Purpose: During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this nerve is often obstructed by the thyroid itself, increasing the risk of serious complications.This study explores the application of an augmented reality (AR) system to facilitate easier identification of the RLN during transoral endoscopic thyroidectomy. Methods: Three patients scheduled for transoral endoscopic thyroidectomy were enrolled in this proof-of-concept study. Preoperative computed tomography scans were used to create an AR model that included the thyroid, trachea, veins, arteries, and RLN. The model was overlaid onto real-time endoscopic camera images during live surgeries.Manual registration of the AR model was performed using a customized controller. The model was aligned with surgical landmarks such as the trachea and common carotid artery. Manual registration accuracy was assessed using the Dice similarity coefficient (DSC) to evaluate the alignment between the real RLN and the RLN of the AR model. Results: The 3 patients included were female (mean age, 33.3 ± 15.7 years), and the mean tumor size was 1.0 ± 0.3 cm. All patients underwent transoral endoscopic thyroidectomy of the right lobe. Final histopathological diagnoses comprised 2 papillary thyroid carcinomas and one follicular adenoma. The manual registration accuracy was 0.60, 0.70, and 0.57 for patients 1, 2, and 3, respectively, with a mean value of 0.6 ± 0.1. Conclusion The application of an AR system during transoral endoscopic thyroidectomy proved feasible and demonstrated potential for improving the localization of anatomical structures, particularly the RLN, as indicated by a moderate DSC.

Purpose: During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this nerve is often obstructed by the thyroid itself, increasing the risk of serious complications.This study explores the application of an augmented reality (AR) system to facilitate easier identification of the RLN during transoral endoscopic thyroidectomy. Methods: Three patients scheduled for transoral endoscopic thyroidectomy were enrolled in this proof-of-concept study. Preoperative computed tomography scans were used to create an AR model that included the thyroid, trachea, veins, arteries, and RLN. The model was overlaid onto real-time endoscopic camera images during live surgeries.Manual registration of the AR model was performed using a customized controller. The model was aligned with surgical landmarks such as the trachea and common carotid artery. Manual registration accuracy was assessed using the Dice similarity coefficient (DSC) to evaluate the alignment between the real RLN and the RLN of the AR model. Results: The 3 patients included were female (mean age, 33.3 ± 15.7 years), and the mean tumor size was 1.0 ± 0.3 cm. All patients underwent transoral endoscopic thyroidectomy of the right lobe. Final histopathological diagnoses comprised 2 papillary thyroid carcinomas and one follicular adenoma. The manual registration accuracy was 0.60, 0.70, and 0.57 for patients 1, 2, and 3, respectively, with a mean value of 0.6 ± 0.1. Conclusion The application of an AR system during transoral endoscopic thyroidectomy proved feasible and demonstrated potential for improving the localization of anatomical structures, particularly the RLN, as indicated by a moderate DSC.

Purpose: During transoral endoscopic thyroidectomy, preserving the recurrent laryngeal nerve (RLN) is a major challenge because visualization of this nerve is often obstructed by the thyroid itself, increasing the risk of serious complications.This study explores the application of an augmented reality (AR) system to facilitate easier identification of the RLN during transoral endoscopic thyroidectomy. Methods: Three patients scheduled for transoral endoscopic thyroidectomy were enrolled in this proof-of-concept study. Preoperative computed tomography scans were used to create an AR model that included the thyroid, trachea, veins, arteries, and RLN. The model was overlaid onto real-time endoscopic camera images during live surgeries.Manual registration of the AR model was performed using a customized controller. The model was aligned with surgical landmarks such as the trachea and common carotid artery. Manual registration accuracy was assessed using the Dice similarity coefficient (DSC) to evaluate the alignment between the real RLN and the RLN of the AR model. Results: The 3 patients included were female (mean age, 33.3 ± 15.7 years), and the mean tumor size was 1.0 ± 0.3 cm. All patients underwent transoral endoscopic thyroidectomy of the right lobe. Final histopathological diagnoses comprised 2 papillary thyroid carcinomas and one follicular adenoma. The manual registration accuracy was 0.60, 0.70, and 0.57 for patients 1, 2, and 3, respectively, with a mean value of 0.6 ± 0.1. Conclusion The application of an AR system during transoral endoscopic thyroidectomy proved feasible and demonstrated potential for improving the localization of anatomical structures, particularly the RLN, as indicated by a moderate DSC.

Purpose: Accurate anatomical knowledge and precise visualization are critical during liver surgery. We developed augmented reality (AR) software that overlays digital 3-dimensional (3D) models onto laparoscopic or robotic views, providing real-time visual aids for surgical navigation during 3D laparoscopic and robotic liver surgeries. This study assesses the accuracy of manual registration and the subjective perception of this AR software by the operator. Methods: Ten consecutive patients undergoing 3D laparoscopic or robotic liver surgery from December 2023 to February 2024 were selected for application of the AR software during surgery. Manual registration accuracy was quantified postregistration using the Dice similarity coefficient (DSC) to compare the stereoscopic and virtual liver images. A 6-question operator survey, using a 5-point Likert scale, was administered after each surgery to evaluate the software’s helpfulness in clinical settings. Results: Seven males and 3 females (mean age, 62.4 ± 6.4 years) underwent liver surgery (3D laparoscopic, 5; robotic, 5).Surgical procedures included 4 right hemihepatectomies, 1 extended left hemihepatectomy, 1 left lateral sectionectomy, and 4 segmentectomies. The mean tumor size was 4.4 ± 2.2 cm (range, 1.0–7.5 cm). The mean DSC was 0.912 ± 0.052 (range, 0.879–0.954). The operator rated registration alignment favorably before (mean score, 3.9 ± 1.1) and after mobilization (mean score, 4.1 ± 1.2). The software was reported as very helpful overall (mean score, 4.2 ± 0.8), and in locating blood vessels (4.2 ± 0.6) and tumors (4.3 ± 0.7). Conclusion Clinical application of the AR software during 3D laparoscopic and robotic liver surgery is feasible, with favorable registration accuracy and high operator perception of helpfulness.

During the course of evaluation and management of neck masses, consideration for Immunoglobulin G4-related disease (IgG4-RD) should be given. IgG4-RD is relatively a new growing entity of immune-mediated origin, characterized by a mass-forming lesion, the infiltration of IgG4-positive plasma cells and occasionally elevated serum IgG4. The most common manifestations are parotid and lacrimal swelling, lymphadenopathy and autoimmune pancreatitis. A previously healthy 72-year-old man was referred to our clinic with a 2-month history of left cervical lymph node enlargement without systemic manifestations . A cervical lymph node biopsy was planned because of elevated serum IgG4 levels. Pathological findings showed prominent infiltration of IgG4-postive plasma cells in the lymph node. After steroid therapy, a computed tomography scan revealed a decrease in the cervical lymph node size. This case illustrates the importance of including IgG4-RD in the differential diagnosis of a cervical lymph node enlargement.
Aged ; Biopsy ; Diagnosis, Differential ; Humans ; Immunoglobulin G ; Immunoglobulins ; Lymph Nodes* ; Lymphatic Diseases* ; Neck ; Pancreatitis ; Plasma Cells