Deep learning based orthognathic surgical planning

Authors
Fang, Xi
ORCID
https://orcid.org/0000-0001-8938-1732
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Other Contributors
Yan, Pingkun
Wang, Ge
Hahn, Juergen
Radke, Richard, J.
Yan, Pingkun
Issue Date
2023-11
Keywords
Biomedical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Orthognathic surgery, which addresses issues with the jaw and face, is an intricate procedure that requires meticulous planning. Computer-Assisted Surgical Simulation (CASS) provides surgeons with a robust platform to refine their surgical strategies through simulated practice before the actual intervention, culminating in a detailed plan that guides the surgical correction. Despite the advancements, accurately crafting this plan is a significant challenge due to the reliability of reference model estimation and the speed of facial biomechanical simulations, as well as the iterative nature of plan revisions. These simulations aid surgeons in fine-tuning their approach, ensuring optimal functional and aesthetic outcomes through a cycle of evaluation and modification. Nonetheless, CASS has shown limitations in the efficiency and precision of the surgical plans which directly affect surgical outcomes. This thesis proposes the integration of deep learning techniques to overcome these limitations. Delving into the relatively untapped potential of deep learning for orthognathic surgery planning, this research confronted three major challenges: 1) How to develop a patient-specific reference bony model leveraging a normal subject dictionary for reliable surgical planning? 2) How can deep learning model the complex non-linear relationship between face and bone to expedite the facial simulation process? 3) How to directly target the facial soft-tissue during the planning phase to remove the need for repeated adjustments and to streamline outcome-focused planning? Overcoming these barriers is essential for a deep learning method that is not only technologically advanced but also clear, efficient, reliable, and clinically relevant. The structure of this dissertation is anchored around three specific aims: Our first aim focuses on the development of a self-supervised learning framework anchored in a deep query network. This framework utilizes a dictionary of normal subjects to facilitate the development of a dependable surgical plan customized for each patient. This innovative approach significantly diminishes initial planning time and heightens plan quality, streamlining the surgeon's workflow. The second aim enhances the precision and speed of facial simulation by integrating the power of deep learning with traditional surgical biomechanical simulation techniques like Finite Element Methods (FEM). This enhancement is realized through the introduction of a cutting-edge attentive correspondence assisted movement transformation network (ACMT-Net) that captures and models the complex non-linear relationship between the bone framework and facial tissues. The final aim shifts the planning paradigm. Instead of primarily looking at bone structures to devise the surgical plan, greater emphasis is placed on the anticipated postoperative appearance of the patient's face. This shift ensures that the surgical plan is formulated just once, automatically validated, and consistent with the patient’s aesthetic goals. This thesis outlines a pathway that adeptly circumvents the fundamental limitations of conventional CASS. It emphasizes the importance of achieving a patient's optimal facial appearance after surgery by incorporating the robust capabilities of deep learning into CASS. This resultant methodology produces a surgical planning methodology that is transparent, accurate, and adaptable, meeting modern surgical needs. This innovative approach aims to revolutionize orthognathic surgical planning by merging cutting-edge deep learning technology with a concentrated emphasis on post-operative aesthetic outcomes.
Description
November2023
Department
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.
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