In 2000, the da Vinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) was approved by the U.S. Food and Drug Administration (FDA) for general laparoscopic surgery. Robotic surgery offers 3-dimensional (3D) imaging, sophisticated endowrist movement, tremor reduction, and excellent ergonomics, compared to endoscopic remote-access surgery. Following its adoption in 2007, robotic thyroidectomy began to replace endoscopic remote-access approaches [12345].

Despite its advantages, robotic thyroidectomy is challenging for inexperienced surgeons for several reasons, including high cost, insufficient chance of training, and most importantly, safety issues. During robotic thyroidectomy, surgeons cannot use palpation. Moreover, identification of neighboring structures covered by tissue is often difficult, potentially leading to injury causing severe damage. Serious complications associated with robotic thyroidectomy have been reported, including brachial plexus injury, tracheal perforation, and flap perforation [6789]. Owing to safety concerns, FDA revoked the use of the robot in thyroid surgery in 2011 [10].

Within the medical field, augmented reality (AR) is an image guided technology in which computer generated images are superimposed onto the live video feed of the surgical view, to provide a composite view in real time. Incorporation of AR in laparoscopic or robotic surgery aids the surgeon to more easily perceive anatomical structures intuitively which helps to identify structures covered or hidden by surrounding tissue. Although there have been attempts to introduce AR laparoscopic surgery, the manufacturers of robotic systems have not yet released an AR-merged robotic system. Thus, at present AR is not readily available.

In this study, we created AR images using preoperative CT images and developed a program to control the AR images. We then superimposed the generated AR images onto the real-time operative view during robotic thyroidectomy.

Because AR integrates computer graphics and real images into a single and unified view, it has potential to overcome the obstacles caused by limited operative view. A number of trials have applied AR in laparoscopic surgeries [11121314]. However, there are several technical barriers to AR in laparoscopic or robotic surgery.

First, recognition of anatomical landmarks, essential for overlaying AR images on top of the target organs, is often difficult. This difficulty is more obvious during surgery on intraperitoneal organs because the target organs continuously move owing to respiration or patient position. Furthermore, the application of AR on deformable organs, such as the liver or pancreas, is challenging because, the overlaid AR image is likely to become separated from the operative image when deformity occurs. To avoid such difficulties, nondeformable organs such as the kidney or adrenal gland have been used as target organs in AR registration [15]. Alternatively, navigation aids have been useful to guide manual registration [16]. For deformable organs, AR is of limited applicability unless the AR registration is able to respond in real time to changes in the organs' shape. Several studies suggested adaptable registration methods in deformable organs. One study used the liver edges as landmarks to develop an automated delineation method to overlay an AR image on the liver during laparoscopic liver surgery [17]. Moreover, the application of AR is still limited in clinical settings because it increases surgical time significantly and registration is still inaccurate.

To our knowledge, this is the first study to apply AR in robotic thyroidectomy. We selected thyroidectomy to test AR on robotic surgery because of its popularity in Korea and technical applicability. Robotic thyroidectomy is one of the most commonly performed robotic procedures owing to its cosmetic advantages compared to conventional open thyroidectomy [5]. Moreover, it was technically easy to apply AR to this procedure because, although the thyroid gland is deformable, the surgical procedure required total resection of a lobe of the thyroid gland without partial preservation, and there was no requirement to trace the thyroid gland. Moreover, neighboring organs (common carotid arteries, trachea, and esophagus), which often lie beyond the operative view and must be protected during surgery, are nondeformable and easy to visualize in AR.

Before we applied AR images on the real-time surgical image, we superimposed the AR image on a 3D printing model and quantitatively evaluated the accuracy of manual registration using DSC. The DSC value was satisfactory compared to a previous study [18]. Then we overlaid the AR images on the real time surgical view. We also demonstrated that the AR image location yielded accurate registration. The AR image location information may be useful for developing automatic or semiautomatic registration algorithms.

There are several limitations in this study. First, the AR images were overlaid manually. Manual registration is advantageous because, compared to automatic registration, it is easy to apply and can be certified as a clinical product. However, automatic registration is more attractive because it is convenient and reduces surgical time. In laparoscopic surgeries, methods of automatic registration that recognize landmarks automatically using point-based, surface-based, or volume-based approaches are evolving rapidly. Thus, by recognizing easily noticeable landmarks such as the trachea or thyroid cartilage, automatic registration may become possible in robotic thyroidectomy.

Second, we used a separate monitor to operate the AR, and the surgeon had to pause the surgery to view the AR image. This was inevitable because the integration of AR on the robotic monitor is currently impossible due to a license issue. Because several companies are developing robotic surgical systems, integration will most likely be possible in the future.

Lastly, the current technology lacks a tracking system. We were not able to update the AR image with camera motion and needed to reset the image registration whenever the operative angle changed. The integrated movement of AR may develop via an optical tracking system or tracking anatomical landmarks [19]. However, at present all tracking technologies are impractical because the equipment is costly and needs extensive calibration, a main cause of inaccurate registration [20].

In conclusion, organs that should be protected during robotic thyroidectomy were clearly visualized using AR based on preoperative CT images. Although current AR in robotic thyroidectomy has limited clinical applicability due to technical and commercial limitations, AR in robotic surgery has several advantages that may enhance surgical safety.