A deep‐learning‐based approach for adenoid hypertrophy diagnosis

Abstract

Purpose: Adenoid hypertrophy is a pathological hyperplasia of adenoids and may cause snoring, apnea, and impede breathing during sleep. In clinical practice, radiologists diagnose the severity of adenoid hypertrophy by measuring the ratio of adenoid width (A) to nasopharyngeal width (N) according to the lateral cephalogram, which indicates the locations of four keypoints. The entire diagnostic process is tedious and time-consuming due to the acquisition of A and N. Thus, there is an urgent need to develop computer-aided diagnostic tools for adenoid hypertrophy. Methods: In this paper, we first propose the use of deep learning to solve the problem of adenoid hypertrophy classification. Deep learning driven by big data has developed greatly in the image processing field. However, obtaining a large amount of training data is hard, making the application of deep learning to medical images more difficult. This paper proposes a keypoint localization method to incorporate more prior information to improve the performance of the model under limited data. Furthermore, we design a novel regularized term called VerticalLoss to capture the vertical relationship between keypoints to provide prior information to strengthen the network performance. Results: To evaluate the performance of our proposed method, we conducted experiments with a clinical dataset from the First Affiliated Hospital of Anhui Medical University consisting of a total of 688 patients. As our results show, we obtained a classification accuracy of 95.6%, a macro F1-score of 0.957, and an average AN ratio error of 0.026. Furthermore, we obtained a macro F1-score of 0.89, a classification accuracy of 94%, and an average AN ratio error of 0.027 while using only half of the data for training. Conclusions: The study shows that our proposed method can achieve satisfactory results in the task of adenoid hypertrophy classification. Our approach incorporates more prior information, which is especially important in the field of medical imaging, where it is difficult to obtain large amounts of training data.

Automated Radiographic Evaluation of Adenoid Hypertrophy Based on VGG-Lite

“…A previous study applied deep learning for keypoint localization on lateral cephalometric images for automatic diagnosis of adenoid hypertrophy (Shen et al 2020). Those researchers measured the AN ratio according to landmarks generated by deep learning.…”

“…A deep learning method has also been applied to orthodontic diagnosis, offering sensitivity, specificity, and accuracy over 90% for vertical and sagittal skeletal diagnosis (Yu et al 2020). So far, only 1 study has applied deep learning for diagnosis of adenoid hypertrophy (Shen et al 2020). Those investigators collected a total of 688 X-ray images of patients with adenoid hypertrophy and divided them into 3 groups for training (488 images), validation (64 images), and testing (116 images).…”

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

“…To our knowledge, so far there are only two studies that have applied AI techniques to AH diagnosis. One of them proposed the VGG-Lite model for the automated evaluation of AH but eliminated the process of landmark identification [36]; the other [21] explored the use of AI in AH diagnosis based on magnetic resonance imaging (MRI), which is not routinely used in orthodontic practice. In contrast, the present study was based on lateral cephalometry, a routine examination conducted by orthodontists.…”

“…Moreover, the application of deep learning algorithms in cephalometric analysis and the diagnosis of skeletal classification has shown good performance [17][18][19][20]. However, research on the use of deep-learning-based methods in radiographic AH assessment is still limited [21].…”

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

“…Screening the presence of upper-airway obstruction, especially adenoid hypertrophy, is critical for orthodontic diagnosis and treatment planning. The application of AI in upper-airway obstruction assessment is summarized in Table 3 [109][110][111][112][113][114][115][116][117]. Detecting adenoid hypertrophy based on lateral cephalograms has been proven to be highly accurate and reliable [118,119].…”