Outcome-Driven Approach to Minimize the Risks of Facial Distortion Following CMF Surgery

摘要： 我们这个项目的主要临床动机是为患有以下疾病的患者提供个性化的精确护理 通过显著改进手术计划方法，改善颌骨(上颌和下颌)畸形。数字 患有颌骨畸形的患者的比例每年都在上升。 正颌外科是通过重新定位骨段来治疗颌骨畸形的主要外科手术。 下巴上的。正颌手术的最终结果是通过最终的面部外观来判断的。虽然 面部软组织不是直接手术，面部会随着骨骼的变化而自动改变。 正颌外科需要广泛的外科计划。虽然我们可以准确地计划骨骼的运动 并在手术期间使用计算机辅助手术模拟(CASS)和3D打印将其传输给患者， 外科医生仍然无法实际预测手术计划期间面部的变化，只是 希望术后恢复正常的脸部会自动恢复。然而，这一“心理线索” 这种方法并不可靠，因为面部变化并不完全遵循骨骼变化。问题是， 在有复合体缺陷的患者中更大。例如，如果患者有骨骼畸形和轻微的面部缺陷， 外科医生在手术前必须知道如何过度矫正骨骼以掩盖软组织缺陷。但 只有通过准确预测面部变化的方法才能获得这些信息。此外，从患者的 透视一下，最后的面部外观是他们非常关心的。因此，这是极其重要的，因为 医生和病人，以准确地预测面部变化。 在之前的项目期间，我们在预测面部变化方面取得了重大成就 使用有限元(FE)方法跟踪骨骼运动。然而，这种方法仍然需要相当大的 准备有限元模型的时间。此外，不是决定最终的手术结果( 面部外观)首先，目前的方法仍然是被动地预测术后面部的变化 骨科手术。这些障碍极大地阻碍了外科医生在临床环境中实际使用它。 我们的假设是，只有当外科医生 在计划骨科手术之前，能够确定最终的治疗结果，期望的术后面部。 为了验证我们的假设，我们建议将结果驱动和基于机器学习的技术集成在一起 首先要估计一个理想的术后面部，然后计划骨科手术。 拟议的项目将对提高患者护理质量产生重大临床影响。它将使 临床医生根据面部和骨骼信息，使用单一的 在他们的常规临床实践中使用软件。它还将使用结果来革命性地改变手术计划技术- 驱动法，即首先评估所需的术后面部，然后计划骨科手术。

Abstract: Our main clinical motivation of this project is to provide personalized precision care to patients with jaw (both maxilla and mandible) deformities by significantly improving surgical planning method. The number of patients suffering from jaw deformities is escalating each year. Orthognathic surgery is a main surgical procedure to treat jaw deformities by repositioning bony segments of the jaws. The ultimate outcomes of orthognathic surgery are judged by the final facial appearance. Although the facial soft tissues are not directly operated on, the face changes “automatically” following the bony changes. Orthognathic surgery requires extensive surgical planning. While we can accurately plan the bony movements and transfer it to the patient during the surgery using computer-aided surgical simulation (CASS) and 3D printing, surgeons are still unable to practically predict the facial changes during the surgical planning, and just hope that a postoperative normal face will be “automatically” restored. However, this “mental-clue” approach is not reliable because the facial change does not exactly follow bony change. The problem is even bigger in patients with composite defects. For example, if a patient has a skeletal deformity and mild facial defect, a surgeon must know, before surgery, how to overcorrect the skeleton to camouflage the soft-tissue defect. But this information can only be attained by accurate method to predict facial changes. In addition, from patient’s perspective, the final facial appearance is great concern to them. Therefore, it is extremely important, for both doctors and patients, to accurately predict facial changes. In the previous project period, we have made significant achievements in predicting facial changes following bony movements using finite element (FE) method. However, this approach still requires a considerable amount of time to prepare FE models. In addition, rather than determining the ultimate surgical outcome (the postoperative facial appearance) first, the current method is still to predict the facial change passively following the bony surgery. These hurdles greatly prevent surgeons from practically using it in the clinical setting. Our hypothesis is that a personalized precision treatment outcome can only be achieved if surgeons are able to determine the final treatment outcome, a desired postoperative face, before planning the bony surgery. To test our hypothesis, we propose to integrate outcome-driven and machine learning-based techniques together to first estimate a desired postoperative face, and then plan the bony surgery. The proposed project will have a significant clinical impact on improving patient care quality. It will enable clinicians to develop an optimal surgical plan based on both facial and bony information, on-the-fly, using a single software in their routine clinical practice. It will also revolutionize the surgical planning technique using outcome- driven approach, i.e., to first estimate a desired postoperative face and then plan the bony surgery.