Minimally invasive surgery (MIS) has become in many surgical specialties and procedures the gold-standard choice due to its efficiency and benefits towards patient safety. However, the introduction of laparoscopic surgery has led to the need of developing new surgical skills different to those required for open surgery, with a significant learning curve to perform a safe laparoscopic surgery. Traditional subjective assessment methods of trainees are no longer adequate for surgical training. Reduced working hours as well as demands from surgeons and associations mean that more objective assessment tools that can accredit surgeons as technically competent are required.
Evidence exists to validate motion analysis for laparoscopic technical skills assessment. Motion analysis of the laparoscopic instruments seeks to determine aspects that indicate the difference between surgeon's level of surgical dexterity. However, at the moment there is not an extended method to be used with current available training systems used in training laboratories as well as for the OR.
The purpose of this book is to present the design, development and validation of three novel motion analysis methods focused on the use of real laparoscopic instruments during laparoscopic performance. These methods are based on computer vision techniques attempting to not interfere with the surgical practice. They are introduced in an evolutionary way from methods for exclusive use in a box trainer to solutions with the potential of being used in actual OR setting.
Overall, this work corroborates the research hypothesis regarding the use of three different video-based tracking technologies for motion analysis of laparoscopic instruments, the use of instrument motion analysis for MIS technical skills assessment and the relationship between motion-related assessment metrics and quality of technical performance in laparoscopic training. These presented methods provide a tool to objectively assess MIS technical performance and a support to train novice surgeons in MIS techniques. The findings of this work encourage us to continue researching in improving these methods to be introduced as part of an actual laparoscopic training program, both inside and outside the OR.
Table of Contents
I. Introduction
I.1. Essentials of laparoscopic surgery
I.2. Psychomotor challenges
I.3. Skills to be a proficient surgeon
I.4. Evolution of surgical education
I.4.1. Traditional surgical learning
I.4.2. New surgical learning approaches
I.5. Objective assessment methods
I.5.1. Structured rating systems for objective evaluation
I.5.1.1. Rating scales
I.5.1.2. Assessment programmes
1.5.2. Instrument motion analysis
I.5.3. A good assessment tool
I.6. Problem statement and aim of this thesis
I.7. Thesis outline
II. Research hypotheses and objectives
II.1. Research hypotheses
II.1.1. First instrument motion analysis method
II.1.2. Second instrument motion analysis method
II.1.3. Third instrument motion analysis method
II.2. Objectives
II.2.1. General objective
II.2.2. Specific objectives
III. State of the art
III.1. Introduction
III.2. Surgical simulators
III.2.1. Physical simulators or box trainers
III.2.2. Virtual simulators
III.2. 3. Hybrid simulators
III.3. Objective assessment methods based on motion analysis
III.3.1. Extracorporeal methods
III.3.2. Intracorporeal methods
Chapter IV: Laparoscopic hybrid simulator with stereoscopic tracking system
IV.1. Introduction
IV.2. Material and methods
IV.2.1. System description
IV.2.2. Stereo vision system
IV.2.2.1. System calibration
Image distortion
Stereo calibration
IV.2.2.2. Stereo alignment
IV.2.2.3. Three-dimensional correspondence
IV.2.3. Laparoscopic instrument tracking
IV.2.4. Technical validation
IV.3. Results
IV.3.1. Relative position error
IV.3.2. Accumulated distance error
IV.3.3. Face validation
VI.4. Discussion
VI.4.1. Chapter conclusions
Chapter V: Optical pose tracker for assessment of MIS technical skills
V.1. Introduction
V.2. Technical validation
V.2.1. First design
V.2.1.1. System description
V.2.1.2. Evaluation tests
V.2.1.3. Results
V.2.2. Second design
V.2.2.1. System description
V.2.2.2. Evaluation tests
V.2.2.3. Results
V.3. Validation for MIS skills assessment
V.3.1. Experimental validation
V.3.1.1. Subjects
V.3.1.2. Tasks
V.3.2. MIS skills assessment
