Enhancing Dental Diagnostics Through AI- Driven Annotation and Defect Identification
Objective
To enhance diagnostic accuracy and efficiency in detecting dental defects, supporting improved treatment planning and patient care.
Process
- Used RadiAnt and 3D Slicer for precise annotations on radiographs and 3D dental images.
- Collaborated with dental professionals to ensure high-quality annotations for improved diagnostics.
- Integrated continuous learning systems to refine AI model performance over time.
- Applied NLP to analyze clinical notes, supporting diagnostic insights.
Challenges
- Radiograph Variability: Ensuring consistent interpretation of dental images.
- Manual Annotation: Managing the time-intensive manual annotation process.
- Detecting Subtle Defects: Identifying early-stage defects, such as small caries.
- Risk of Misdiagnosis: Reducing diagnostic errors to improve patient outcomes.
Outcome
Increased diagnostic accuracy, reduced error rates, and improved efficiency in defect identification, resulting in more precise, personalized treatment plans for patients.
