Objective Assessments and Annotation Frameworks in Microscopic Surgery: A Scoping Review

Area of Science:

• Surgical Education and Assessment
• Medical Informatics
• Otolaryngology Research

Background:

• Microscopic surgery assessment tools vary in objectivity and proficiency determination.
• Artificial intelligence (AI) offers objective analysis but lacks standardization in mastoidectomy.
• Previous efforts in objective microsurgical analysis need reconciliation with AI applications.

Purpose of the Study:

• To compile and compare objective features and assessment targets of microscopic surgery analysis tools.
• To inform the development of a standardized annotation framework for mastoidectomy.
• To evaluate the current landscape of surgical skill assessment methods.

Main Methods:

• A comprehensive scoping review of studies on objective analysis tools for microscopic surgery.
• Searched Ovid MEDLINE®, Embase, CENTRAL, Cochrane Database of Systematic Reviews, and Scopus.
• Extracted data on procedure type, evaluation modality, and classified tools into five domains: checklist/rating-based, instrument motion analysis, error identification, workflow modeling, and AI-assisted feedback.

Main Results:

• 52 studies met inclusion criteria, with 80.8% using manual assessments (checklists, rating scales).
• Automated assessments (19.2%) utilized motion tracking and AI-assisted feedback.
• Otolaryngology studies (28) focused on mastoidectomy, cochlear implantation, or temporal bone dissection; 13 studies used virtual reality platforms.

Conclusions:

• Microscopic surgery evaluation remains predominantly manual and subjective, with low adoption of automated tools.
• Instrument tracking and AI feedback show promise but require further development.
• A standardized framework for automated mastoidectomy assessment is needed for future development.