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

以结果为导向的方法，最大限度地降低 CMF 手术后面部变形的风险

• 批准号: 10225298
• 负责人: James J Xia
• 金额: $ 61.66万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10225298
• 关键词: 3-Dimensional; 3D Print; Achievement; Algorithms; American; Anatomy; Appearance; Biomechanics; Clinical; Complex; Computer Assisted; Computer software; Congenital Abnormality; Databases; Defect; Deformity; Development; Ear; Elements; Ensure; Face; Generations; Goals; Head; Hour; Individual; Jaw; Laboratories; Learning; Lip structure; Location; Machine Learning; Mandible; Maxilla; Methods; Modeling; Motivation; Movement; Operative Surgical Procedures; Outcome; Patient Care; Patients; Planning Techniques; Postoperative Period; Precision therapeutics; Process; Progress Reports; Psyche structure; Quality of Care; Reconstructive Surgical Procedures; Reporting; Residual state; Running; Shapes; Skeleton; Slide; Structure; Surgeon; Syndrome; System; Techniques; Testing; Time; Trauma; Treatment outcome; Validation; Work; base; bone; clinical practice; craniomaxillofacial; design; improved; learning network; orthognathic; personalized care; prevent; psychologic; risk minimization; skeletal; soft tissue; success; surgery outcome; treatment planning; virtual surgery

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.

抽象的： 该项目的主要临床动机是为患者提供个性化的精确护理 下颌（上颌骨和下颌骨）畸形通过显着改善手术计划方法。数字 每年患有下颌畸形的患者正在上升。 正牙手术是通过重新定位骨段来治疗下颌畸形的主要手术程序 下巴。正牙手术的最终结果是根据最终的面部外观来判断的。虽然 面部软尖未直接操作，骨变化后，面部“自动变化”。 正牙手术需要广泛的手术计划。虽然我们可以准确计划奖金运动 并在手术期间使用计算机辅助手术模拟（CASS）和3D打印将其转移给患者， 外科医生仍然无法实际预测外科手术计划期间的面部变化，而 希望术后正常面部将“自动”恢复。但是，这个“心理效果” 方法不是可靠的，因为面部变化并不能完全遵循骨变化。问题甚至是 复合缺陷的患者更大。例如，如果患者患有骨骼畸形和轻度面部缺陷，则 外科医生必须在手术前知道如何过度纠正骨骼以伪装软组织缺陷。但 此信息只能通过准确的方法附加以预测面部变化。此外，从患者的 透视，最终的面部外观对他们来说是非常关注的问题。因此，这非常重要，因为 医生和患者，准确预测面部变化。 在上一个项目期间，我们在预测面部变化方面取得了重大成就 使用有限元（FE）方法进行奖励运动。但是，这种方法仍然需要考虑 准备FE模型的时间。另外，而不是确定最终的手术结果（ 术后面部外观）首先，当前的方法仍在被动预测面部变化 骨手术。这些障碍极大地阻止了外科医生在临床环境中实际使用它。 我们的假设是，只有在外科医生是外科医生才能实现个性化的精确治疗结果 在计划骨手术之前，可以确定最终的治疗结果，即所需的术后面孔。 为了检验我们的假设，我们建议将结果驱动和基于机器学习的技术整合在一起 首先估计所需的术后面孔，然后计划奖励手术。 拟议的项目将对改善患者护理质量产生重大临床影响。它将启用 临床医生使用一个单一的临床医生根据面部和奖励信息制定最佳外科计划 在常规临床实践中的软件。它还将使用结果彻底改变手术计划技术 - 驱动的方法，即首先估计所需的术后面孔，然后计划奖励手术。