Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Rhinoplasty and facial plastic surgery are among the most frequently performed surgical procedures in the world. Although the underlying anatomical features of nose and face are very well known, performing a successful facial surgery requires not only surgical skills but also aesthetical talent from surgeon. Sculpting facial features surgically in correct proportions to end up with an aesthetically pleasing result is highly difficult. To further complicate the matter, some patients may have some anatomical features which affect rhinoplasty operation outcome negatively. If goes undetected, these anatomical variants jeopardize the surgery causing unexpected rhinoplasty outcomes. In this study, a model is developed with the aid of artificial intelligence tools, which analyses facial features of the patient from photograph, and generates an index of “appropriateness” of the facial features and an index of existence of anatomical variants that effect rhinoplasty negatively. The software tool developed is intended to detect the variants and warn the surgeon before the surgery. Another purpose of the tool is to generate an objective score to assess the outcome of the surgery.
