This study, proposed by Wang et al., aims to explore how different structures and shapes of the upper jaw produce distinct facial appearances, like mid-face depression or protrusion, using Asian females as the targeted demographic. By doing this, clinicians would be able to more accurately diagnose patients and implement better treatment strategies. Midfacial prominence is already being evaluated using facial angle and nasomental angle measurements, however, there are limitations that prevent these measurements from being fully accurate and comprehensive. So, the researchers built a ML-based diagnostic model to see which of the thirteen measurement models would perform the best. The models were evaluated on internal and external test sets, testing straightforward, complex, and balanced cases. Results showed that the XGBoost model was the most suitable for diagnosing Asian female faces. The subnasale-to-forehead and maxilla-to-skull base anteroposterior relations were the most critical features. On the other hand, facial angle was the least reliable, but the model still performed well in the case without the facial angle. Although this ML-based model provides more valuable and accurate results than previous methods, its use is still only limited to the Asian female population.