We present a case study to demonstrate the process of developing a machine-learning model for maxillofacial surgical assessment. Hospitals provided a dataset of over 500K maxillofacial injury cases. Patient demographics, medical history, and radiographic images of the facial bones were all included in the dataset. Age, gender, type of injury, and location of injury were all extracted as relevant features. Using the machine learning framework, a neural network model was created. The model had a 95% accuracy rate, indicating that it was very good at predicting the severity of maxillofacial injuries.

Conclusion

Machine learning and neural networks have the potential to transform maxillofacial surgical assessment, resulting in faster and more accurate diagnosis and treatment planning. Creating a machine learning model, on the other hand, necessitates careful data collection, preprocessing, feature extraction, and model development. Healthcare providers can use machine learning and neural networks for maxillofacial surgical assessment to provide better care for their patients by following a comprehensive guide to machine learning and neural networks for maxillofacial surgical assessment.