Robotic thyroidectomy using Artificial Intelligence (AI) model in real-time recognizing Recurrent Laryngeal Nerves...

Area of Science:

• Surgical Oncology
• Robotic Surgery
• Artificial Intelligence in Medicine

Background:

• Robotic thyroidectomy offers advantages over endoscopic approaches, including 3D visualization and instrument articulation.
• Patient anxiety regarding cosmetic outcomes and voice changes drives demand for scarless thyroidectomy techniques.
• AI-powered image recognition enhances real-time identification of critical structures like the recurrent laryngeal nerve (RLN).

Purpose of the Study:

• To demonstrate the feasibility and safety of AI-assisted robotic thyroidectomy for complex cases.
• To evaluate the efficacy of real-time AI navigation for recurrent laryngeal nerve (RLN) dissection.
• To highlight the benefits of integrating AI with robotic surgery for improved patient and surgeon experience.

Main Methods:

• A 42-year-old female patient with papillary thyroid carcinoma underwent robotic thyroidectomy with AI-assisted real-time RLN navigation.
• The procedure involved right lobectomy, isthmusectomy, and central compartment lymph node dissection (CCND).
• An AI system provided continuous real-time guidance during RLN dissection.

Main Results:

• The AI system demonstrated robust performance in real-time RLN navigation.
• The surgery was completed in 120 minutes with minimal blood loss.
• The patient experienced no postoperative hoarseness and was discharged on postoperative day 3.

Conclusions:

• AI-assisted robotic thyroidectomy is feasible and safe for complex cases, particularly for RLN dissection.
• This approach integrates a robotic platform with AI real-time navigation and intraoperative nerve monitoring (IONM).
• The technology transitions surgery towards a data-driven, intelligent paradigm, standardizing procedures and shortening the learning curve.