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From Pixels to Diagnosis: Algorithmic Analysis of Clinical Oral Photos for Early Detection of Oral Squamous Cell Carcinoma.
Rabinovici-Cohen, Simona; Fridman, Naomi; Weinbaum, Michal; Melul, Eli; Hexter, Efrat; Rosen-Zvi, Michal; Aizenberg, Yelena; Porat Ben Amy, Dalit.
Afiliación
  • Rabinovici-Cohen S; IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel.
  • Fridman N; TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel.
  • Weinbaum M; The Department of Industrial Engineering & Management, Ariel University, Ariel 40700, Israel.
  • Melul E; TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel.
  • Hexter E; TIMNA-Big Data Research Platform Unit, Ministry of Health, Jerusalem 9446724, Israel.
  • Rosen-Zvi M; IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel.
  • Aizenberg Y; IBM Research-Israel, Mount Carmel, Haifa 3498825, Israel.
  • Porat Ben Amy D; Faculty of Medicine, The Hebrew University, Jerusalem 91120, Israel.
Cancers (Basel) ; 16(5)2024 Feb 29.
Article en En | MEDLINE | ID: mdl-38473377
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Israel

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Israel
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