scholarly journals Deep neural networks for the assessment of surgical skills: A systematic review

2021 ◽  
pp. 154851292110345
Author(s):  
Erim Yanik ◽  
Xavier Intes ◽  
Uwe Kruger ◽  
Pingkun Yan ◽  
David Diller ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Surgical Skills ◽  
Video Data ◽  
Skill Assessment ◽  
Assessment Process ◽  
Surgical Skill ◽  
Surgical Trainee ◽  
Surgical Skill Assessment

Surgical training in medical school residency programs has followed the apprenticeship model. The learning and assessment process is inherently subjective and time-consuming. Thus, there is a need for objective methods to assess surgical skills. Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency. There is considerable recent interest in deep neural networks (DNNs) due to the availability of powerful algorithms, multiple datasets, some of which are publicly available, as well as efficient computational hardware to train and host them. We have reviewed 530 papers, of which we selected 25 for this systematic review. Based on this review, we concluded that DNNs are potent tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets representing the surgical trainee and expert demographics and multimodal data beyond kinematics and videos.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joël L. Lavanchy ◽  
Joel Zindel ◽  
Kadir Kirtac ◽  
Isabell Twick ◽  
Enes Hosgor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Surgical Skills ◽  
Adverse Outcomes ◽  
Machine Learning Algorithms ◽  
Surgical Instruments ◽  
Skill Assessment ◽  
Surgical Skill ◽  
Surgical Skill Assessment ◽  
Motion Features

AbstractSurgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.

scholarly journals Surgical skill assessment using machine learning algorithms

2021 ◽  
Vol 108 (Supplement_4) ◽  
Author(s):  
J L Lavanchy ◽  
J Zindel ◽  
K Kirtac ◽  
I Twick ◽  
E Hosgor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Movement Patterns ◽  
Surgical Skills ◽  
Machine Learning Algorithms ◽  
Skill Assessment ◽  
Surgical Skill ◽  
Surgical Skill Assessment

Abstract Objective Surgical skill is correlated with clinical outcomes. Therefore, the assessment of surgical skill is of major importance to improve clinical outcomes and increase patient safety. However, surgical skill assessment often lacks objectivity and reproducibility. Furthermore, it is time-consuming and expensive. Therefore, we developed an automated surgical skill assessment using machine learning algorithms. Methods Surgical skills were assessed in videos of laparoscopic cholecystectomy using a three-step machine learning algorithm. First, a three-dimensional convolutional neural network was trained to localize and classify the instruments within the videos. Second, movement patterns of the instruments were recorded over time and extracted. Third, the movement patterns were correlated with human surgical skill ratings using a linear regression model to predict surgical skill ratings automatically. Machine ratings were compared against human ratings of four board certified surgeons using a score ranging from 1 (poor skills) to 5 (excellent skills). Results Human raters and machine learning algorithms assessed surgical skills in 242 videos. Inter-rater reliability for human raters was excellent (79%, 95%CI 72-85%). Instrument detection showed an average precision of 78% and average recall of 82%. Machine learning algorithms showed an 87% accuracy in predicting good or poor surgical skills, when compared to human raters. Conclusion Machine learning algorithms can be trained to distinguish good and poor surgical skills with high accuracy. This work was published in Sci Rep 11, 5197 (2021). https://doi.org/10.1038/s41598-021-84295-6

scholarly journals Objective surgical skill assessment: the diagonal operating matrix☆

2007 ◽  
Vol 6 (2) ◽  
pp. 188-191 ◽  
Author(s):  
Sharif Al-Ruzzeh ◽  
Shishir Karthik ◽  
David O'Regan
Keyword(s):  
Skill Assessment ◽  
Surgical Skill ◽  
Surgical Skill Assessment

scholarly journals Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 612 ◽  
Author(s):  
Eldar Šabanovič ◽  
Vidas Žuraulis ◽  
Olegas Prentkovskis ◽  
Viktor Skrickij
Keyword(s):  
Neural Networks ◽  
Friction Coefficient ◽  
Deep Neural Networks ◽  
Image Sensor ◽  
Video Data ◽  
Road Surface ◽  
Driver Assistance Systems ◽  
Surface Type ◽  
Braking Performance ◽  
The Road

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

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