V.3.3. Statistical analysis
V.3.4. Results
V.3.4.1. Face validation
V.3.4.2. GOALS exploratory analysis
V.3.4.3. Construct validation
V.3.5.3. Concurrent validation
V.3.4.4. Correlation between suturing checklist and motion metrics
V.4. Installing the motion assessment system inside an OR setting
V.4.1. Analysis of the position of the camera system
V.4.1.1. Materials and methods
V.4.1.2. Results
V.4.2. Feasibility study for the use of the motion analysis method in a OR setting
V.4.2.1. Materials and methods
V.4.2.2. Results
V.5. Discussion
V.5.1. Chapter conclusions
Chapter VI: Video-based instrument tracking method
VI.1. Introduction
VI.2. Materials
VI.3. Laparoscopic instrument tracking method
VI.3.1. Materials and methods
VI.3.1.1. Compilation of the training images
VI.3.1.2. Create training sample
VI.3.1.3. Train the classifier
VI.3.1.4. Technical validation
Test 1: Accuracy under different working conditions
Test 2: Accuracy in different training settings
VI.3.2. Results
VI.3.2.1. Test 1: Accuracy under different working conditions
VI.3.2.2. Test 2: Accuracy in different training settings
VI.4. Method to insert multimedia support content
VI.4.1. Technical validation
VI.4.2. Results
VI.5. Discussion
VI.5.1. Chapter conclusions
Chapter VII: General discussion
Chapter VIII: Conclusions and future works
VIII.1. Research hypotheses’ verification
VIII.1.1. General hypothesis
VIII.1.2. First instrument motion analysis method
VIII.1.3. Second instrument motion analysis method
VIII.1.4. Third instrument motion analysis method
VIII.2. Main contributions
VIII.4. Future works
VIII.4.1. Concerning the first method based on stereoscopic techniques
VIII.4.2. Concerning the second method based on an optical pose tracker
VIII.4.3. Concerning the third method based on a classifier
Research Goals and Thematic Focus
The primary research objective of this thesis is to design, develop, and validate three innovative motion analysis methods for real laparoscopic instruments. The core research question addresses how computer vision techniques can be utilized to provide objective, non-intrusive assessment of surgical dexterity in training environments ranging from box trainers to actual operating rooms, thereby overcoming the limitations of subjective traditional evaluation methods.
- Development of a cost-effective stereoscopic tracking system for box trainers.
- Implementation of a third-generation optical pose tracker for MIS skills assessment.
- Creation of a robust, video-based tracking method using endoscopic image analysis.
- Establishment of motion-related metrics to correlate with surgical proficiency scores (GOALS).
- Feasibility analysis of integrating motion tracking systems into clinical OR environments.
Excerpt from the Book
I.5.2. Instrument motion analysis
In order to investigate surgeons’ laparoscopic technical abilities during surgical interventions, it seems reasonable to observe carefully the movements of the laparoscopic instruments. Measuring competence merely by setting time targets for certain procedures could be inaccurate and insufficient. A fast surgeon is not necessarily a good surgeon. Counting the number of procedures performed has also been used as a tool to accredit surgeons, but it does not have to tell about how well the surgeon operates (Darzi et al., 1999).
Traditional psychology literature suggests that when subjects learn a complex motor task, as they become more competent at that task, they also become more efficient in the movements they use to complete it. At the beginning, they may be inaccurate and with uncoordinated movements; but as they learn and become more competent at the task, they achieve more efficient, coordinate, and smooth movements (Rosenbaum, 2009). The three-stage theory of motor skills acquisition has been globally accepted (Sándor et al., 2010). During the first stage of training, the learner has to understand the task and create a plan to perform it, but this stage also allows the learner to make errors (cognitive stage). With improved practice, the learner reaches the integrative stage, when the task is performed more fluently. Finally, in the autonomous stage, the learner no longer needs to think about how to execute the task and can therefore concentrate exclusively on the actual task (Reznik, 1993).
