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A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future

Thyroid cancer is common worldwide, with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems. Earlier approaches along these lines relied on clinically relevant features that were manually identified by radiologists. With the recent success of artificial intelligence (AI), various new methods are being developed to identify these features in thyroid ultrasound automatically. In this paper, we present a systematic review of state-of-the-art on AI application in sonographic diagnosis of thyroid cancer. This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis. With more than 50 papers included in this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.


Introduction
The incidence of thyroid cancer has increased at an alarming rate in In this paper, we review various artificial intelligence (AI)-based CAD systems that have been proposed for thyroid ultrasound nodule analysis, including segmentation, detection and classification. Earlier reviews have focused on specific methods such as feature-based [7], deep learning [8], linear [9] and non-linear [10] approaches (see Table 1). We aim to provide a broader perspective in our discussion and compare the above-mentioned approaches to one another.
Our review also includes more recent publications in each of these approaches. This paper presents, for the first time, a systematic review of the existing literature on sonographic diagnosis of thyroid nodules, which covers both classical and deep learning methods. Figure 1 shows the histogram of the papers introducing novel machine learning-based techniques for thyroid nodule diagnosis.
Inclusion and exclusion criteria are summarized in Table 2. With more than 50 articles included in this survey, we focus on the methodological aspects of those works. We propose a structured analysis of the different approaches used for ultrasound-based thyroid tissue quantification. The paper is organized as follows. Section 2 presents an overview of the parameterization models for thyroid ultrasound nodule analysis. In Section 3, we will discuss the main findings of previous works, limitations, challenges and future trends that are specific to thyroid US images. Conclusions are presented in Section 4. to determine whether they were suitable for this review. We adopted a set of inclusion and exclusion criteria (see Table 2). The articles were downloaded, and the titles, abstract and methods sections were reviewed in order to verify that the articles met the aforementioned criteria. A total of 52 articles met the criteria for inclusion in the final review. Overall, our review was aimed at describing recent artificial intelligence achievements in sonographic diagnosis of thyroid nodules.
Based on the papers surveyed, we identified 2 major categories: 1) clinical features-based models; 2) machine learning-based models; and several other sub-categories of methods.   relevance (mRMR) [23] and T-test [24] were utilized to select those features which contribute most to the model. Thyroid and nodule segmentation: Poudel et al. [29] compared the performance of four popular segmentation methods, i.e., active contour without edges, graph cut, pixel based classifier and random forest classifier with 3D U-net [30] on Opencas thyroid dataset [31].
They concluded that 3D U-net outperforms other methods in terms of accuracy. Several deep learning architectures were utilized for nodule segmentation in the literature. In [32], mark-guided U-net [33] was proposed for segmentation of thyroid nodules. Four landmark points corresponding to the major and minor axes of a nodule were determined manually to guide the training and inference of U- and Resnet v2-50 [39] was employed in [40]. A retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China was performed in [41].  [48]. The best classification accuracy was reported using multi-task cascade VGG-16 model [49].
The main limitations of previous works are discussed below. and Opencas dataset [50], provide open access to detailed manuallyguided expert annotations of thyroid structure and nodules. Collection of a large comprehensive dataset is critical to develop future CAD systems that are robust to pathology.

Conclusions
In this paper, we presented the first systematic review of CAD systems for sonographic diagnosis of thyroid cancer. The continuing progress of machine learning have favored the development of complex and comprehensive models for sonographic diagnosis of thyroid nodules. As shown in this survey, the automatic parameterization of thyroid has been approached from different perspectives, (e.g. detection, segmentation and classification) using various methodologies.
The categorization of approaches in this paper provides a reference guide to the current techniques available for the analysis of thyroid US images. We have also indicated current challenges and future opportunities in CAD systems for analysis of thyroid US images.
New efficient machine learning models should embed the anatomical context inherent to the thyroid gland to provide the essential accuracy for clinical practice.