Academic Journal
Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment.
| Title: | Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment. |
|---|---|
| Authors: | Shayan AM, Hitchcock DB, Singh SP, Gao J, Groff RE, Singapogu RB |
| Source: | IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2025 Apr; Vol. 29 (4), pp. 2981-2992. Date of Electronic Publication: 2025 Apr 04. |
| Publication Type: | Journal Article; Research Support, N.I.H., Extramural |
| Language: | English |
| Journal Info: | Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101604520 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2168-2208 (Electronic) Linking ISSN: 21682194 NLM ISO Abbreviation: IEEE J Biomed Health Inform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013- |
| MeSH Terms: | Suture Techniques*/education , Hand*/physiology , Clinical Competence*, Biomechanical Phenomena/physiology ; Humans ; Cluster Analysis ; Male ; Female ; Adult |
| Abstract: | This study explores the application of functional data analysis (FDA) to hand roll velocity during radial suturing on the SutureCoach bench simulator for evaluating open suturing performance. By treating temporal sensor data as mathematical functions, FDA provides a holistic view of the dynamic changes in hand roll, offering comprehensive assessments that are easily interpretable and clinically relevant. Cluster analysis was performed on hand roll profiles from 96 subjects, categorized into advanced surgeons, trainee surgeons, and novices. Functional k-means, using dynamic time-warping to align curves, were used to partition the data into two preset numbers of clusters (3 and 6). Both clustering models (3-cluster and 6-cluster) effectively clustered performance into groups with distinct characteristics and levels of skill (evident from visual inspection of cluster centroids). The relationship between cluster membership and suturing skills was corroborated using proxy measures of skill: expert global rating scale ratings, clinical status and expertise, and simulator-derived metrics. The findings of this study offer valuable insight into essential components of suturing skill and can improve the autonomy and efficiency of simulation-based suturing training. The clinical relevance of our results is immediately pertinent to the field of surgical skill assessment, where FDA-based methods could potentially be employed for objective feedback and training. |
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| Grant Information: | R01 HL146843 United States HL NHLBI NIH HHS |
| Entry Date(s): | Date Created: 20241111 Date Completed: 20250425 Latest Revision: 20260405 |
| Update Code: | 20260522 |
| PubMed Central ID: | PMC12083055 |
| DOI: | 10.1109/JBHI.2024.3496122 |
| PMID: | 39527415 |
| Database: | MEDLINE |
| ISSN: | 2168-2208 |
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| DOI: | 10.1109/JBHI.2024.3496122 |