehensive understanding of the patient’s condition. When evaluated on holdout data, the model to predict OSCC achieved an AUC of 0.96 (CI: 0.91, 0.98), with a sensitivity of 0.91 and specificity of 0.81. When the data are stratified based on lesion location, we find that our models can provide enhanced accuracy (AUC 1.00) in differentiating specific groups of patients that have lesions in the lingual mucosa, floor of mouth, or posterior tongue. These results underscore the potential of leveraging clinical photos for the timely and accurate identification of OSCC.

Original languageEnglish
Article number1019
JournalCancers
Volume16
Issue number5
DOIs
StatePublished - 29 Feb 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • artificial intelligence (AI)
  • clinical photographic images
  • convolutional neural network (CNN)
  • deep learning (DL)
  • head and neck cancers (HNCs)
  • image processing
  • machine learning (ML)
  • oral squamous cell carcinoma (OSCC)

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