- Home
- Medical news & Guidelines
- Anesthesiology
- Cardiology and CTVS
- Critical Care
- Dentistry
- Dermatology
- Diabetes and Endocrinology
- ENT
- Gastroenterology
- Medicine
- Nephrology
- Neurology
- Obstretics-Gynaecology
- Oncology
- Ophthalmology
- Orthopaedics
- Pediatrics-Neonatology
- Psychiatry
- Pulmonology
- Radiology
- Surgery
- Urology
- Laboratory Medicine
- Diet
- Nursing
- Paramedical
- Physiotherapy
- Health news
- Fact Check
- Bone Health Fact Check
- Brain Health Fact Check
- Cancer Related Fact Check
- Child Care Fact Check
- Dental and oral health fact check
- Diabetes and metabolic health fact check
- Diet and Nutrition Fact Check
- Eye and ENT Care Fact Check
- Fitness fact check
- Gut health fact check
- Heart health fact check
- Kidney health fact check
- Medical education fact check
- Men's health fact check
- Respiratory fact check
- Skin and hair care fact check
- Vaccine and Immunization fact check
- Women's health fact check
- AYUSH
- State News
- Andaman and Nicobar Islands
- Andhra Pradesh
- Arunachal Pradesh
- Assam
- Bihar
- Chandigarh
- Chattisgarh
- Dadra and Nagar Haveli
- Daman and Diu
- Delhi
- Goa
- Gujarat
- Haryana
- Himachal Pradesh
- Jammu & Kashmir
- Jharkhand
- Karnataka
- Kerala
- Ladakh
- Lakshadweep
- Madhya Pradesh
- Maharashtra
- Manipur
- Meghalaya
- Mizoram
- Nagaland
- Odisha
- Puducherry
- Punjab
- Rajasthan
- Sikkim
- Tamil Nadu
- Telangana
- Tripura
- Uttar Pradesh
- Uttrakhand
- West Bengal
- Medical Education
- Industry
Machine learning effective in predicting oral cancer risk - Video
Overview
According to a recent study, visual oral examination (VOE) was performed among 1467 participants of a community-based screening program. Each individual's status was defined as positive/negative for oral cancer and histologic confirmation of epithelial dysplasia (ED) and squamous cell carcinoma (SCC) was performed for positive status.
Information on demography, habitual, lifestyle and familial risk factors was obtained, and expired carbon monoxide levels (in ppm) were assessed using a monitor. Input features and histologic diagnoses were used to populate 12 machine learning algorithms. And the study then demonstrated that machine learning is a successful tool for predicting oral cancer risk and may be applied to identify 'at-risk populations' in opportunistic and organized screening.
Reference: Meeting Announcement INTERNATIONAL & AMERICAN ASSOCIATIONS FOR DENTAL RESEARCH; https://www.iadr.org/about/news-reports/press-releases/machine-learning-predicts-oral-cancer.
Speakers
Dr. Nandita Mohan
BDS, MDS( Pedodontics and Preventive Dentistry)