P.140 Development and predictive validation of an intelligent surgical bimanual skills continuous assessment system. (June 2022)
- Record Type:
- Journal Article
- Title:
- P.140 Development and predictive validation of an intelligent surgical bimanual skills continuous assessment system. (June 2022)
- Main Title:
- P.140 Development and predictive validation of an intelligent surgical bimanual skills continuous assessment system
- Authors:
- Yilmaz, R
Winkler-Schwartz, A
Mirchi, N
Reich, A
Ledwos, N
Del Maestro, R - Abstract:
- Abstract : Background: Surgeons' bimanual dexterity may correlate with the surgical outcome. Continuous assessment of psychomotor performance enables action-oriented feedback and error avoidance guidance. We outline an artificial intelligence (AI) application to continuously assess surgical bimanual skills and its predictive validation on surgical trainee performance throughout a neurosurgery residency program. Methods: Participants (n=50, 14 experts/neurosurgeons, 14 senior residents, 10 junior residents, 12 novices/medical students) performed two simulated subpial tumour resections a total of 300 times. A deep neural network was developed using expert/neurosurgeon and novice/medical student data to score bimanual performance at 0.2-second intervals between a score of 1.00 and -1.00. An average score was calculated for each task. Results: The average performance score differentiated among four expertise levels, p<.001. Neurosurgeons scored significantly higher than senior residents (p=.045) and junior residents scored significantly higher than medical students (p=.04). The intelligent system also differentiated between senior and junior trainee levels (p=.004). The performance score linearly correlated with resident year of neurosurgical training (adjusted R2=27.7%). Conclusions: The AI-powered intelligent system outlined is the first expert surgeon-data-based technical skills continuous assessment system, with predictive validity throughout a neurosurgical residencyAbstract : Background: Surgeons' bimanual dexterity may correlate with the surgical outcome. Continuous assessment of psychomotor performance enables action-oriented feedback and error avoidance guidance. We outline an artificial intelligence (AI) application to continuously assess surgical bimanual skills and its predictive validation on surgical trainee performance throughout a neurosurgery residency program. Methods: Participants (n=50, 14 experts/neurosurgeons, 14 senior residents, 10 junior residents, 12 novices/medical students) performed two simulated subpial tumour resections a total of 300 times. A deep neural network was developed using expert/neurosurgeon and novice/medical student data to score bimanual performance at 0.2-second intervals between a score of 1.00 and -1.00. An average score was calculated for each task. Results: The average performance score differentiated among four expertise levels, p<.001. Neurosurgeons scored significantly higher than senior residents (p=.045) and junior residents scored significantly higher than medical students (p=.04). The intelligent system also differentiated between senior and junior trainee levels (p=.004). The performance score linearly correlated with resident year of neurosurgical training (adjusted R2=27.7%). Conclusions: The AI-powered intelligent system outlined is the first expert surgeon-data-based technical skills continuous assessment system, with predictive validity throughout a neurosurgical residency program. Intelligent systems may aid in the competency-based approach in surgery by accurately assessing trainee skills. … (more)
- Is Part Of:
- Canadian journal of neurological sciences. Volume 49(2022)Supplement 1
- Journal:
- Canadian journal of neurological sciences
- Issue:
- Volume 49(2022)Supplement 1
- Issue Display:
- Volume 49, Issue 1 (2022)
- Year:
- 2022
- Volume:
- 49
- Issue:
- 1
- Issue Sort Value:
- 2022-0049-0001-0000
- Page Start:
- S44
- Page End:
- S44
- Publication Date:
- 2022-06
- Subjects:
- Neurology -- Periodicals
Nervous system -- Surgery -- Periodicals
Electronic journals
616.8 - Journal URLs:
- http://journals.cambridge.org/action/displayJournal?jid=CJN ↗
http://www.cjns.org/home.html ↗
http://cjns.metapress.com/link.asp?id=300307 ↗
http://cjns.metapress.com/openurl.asp?genre=journal&issn=0317-1671 ↗ - DOI:
- 10.1017/cjn.2022.224 ↗
- Languages:
- English
- ISSNs:
- 0317-1671
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library STI - ELD Digital Store
- Ingest File:
- 22358.xml