Instrument motion analysis has been used successfully in several studies to address the assessment of surgeon’s psychomotor skills (Oropesa et al., 2013a; Climent and Hexsel, 2012; Pagador et al., 2012; Chmarra et al., 2010; Datta et al., 2007). Motion analysis can typically be performed both in VR simulators and in real-life training (box trainers/OR). Systems for the latter case are cheaper assessment alternatives, enabling natural feedback. Assessment methods based on instrument motion analysis can be used in a wide range of surgical and training settings from box trainers to the OR.
Summary of Chapters
I. Introduction: Discusses the transition from open to minimally invasive surgery and the urgent need for objective assessment tools to replace traditional subjective evaluation methods.
II. Research hypotheses and objectives: Defines the core hypotheses regarding real-time motion analysis and the specific research objectives for developing three tracking methods.
III. State of the art: Provides a comprehensive overview of existing surgical simulators and the diverse technological approaches for tracking laparoscopic instruments for skill assessment.
Chapter IV: Laparoscopic hybrid simulator with stereoscopic tracking system: Presents the development of a cost-effective hybrid simulator using stereoscopic cameras installed inside a box trainer.
Chapter V: Optical pose tracker for assessment of MIS technical skills: Details the integration of a third-generation optical pose tracker to assess technical skills through validated motion metrics.
Chapter VI: Video-based instrument tracking method: Introduces an innovative method for instrument tracking using only endoscopic video analysis and an assistance system for training.
Chapter VII: General discussion: Compares the three developed tracking methods, highlighting their respective advantages, limitations, and potential for integration into surgical curricula.
Chapter VIII: Conclusions and future works: Reviews the verification of research hypotheses and outlines future directions for improving tracking robustness and clinical integration.
Keywords
Laparoscopic surgery, motion analysis, instrument tracking, objective assessment, surgical training, stereoscopic vision, optical pose tracker, video-based tracking, psychomotor skills, surgical education, MIS, box trainer, cascade classifier, performance metrics.
Frequently Asked Questions
What is the core focus of this research?
This work focuses on designing, developing, and validating three novel, automated motion analysis methods for laparoscopic instruments to provide objective assessment of technical skills in surgical training.
Which specific surgical training platforms are investigated?
The research explores the application of these tracking methods within box trainers and assesses the feasibility of their implementation in clinical environments like the operating room.
What is the primary objective of these tracking systems?
The main goal is to objectively distinguish surgical skill levels (novice, intermediate, expert) and provide actionable feedback for trainees, moving away from subjective, mentor-based evaluations.
What scientific methods are utilized for tracking?
The research employs stereoscopic vision techniques, a third-generation optical pose tracker, and video-based tracking using cascade classifiers as the primary technological solutions.
What do the validation chapters cover?
They provide both technical validation of tracking accuracy and experimental validation regarding construct and concurrent validity using standard assessment metrics like GOALS.
What are the fundamental motion metrics used?
Key metrics include time, path length, speed, acceleration, and motion smoothness, alongside newer metrics like economy of area and volume.
How is the Kalman filter used in this research?
A smoothing Kalman filter is implemented to process raw data from the trackers, effectively reducing signal noise to ensure the reliability of the derived surgical performance metrics.
Can these systems be used in a real operating room?
Yes, Chapter V and VI demonstrate the feasibility of installing these motion analysis systems in an experimental operating room setting for potential future clinical and training applications.
How does the video-based tracker differ from the others?
Unlike the stereoscopic and optical pose trackers, the video-based method uses existing endoscopic video as the sole source of information, eliminating the need for external markers or specialized hardware inside the workspace.
- Quote paper
- Juan A. Sánchez-Margallo (Author), Francisco M. Sánchez-Margallo (Author), José Moreno del Pozo (Author), Enrique J. Gómez Aguilera (Author), 2014, Methods for laparoscopic instrument tracking and motion analysis for objective assessment of surgical technical skills, Munich, GRIN Verlag, https://www.grin.com/document/286604
