Automation of surgical skill assessment using a three-stage machine learning algorithm

Joël Lukas Lavanchy, Joël Zindel, Kadir Kirtac, Isabell Twick, Enes Hosgor, Daniel Candinas, Guido Beldi

Scientific Reports 2021 article cited by 148

Source Open-access PDF More in Medicine & Health

importance 4/10 partially solved Surgical Simulation and Training surgical training stage: research

Problem

Manual assessment of surgeons' skills is time-consuming, subjective and prone to inconsistent interpretation.

Why it matters

Surgical skill levels are linked to clinical outcomes and adverse events.

What the AI did

A three-stage pipeline used a CNN to detect and localize laparoscopic instruments, extracted motion features over time, and trained regression to predict skill level.

Results

87 ± 0.2% accuracy distinguishing good versus poor surgical skill

Impact

Automated binary skill assessment is demonstrated, though degree of skill cannot yet be reliably quantified.

Record

Venue	Scientific Reports
Published	2021
Work type	article
Authors	Joël Lukas Lavanchy, Joël Zindel, Kadir Kirtac, Isabell Twick, Enes Hosgor, Daniel Candinas, Guido Beldi
Citations	148 (OpenAlex, at import time)
References	36
Model or method	Convolutional neural network plus linear regression (three-stage)
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-021-84295-6
Open access	gold
Keywords	surgical skill assessment, machine learning, CNN, laparoscopic cholecystectomy, motion features

Importance rating

4 / 10

real but narrower advance

Automating surgical skill assessment addresses subjective, time-consuming manual review linked to clinical outcomes, but abstract only demonstrates binary good/poor classification on one procedure; no scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Medicine & Health

#	Paper	Year	Citations	Importance
1	Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction Yeong Chan Lee, Jiho Cha, Injeong Shim, Woong‐Yang Park, Se Woong Kang...	2023	107	10
2	Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman, Amith Abdullah Khandakar, Muhammad Abdul Kadir, Khand...	2020	651	10
3	A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, Sa...	2022	116	10
4	Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Mu...	2022	89	10
5	Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, Arup Acharjee...	2021	76	10