Disparity in Frontal Lobe Connectivity on a Complex Bimanual Motor Task Aids in Classification of Operator Skill Level
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1201
- Type
- D - Journal article
- DOI
-
10.1089/brain.2015.0350
- Title of journal
- Brain Connectivity
- Article number
- -
- First page
- 375
- Volume
- 6
- Issue
- 5
- ISSN
- 2158-0014
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
A - Artificial Intelligence (AI)
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This ground-breaking work within the NHS-Surgical-Innovation-Centre recognised specialised competence from functional brain connectivity and machine learning as a complex bi-manual task. Significance: published in Brain-Connectivity, a top journal, this neuroergonomics work significantly impacted the area of computer-aided surgery e.g. cites from international leaders inc: Dias(Harvard, USA, Acad. Med), Nemani(RPI, USA, Science Advances) and Kim(Samsung Medical, Korea, Mol Cells.); for the first time proposing objective measures of surgical skill. Rigorous evaluation techniques were used to assess model generalisation. Subject groups were sampled using gold a standard(FLS score) surgical skill assessment. Work continues as it has tremendous importance for surgical training (Kiani,2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
