High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.

Autor: Chaudhary N; Multidisciplinary Centre for Advanced Research and Studies, Jamia Millia Islamia, New Delhi, India., Rai A; Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India., Rao AM; Department of Computer Science, Ashoka University, Sonipat, Haryana, India., Faizan MI; Multidisciplinary Centre for Advanced Research and Studies, Jamia Millia Islamia, New Delhi, India., Augustine J; Maulana Azad Institute of Dental Sciences, New Delhi, India., Chaurasia A; King George Medical University, Lucknow, Uttar Pradesh, India., Mishra D; All India Institute of Medical Sciences, New Delhi, India., Chandra A; Banaras Hindu University, Banaras, Uttar Pradesh, India., Chauhan V; Multidisciplinary Centre for Advanced Research and Studies, Jamia Millia Islamia, New Delhi, India., Ahmad T; Multidisciplinary Centre for Advanced Research and Studies, Jamia Millia Islamia, New Delhi, India. tahmad7@jmi.ac.in.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Sep 27; Vol. 11 (1), pp. 1050. Date of Electronic Publication: 2024 Sep 27.
DOI: 10.1038/s41597-024-03836-6
Abstrakt: Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured at 1000X effective magnification (100X objective lens), encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well- differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). The database seeks to aid in developing innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes.
(© 2024. The Author(s).)
Databáze: MEDLINE
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