Deep Learning in Dentistry, Computer Vision, Pattern Recognition
Focusing on enhancing diagnostic capabilities in dentistry using AI-driven deep learning and computer vision techniques, the system developed automatically analyzes dental radiographs, improving the accuracy and speed of diagnosis.
Location: Romania
Industry: Healthcare
Solution: AI, Elastic search
Engagement model: Solution Based
Key Technologies: MongoDB, Tensor Flow, Flask, Common objects in context,jQuery
Challenge
Dental professionals face significant challenges in diagnosing dental issues quickly due to the time-intensive nature of manual radiograph analysis. Additionally, compliance with regulatory reporting requirements for APRA (Taxonomy & Freeform) necessitates sophisticated technical expertise and development capabilities.
Solution
To address these challenges, a web-based application utilizing deep learning, computer vision, and pattern recognition was developed. This AI-driven tool serves as a smart assistant to dental professionals by rapidly analyzing radiographs and identifying up to four dental problems within seconds. The application complies with APRA's regulatory requirements, ensuring accuracy and adherence to industry standards.
Technologies Used:
MongoDB: Database management
TensorFlow: Machine learning and neural network development
Flask: Backend framework
Common Objects in Context (COCO): For training and evaluating object recognition algorithms
jQuery: For client-side scripting of the web application
Results
The deployment of the AI application in dental practices markedly increased the productivity of dentists by reducing the radiograph analysis time from three minutes to approximately five seconds. It also provided dentists with quick access to historical radiograph data, facilitating better patient management and care.
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