A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. PGM Knoops, A Papaioannou, A Borghi, et al.

Date: September 2019.
Source: Scientific Reports 9, 13597 (2019).
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.

Article: A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery.
Authors: Paul GM Knoops, Athanasios Papaioannou, Alessandro Borghi, Richard WF Breakey, Alexander T Wilson, Owase Jeelani, Stefanos Zafeiriou, Derek Steinbacher, Bonnie L Padwa, David J Dunaway, & Silvia Schievano. UCL Great Ormond Street Institute of Child Health and Imperial College London, London, UK.