Automated Orthodontic Diagnosis from a Summary of Medical Findings (2023)

Abstract: We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.

## introduction to make a proper orthodontic diagnosis, dentists need a wealth of knowledge and experience. therefore, inexperienced dentists may overlook patient problems. artificial intelligence technologies, such as automatic diagnosis, are promising for preventing such errors by dentists @xcite . even for experienced dentists, automatic diagnosis technology can contribute in terms of workload reduction and improved efficiency. therefore, this study focuses on automatic diagnosis from medical findings texts written by dentists. while computer vision technologies for orthodontic applications, such as landmark identification from cephalometric x-rays @xcite and tooth segmentation on 3d dental surfaces captured by intraoral scanners @xcite , have been actively studied, research on natural language processing (nlp) technologies in this area has been limited. the only previous work @xcite applying nlp to automatic diagnosis from medical findings relied on bag-of-words (bow) feature extraction and support vector machine (svm) classification without the benefit of deep-learning (dl), which has been successfully applied to a variety of tasks in recent years. we hypothesize that this is due to the frequent use of technical terms not covered by even powerful pre-trained models, as well as the long documents in which the medical findings are written in an inconsistent style. specifically, the medical findings in this study average 1,886 tokens, with a maximum of 6,379 tokens, and contain many incomplete sentences, such as bullet points. to solve the problems of document length and style inconsistency, we utilize a short summary of medical findings written by dental specialists for automatic diagnosis. in contrast to the original medical findings, our summary is about 90% shorter (179 tokens on average) and consists only of complete sentences. with these advantages, summary text facilitates feature extraction from documents by encoders in dl models. to evaluate the effectiveness of automatic diagnosis from a summary of medical findings, we experimented with 970 japanese medical findings. experimental results on dl models of recurrent neural networks (rnn), convolutional neural networks (cnn), self-attention networks (san), and bert @xcite , a pre-trained san with masked language modeling objectives, showed that the cnn model achieved the best performance. furthermore, the performance of the svm and dl models was consistently improved when utilizing the summaries compared to the original medical findings. in particular, bert was the best performance improvement with the proposed method. ## related work with the development of dl technologies, many medical applications including the field of orthodontics are being addressed. ## proposed method we utilize deep learning-based document classification models for automatic diagnosis from medical findings in the field of orthodontics. we first describe these models in section 3.1. since medical findings are difficult to classify as they are, our proposed method instead utilizes a short summary of them, written in a consistent style. this is explained in section 3.2. ## evaluation to evaluate the effectiveness of the proposed method, automatic diagnosis is performed from the original medical findings or a summary of them, and their performance is compared. we treat this task as a multi-label document classification. ## conclusion in this study, we improved the performance of automatic diagnosis in orthodontic treatment by utilizing a short document that was manually summarized from medical findings by dentists. experimental results on japanese datasets show that the proposed method consistently improves the performance of various dl models. among them, our cnn model outperformed the existing model and updated the state-of-the-art performance. our future work includes the automatic generation of summaries and the development of multimodal automatic diagnosis taking into account image information. although this study utilized summaries of medical findings manually generated by experienced dentists, there is a substantial cost to creating such a dataset. it is desirable to develop an automatic diagnostic system that reduces the workload on dentists by automatically generating summaries. in addition, our dataset includes both facial and x-ray images. this allows us to develop multimodal models that incorporate findings from the field of computer vision, which are actively studied. multimodal automatic diagnostic systems that combine both image and linguistic information in a complementary manner are expected to have higher performance.