Assessing outcomes of ear molding therapy by health care providers and convolutional neural network

Abstract

Ear molding therapy is a nonsurgical technique to correct certain congenital auricular deformities. While the advantages of nonsurgical treatments over otoplasty are well-described, few studies have assessed aesthetic outcomes. In this study, we compared assessments of outcomes of ear molding therapy for 283 ears by experienced healthcare providers and a previously developed deep learning CNN model. 2D photographs of ears were obtained as a standard of care in our onsite photography studio. Physician assistants (PAs) rated the photographs using a 5-point Likert scale ranging from 1(poor) to 5(excellent) and the CNN assessment was categorical, classifying each photo as either “normal” or “deformed”. On average, the PAs classified 75.6% of photographs as good to excellent outcomes (scores 4 and 5). Similarly, the CNN classified 75.3% of the photographs as normal. The inter-rater agreement between the PAs ranged between 72 and 81%, while there was a 69.6% agreement between the machine model and the inter-rater majority agreement between at least two PAs (i.e., when at least two PAs gave a simultaneous score < 4 or ≥ 4). This study shows that noninvasive ear molding therapy has excellent outcomes in general. In addition, it indicates that with further training and validation, machine learning techniques, like CNN, have the capability to accurately mimic provider assessment while removing the subjectivity of human evaluation making it a robust tool for ear deformity identification and outcome evaluation.

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks

“…Hallac et al found that the CNN GoogLeNet could serve as an ear deformity detection model with a test accuracy of about 94.1% ( 13 ). Besides, CNN was demonstrated to be a robust tool for assessing outcomes of ear molding therapy by removing the subjectivity of human evaluation ( 14 ). Similarly, Ye et al revealed that the CNN ResNet can evaluate reconstructed auricles in a manner resembling that of a medical student, indicating the potential of CNN for assessing the outcomes of auricular reconstruction ( 15 ).…”

“…Due to the complex shape and composition of the ear, it is difficult to develop mathematical models to identify deformities in the ear. Previous studies have developed CNN models to identify ear abnormalities from two-dimensional photographs with satisfactory results (13)(14)(15). To our knowledge, no CNN model has been reported to evaluate the severity of microtia, for the reported models only distinguished between normal ears and malformed ears.…”

A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities

“…Actually, the bio-metric features of the auricle are so individually specific, that make the ear shape available as another fingerprint for human recognition 12 , 13 . Researches have also been conducted to identify the auricular deformities and evaluate the corrective effects using convolutional neural networks 14 , 15 .…”