  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.
