A I [M L and D L ] in Thyroid Imaging

 Thyroid nodules are a common clinical problem, occurring in 19%-68% of the healthy population [1-3]. Ultrasonography (US) is an essential diagnostic tool used to assess the risk of malignancy and to inform decision-making regarding the use of fine-needle aspiration (FNA) and postFNA management in patients with thyroid nodules [1-3]. However, accurate recognition and consistent interpretation of US features are challenging for less-experienced operators, resulting in moderate to substantial interobserver and intraobserver variability [4-8]. In addition to experienced radiologists, many other clinicians-including endocrinologists, surgeons, nuclear medicine physicians, cytopathologists, family practice physicians, and other non-imaging specialists-perform thyroid US at primary care centers; therefore, unnecessary FNA and/or diagnostic surgery are commonly performed, placing a significant burden on the healthcare system and causing considerable anxiety to patients [1-3]. In addition, examining thyroid nodules on US is relatively labor-intensive due to their high prevalence in practice. Artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems, based on machine learning (ML) and deep learning (DL) techniques, have been introduced for thyroid cancer diagnosis  to overcome the limitations of US diagnosis by clinicians. Many studies have reported the potential roles of these systems in thyroid cancer diagnosis, and have demonstrated comparable or even higher diagnostic performance than experienced radiologists [813]. However, at this point, the use of AI tools in clinical practice is of great concern since most studies were designed as proof-of concept or technical feasibility research without a thorough external validation of real-world clinical performance [14-16]. Most studies have been based on algorithms developed by individual researchers, and only a few have investigated the use of commercially available systems. In this review, we discuss the clinical background, development, and validation studies of AI-based CAD systems in thyroid cancer diagnosis, and describe the future developmental directions of these systems for the personalized and optimized management of thyroid nodules.