Applying artificial intelligence algorithm in the design of a guide plate for mandibular angle ostectomy.

Area of Science:

• Oral and Maxillofacial Surgery
• Medical Artificial Intelligence
• Surgical Planning

Background:

• Surgical guide plates enhance surgical accuracy but are complex and time-consuming to design.
• Standardizing the design of these guides is crucial for improving efficiency and outcomes.

Purpose of the Study:

• To develop and evaluate an artificial intelligence (AI) algorithm for designing standardized mandibular angle ostectomy guide plates.
• To assess the AI algorithm's ability to reduce clinician workload and improve design efficiency.

Main Methods:

• An AI algorithm with a safety-ensuring penalty factor was developed and trained.
• A retrospective cohort study compared AI-generated guide plates against those designed by experienced residents for mandibular angle ostectomy.
• Outcomes included ostectomy symmetry, safety, shape, location, effectiveness, and design duration.

Main Results:

• The AI group showed significantly improved safety (P<0.05) and drastically reduced design duration (24.98 vs. 1685.08 hours, P<0.05).
• No significant differences were observed in ostectomy symmetry, shape, location, or effectiveness between AI-designed and resident-designed plates.
• Fifty patients (7 men, 43 women; mean age 27 years) were included in the study.

Conclusions:

• The AI algorithm effectively enhances safety and significantly reduces design time for surgical guide plates.
• AI demonstrates strong potential for application in accurate mandibular angle ostectomy, improving efficiency without compromising quality.