/
FGAI4H-R-040-A03 Cambridge, 21-24 March 2023 FGAI4H-R-040-A03 Cambridge, 21-24 March 2023

FGAI4H-R-040-A03 Cambridge, 21-24 March 2023 - PowerPoint Presentation

ethlyn
ethlyn . @ethlyn
Follow
66 views
Uploaded On 2023-05-21

FGAI4H-R-040-A03 Cambridge, 21-24 March 2023 - PPT Presentation

Source SDS Sharda University Title Att3 Presentation Machine learning for classification of oral epithelial dysplasia and diagnosis of squamous cell carcinoma Contact Parul Khare ID: 998909

dysplasia oral epithelial diagnosis oral dysplasia diagnosis epithelial machine learning 2022 amp sunycell computational pathology cell squamous framework classification

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "FGAI4H-R-040-A03 Cambridge, 21-24 March ..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

1. FGAI4H-R-040-A03Cambridge, 21-24 March 2023Source:SDS Sharda UniversityTitle:Att.3 – Presentation - Machine learning for classification of oral epithelial dysplasia and diagnosis of squamous cell carcinoma Contact:Parul KhareE-mail: parulsinha02@gmail.com Abstract:This PPT contains a presentation on machine learning for classification of oral epithelial dysplasia and diagnosis of squamous cell carcinoma given in the AI for Dentistry Symposium on 21 March 2023.

2. Artificial Intelligence in Oral & Maxillofacial Oncology

3. Dr Parul Khare SinhaPrincipal InvestigatorCo-Principal InvestigatorDr Brandon Veremis Dr Scott DoyleMartha ButtnerDr Margaret BrandweinDr Falk SchwendickeDr Kenneth Aschheim

4. Machine Learning for Classification of Oral Epithelial Dysplasia and Diagnosis of Squamous Cell CarcinomaProgress Report

5. 1st June 2022, BerlinArtificial Intelligence in Oral & Maxillofacial Oncology

6. We welcome our 10 collaborating countries onboard!India, USA, Jordan, Israel, Mexico, Brazil, Al Savaodr, Egypt, Sri Lanka, Syria

7. Aim- is to use a machine learning approach to address the lack of reproducibility in epithelial dysplasia. Objectives: - The first approach we will use is to use outcomes data to predict the risk of malignant transformation.- If insufficient outcomes data is available, we will develop a classifier for epithelial lesions of the oral cavity using a consensus of multiple pathologist opinions.

8. Study samples- 2000 cases from 10 international institutions (200 each)10 different oral pathologists will review the annotationsCategory- Benign, PMLs & SQCC

9. The early diagnosis of premalignant conditions can help prevent progression to malignancyLeukoplakia image from: González-Moles MÁ, Aguilar-Ruiz M, Ramos-García P. Challenges in the Early Diagnosis of Oral Cancer, Evidence Gaps and Strategies for Improvement: A Scoping Review of Systematic Reviews. Cancers (Basel). 2022;14(19):4967. Published 2022 Oct 10. doi:10.3390/cancers14194967There is often subjectivity in diagnosing and grading dysplasia among pathologists--AI may help with standardization

10. Diagnosing dysplasia can be easy!

11. But sometimes it can be hard.

12. SUNYCell: Computational Pathology Framework

13. https://digitalslidearchive.github.iohttps://sunycell.ccr.buffalo.eduSUNYCell: Computational Pathology Framework

14. SUNYCell: Computational Pathology Framework

15.