A deep-learning-based approach for adenoid hypertrophy diagnosis.

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.