Treffer: Time and Expertise in Open Structural Rhinoplasty: A Task-Based Analysis Using Hierarchical Task Analysis and Machine Learning.

Title:
Time and Expertise in Open Structural Rhinoplasty: A Task-Based Analysis Using Hierarchical Task Analysis and Machine Learning.
Authors:
Celikoyar MM; Department of Otolaryngology, Demiroğlu Bilim University School of Medicine, Istanbul, Turkey., Topsakal O; Bellini College of Artificial Intelligence, Cybersecurity and Computing, University of South Florida, Tampa, FL., Dobratz E; Department of Otolaryngology/Head and Neck Surgery, Eastern Virginia Medical School, Norfolk, VA., Demirel D; School of Computer Science, Gallogly College of Engineering, University of Oklahoma, Norman, OK.
Source:
The Journal of craniofacial surgery [J Craniofac Surg] 2025 Nov-Dec 01; Vol. 36 (8), pp. e1413-e1420. Date of Electronic Publication: 2025 Sep 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 9010410 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-3732 (Electronic) Linking ISSN: 10492275 NLM ISO Abbreviation: J Craniofac Surg Subsets: MEDLINE
Imprint Name(s):
Publication: <2014-> : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Burlington, Ont. : B.C. Decker, c1990-
References:
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Contributed Indexing:
Keywords: Hierarchical task analysis; biomedical; machine learning; rhinoplasty; surgical education; technology assessment
Entry Date(s):
Date Created: 20250918 Date Completed: 20251215 Latest Revision: 20260107
Update Code:
20260107
DOI:
10.1097/SCS.0000000000011959
PMID:
40965438
Database:
MEDLINE

Weitere Informationen

Rhinoplasty consistsof specific surgical tasks performed in order and executed at specific times. Hierarchical task analysis (HTA) is an essential tool for developing performance metrics to help evaluate surgeries. The authors aimed to determine if there is a correlation with experience and time required for task completion. We developed an HTA for open structural rhinoplasty, then performed a survey to gather surgeons' self-reported time to complete tasks. Surgeons were grouped according to the number of rhinoplasty cases they have performed; those who performed <100 were considered "non-expert," and those who performed more than 100 cases were considered "expert." Statistical analysis was done. Machine learning (ML) was utilized as well to help evaluate the comparison of two groups. Responses from 25 surgeons were analyzed. The surgical steps that showed statistically significant differences between the two surgeon groups included the elevation of (septal) mucoperichondrial-mucoperiosteal flaps, cephalic trim, septoplasty closure, and rhinoplasty closure, with significantly shorter time required by the expert surgeons. According to ML model, rhinoplasty closure, injection, transcolumellar incisions, dorsal hump reduction, dorsal surgery-lateral osteotomies, assessment of lower lateral cartilage, and dorsal hump bone reduction were the steps where the 2 groups of surgeons had significantly different time frames. These tasks may be accepted as more prone to benefits from time and surgical volume. The number of cases observed had no significant effect, therefore, the benefits from time and surgical volume are most noted with hands-on practice and performing the procedure.
(Copyright © 2025 by Mutaz B. Habal, MD.)

The authors report no conflicts of interest.