Emily Borsting1, Robert DeSimone1, Mustafa Ascha2, Mona Ascha3. 1. Department of Plastic Surgery, University of California, Irvine, CA. 2. Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine. 3. Division of Plastic and Reconstructive Surgery, Department of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH.
Abstract
BACKGROUND: Advances in deep learning (DL) have been transformative in computer vision and natural language processing, as well as in healthcare. The authors present a novel application of DL to plastic surgery. Here, the authors describe and demonstrate the mobile deployment of a deep neural network that predicts rhinoplasty status, assess model accuracy compared to surgeons, and describe future directions for such applications in plastic surgery. METHODS: A deep convolutional neural network ("RhinoNet") was developed to classify rhinoplasty images using only pixels and rhinoplasty status labels ("before"/"after") as inputs. RhinoNet was trained using a dataset of 22,686 before and after photos which were collected from publicly available sites. Network classification was compared to that of plastic surgery attendings and residents on 2269 previously-unseen test-set images. RESULTS: RhinoNet correctly predicted rhinoplasty status in 85% of the test-set images. Sensitivity and specificity of model predictions were 0.840 (0.79-0.89) and 0.826 (0.77-0.88), respectively; the corresponding values for expert consensus predictions were 0.814 (0.76-0.87) and 0.867 (0.82-0.91). RhinoNet and humans performed with effectively equivalent accuracy in this classification task. CONCLUSION: The authors describe the development of DL applications to identify the presence of superficial surgical procedures solely from images and labels. DL is especially well suited for unstructured, high-fidelity visual and auditory data that does not lend itself to classical statistical analysis, and may be deployed as mobile applications for potentially unbridled use, so the authors expect DL to play a key role in many areas of plastic surgery.
BACKGROUND: Advances in deep learning (DL) have been transformative in computer vision and natural language processing, as well as in healthcare. The authors present a novel application of DL to plastic surgery. Here, the authors describe and demonstrate the mobile deployment of a deep neural network that predicts rhinoplasty status, assess model accuracy compared to surgeons, and describe future directions for such applications in plastic surgery. METHODS: A deep convolutional neural network ("RhinoNet") was developed to classify rhinoplasty images using only pixels and rhinoplasty status labels ("before"/"after") as inputs. RhinoNet was trained using a dataset of 22,686 before and after photos which were collected from publicly available sites. Network classification was compared to that of plastic surgery attendings and residents on 2269 previously-unseen test-set images. RESULTS: RhinoNet correctly predicted rhinoplasty status in 85% of the test-set images. Sensitivity and specificity of model predictions were 0.840 (0.79-0.89) and 0.826 (0.77-0.88), respectively; the corresponding values for expert consensus predictions were 0.814 (0.76-0.87) and 0.867 (0.82-0.91). RhinoNet and humans performed with effectively equivalent accuracy in this classification task. CONCLUSION: The authors describe the development of DL applications to identify the presence of superficial surgical procedures solely from images and labels. DL is especially well suited for unstructured, high-fidelity visual and auditory data that does not lend itself to classical statistical analysis, and may be deployed as mobile applications for potentially unbridled use, so the authors expect DL to play a key role in many areas of plastic surgery.
Authors: Antonio Mario Bulfamante; Francesco Ferella; Austin Michael Miller; Cecilia Rosso; Carlotta Pipolo; Emanuela Fuccillo; Giovanni Felisati; Alberto Maria Saibene Journal: Eur Arch Otorhinolaryngol Date: 2022-10-19 Impact factor: 3.236