Abstract
Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation. The model, trained and validated with 4,261 faces of healthy volunteers and orthognathic (jaw) surgery patients, diagnoses patients with 95.5% sensitivity and 95.2% specificity, and simulates surgical outcomes with a mean accuracy of 1.1 ± 0.3 mm. We demonstrate how this model could fully-automatically aid diagnosis and provide patient-specific treatment plans from a 3D scan alone, to help efficient clinical decision making and improve clinical understanding of face shape as a marker for primary and secondary surgery.
Highlights
Over 200,000 maxillofacial procedures, including orthognathic surgery, are performed in the USA every year to treat a range of diseases, defects and injuries in the head, neck and face[1]
This model could potentially transform patient-specific clinical decision making in orthognathic surgery and other fields of plastic and reconstructive surgery
To demonstrate the power of our large-scale clinical 3D morphable models (3DMM), we present the following results: (1) a description of how the models were built and the 3D face databases used, including intrinsic statistical validation metrics; (2) an evaluation of mean face shape to compare, quantitatively and qualitatively, how patient faces differ from volunteer faces preoperatively and postoperatively; (3) a manifold visualisation to compare high-dimensional patient and volunteer shape data; (4) a classification for automated diagnosis of faces with orthognathic shape features as an indication for orthognathic surgery, and (5) an analysis of different regression techniques to simulate patient faces for automated patient-specific surgical planning
Summary
Over 200,000 maxillofacial procedures, including orthognathic (jaw) surgery, are performed in the USA every year to treat a range of diseases, defects and injuries in the head, neck and face[1] For these operations, vast quantities of patient data are collected[2], providing a great opportunity for the development of machine-learning-based methods, for use in clinical decision-making and to enable automated personalised medicine approaches[3,4]. We believe our proposed model is an important step towards making computer-assisted surgical planning cheaper, and more accessible for surgeons and patients This model could potentially transform patient-specific clinical decision making in orthognathic surgery and other fields of plastic and reconstructive surgery
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