Page 74 - AI for Good Innovate for Impact
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AI for Good Innovate for Impact



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                            Item                                    Details
                       Model Training  •  Data preparation: Standard Tessellation Language (STL) preprocessing,
                       and Fine-Tuning   noise filtering, anatomical labelling.
                                      •  Model architecture: Convolutional Neural Networks (CNNs) and Generative
                                         Adversarial Networks (GANs) for 3D mesh analysis. (PyTorch, TensorFlow)
                                      •  Training: GPU-based batch training with data augmentation and dental-spe-
                                         cific loss functions.
                                      •  Hyperparameter tuning: Optimization via Optuna; cross-validation with
                                         clinical datasets.
                                      •  Fine-tuning: Continuous learning from real-world usage; transfer learning
                                         for specific cases.
                                      •  Deployment: Cloud-based AIaaS; model tracking and update via MLflow.

                       Testbeds    or The system has been evaluated in real-world settings through pilot deploy-
                       Pilot Deploy- ments, demonstrating its practical feasibility and user acceptance [2], [3], [4],
                       ments          [5].

                       Code reposito- Private
                       ries



                      2      Use Case Description


                      2�1     Description

                      Dental professionals and technicians continue to face significant challenges in prosthesis design,
                      even as digital technologies become more prevalent in clinical and laboratory environments.
                      Traditional dental CAD workflows remain labour-intensive, requiring a high level of manual
                      intervention and expertise.
                      These workflows are not only time-consuming but also prone to inconsistencies and human
                      error, which can negatively impact treatment quality, extend patient discomfort, and increase
                      clinical risks due to poorly fitted or inaccurately designed prostheses. This demonstrates the
                      pressing need for an automated, standardized solution that can enhance accuracy, speed, and
                      consistency in dental restoration.
                      Dentbird Solutions introduces an AI-powered dental CAD system designed to address these
                      limitations by automating the most time-consuming steps in prosthesis design. Leveraging
                      advanced deep learning and 3D data processing technologies, the software automatically
                      classifies upper and lower jaw data, detects margin lines, and generates patient-specific
                      prosthesis (crown, implant crown, bridge, inlay, and onlay) designs in a matter of minutes.

                      The system also supports continuous mass production workflows, significantly alleviating the
                      workload of dental technicians. As a cloud-based AI-as-a-Service (AIaaS) software, Dentbird
                      eliminates the need for local software installation, making advanced dental CAD technology
                      accessible across various clinical and laboratory settings.

                      Over time, the AI continuously improves through user feedback and new clinical data, ensuring
                      optimal performance across a wide range of restorative cases. By streamlining workflows
                      and minimizing variability, Dentbird Solutions enables faster, more predictable treatment
                      outcomes—ultimately contributing to the advancement of global oral healthcare standards.




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