Testing a better method of predicting postsurgery soft tissue response in Class II patients: A prospective study and validity assessment

Abstract

After conducting a prospective study using a new data set, this new prediction method again performed well. In addition, a cross-validation technique may be considered a better option than simple validation when constructing a prediction algorithm.

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

“…Conventional key landmarks that have previously been required for cephalometric analysis as well as a large number of other landmarks are essential for accurately predicting posttreatment changes. [22][23][24][25] As a limitation of the present study, the way AI learned during the training session and how it identified landmarks later in the test step are not explainable without describing computer science jargon. Although some technical details have been necessary, this present study intended to focus on showcasing the results from AI.…”

“…For those purposes, an expanded number, even hundreds, of variables of anatomic landmarks is necessary. 14,[22][23][24][25] In the present study, unlike the learning data that included images from a variety of malocclusion patients, the test images were selected from patients who had a severe type of mandibular deficiency, prognathism, or facial asymmetry. They eventually had orthognathic surgeries performed.…”

Predicting soft tissue changes after orthognathic surgery: The sparse partial least squares method

“…The x coordinates represented the horizontal distance from the vertical axis, and the y coordinates represented the vertical distance from the horizontal axis measured in millimeters, which followed the same superimposition method that appeared in previous PLS studies. 3,4,6,7 The coordinates of every landmark on each tracing were sequentially computed relative to the x and y reference system with a custom digitizing program via Microsoft Visual C# 2010 (Microsoft, Redmond, Wash).…”

“…For example, there is no single prediction algorithm that can be applied to various surgical movements simultaneously. Surgical movements differ for Class II and Class III skeletal discrepancies, and prior studies [1][2][3][4][5][6][7] derived a different prediction equation for each skeletal discrepancy. Prediction results for bimaxillary surgery were reported 8,9 to be less accurate than those for single-jaw surgeries.…”

“…Recent studies 3,4,6,7 showed that the prediction equation based on the partial least squares (PLS) method resulted in increased accuracy and enabled consideration of a variety of input variables. PLS prediction results, in comparison with those associated with conventional, ordinary least squares (OLS) approaches such as regression analyses, demonstrated that PLS was more accurate than were OLS statistical methods.…”