Use of Convolutional Neural Networks to Evaluate Auricular Reconstruction Outcomes for Microtia
Mariam Tolba, Z. Jason Qian, Hung‐Fu C. Lin, Kristen W. Yeom, Mai Thy Truong
Abstract
OBJECTIVES: The objective of this study is to determine whether machine learning may be used for objective assessment of aesthetic outcomes of auricular reconstructive surgery. METHODS: Images of normal and reconstructed auricles were obtained from internet image search engines. Convolutional neural networks were constructed to identify auricles in 2D images in an auto-segmentation task and to evaluate whether an ear was normal versus reconstructed in a binary classification task. Images were then assigned a percent score for "normal" ear appearance based on confidence of the classification. RESULTS: Images of 1115 ears (600 normal and 515 reconstructed) were obtained. The auto-segmentation task identified auricles with 95.30% accuracy compared to manually segmented auricles. The binary classification task achieved 89.22% accuracy in identifying reconstructed ears. When the confidence of the classification was used to assign percent scores to "normal" appearance, the reconstructed ears were classified to a range of 2% (least like normal ears) to 98% (most like normal ears). CONCLUSION: Image-based analysis using machine learning can offer objective assessment without the bias of the patient or the surgeon. This methodology could be adapted to be used by surgeons to assess quality of operative outcome in clinical and research settings. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:2413-2416, 2023.