Utilidad de un sistema de seguimiento óptico de instrumental en cirugía laparoscópica para evaluación de destrezas motoras

En este trabajo se estudia la utilidad de un sistema de evaluación de destrezas quirúrgicas basado en el análisis de los movimientos del instrumental laparoscópico. Método: El sistema consta de un simulador físico laparoscópico y un sistema de seguimiento y evaluación de habilidades técnicas quirúrgicas. En el estudio han participado 6 cirujanos con experiencia intermedia (entre 1 y 50 intervenciones laparoscópicas) y 5 cirujanos expertos (más de 50 intervenciones laparoscópicas), todos ellos con la mano derecha como dominante. Cada sujeto realizó 3 repeticiones de una tarea de corte con la mano derecha en tejido sintético, una disección de la serosa gástrica y una sutura en la disección realizada. Para cada ejercicio se analizaron los parámetros de tiempo, distancia recorrida, velocidad, aceleración y suavidad de movimientos para los instrumentos de ambas manos. Resultados: En la tarea de corte, los cirujanos expertos muestran menor aceleración (p = 0,014) y mayor suavidad en los movimientos (p = 0,023) en el uso de la tijera. Respecto a la actividad de disección, los cirujanos expertos requieren menos tiempo (p = 0,006) y recorren menos distancia con ambos instrumentos (p = 0,006 para disector y p = 0,01 para tijera). En la tarea de sutura, los cirujanos expertos presentan menor tiempo de ejecución que los cirujanos de nivel intermedio (p = 0,037) y recorren menos distancia con el disector (p = 0,041). Conclusiones: El sistema de evaluación se mostró útil en las tareas de corte, disección y sutura, y constituye un progreso en el desarrollo de sistemas avanzados de entrenamiento y evaluación de destrezas quirúrgicas laparoscópicas

Learning curves of basic laparoscopic psychomotor skills in SINERGIA VR simulator

Purpose: Surgical simulators are currently essential within any laparoscopic training program because they provide a low-stakes, reproducible and reliable environment to acquire basic skills. The purpose of this study is to determine the training learning curve based on different metrics corresponding to five tasks included in SINERGIA laparoscopic virtual reality simulator. Methods: Thirty medical students without surgical experience participated in the study. Five tasks of SINERGIA were included: Coordination, Navigation, Navigation and touch, Accurate grasping and Coordinated pulling. Each participant was trained in SINERGIA. This training consisted of eight sessions (R1–R8) of the five mentioned tasks and was carried out in two consecutive days with four sessions per day. A statistical analysis was made, and the results of R1, R4 and R8 were pair-wise compared with Wilcoxon signed-rank test. Significance is considered at P value <0.005. Results: In total, 84.38% of the metrics provided by SINERGIA and included in this study show significant differences when comparing R1 and R8. Metrics are mostly improved in the first session of training (75.00% when R1 and R4 are compared vs. 37.50% when R4 and R8 are compared). In tasks Coordination and Navigation and touch, all metrics are improved. On the other hand, Navigation just improves 60% of the analyzed metrics. Most learning curves show an improvement with better results in the fulfillment of the different tasks. Conclusions: Learning curves of metrics that assess the basic psychomotor laparoscopic skills acquired in SINERGIA virtual reality simulator show a faster learning rate during the first part of the training. Nevertheless, eight repetitions of the tasks are not enough to acquire all psychomotor skills that can be trained in SINERGIA. Therefore, and based on these results together with previous works, SINERGIA could be used as training tool with a properly designed training program

Systems and technologies for objective evaluation of technical skills in laparoscopic surgery

Minimally invasive surgery is a highly demanding surgical approach regarding technical requirements for the surgeon, who must be trained in order to perform a safe surgical intervention. Traditional surgical education in minimally invasive surgery is commonly based on subjective criteria to quantify and evaluate surgical abilities, which could be potentially unsafe for the patient. Authors, surgeons and associations are increasingly demanding the development of more objective assessment tools that can accredit surgeons as technically competent. This paper describes the state of the art in objective assessment methods of surgical skills. It gives an overview on assessment systems based on structured checklists and rating scales, surgical simulators, and instrument motion analysis. As a future work, an objective and automatic assessment method of surgical skills should be standardized as a means towards proficiency-based curricula for training in laparoscopic surgery and its certification

Development of a Suturing Simulation Device for Synchronous Acqusition of Data

There have been tremendous technological advancements in the field of surgery with new devices and minimally invasive techniques rapidly being developed. As a result, there is a corresponding need to train novice surgeons and residents to use these new technologies. Due to new regulations in medical education, an increasing the amount of surgical skills training is designed for outside the operation room using surgical simulators. In this work, a device called the suture platform was conceptualized for assessing and training basic suturing skills of medical students and novice surgeons. In the traditional approach of “open” surgery, which has not benefitted as much from simulation, suturing is one of the most foundational surgical maneuvers. The specific task developed on the suture platform is called radial suturing and was prescribed by expert surgeons as one of five core “open” vascular skills. In the initial phase of the platform development, a six-axis force sensor was used to obtain data on the device and the procedure was video-recorded for analysis. Pilot data was analyzed using force-based parameters (e.g. peak force) and temporal parameters with the goal of examining if experts were distinguished from novices. During analysis, it became apparent that future development of the device should focus on obtaining synchronized data from video and other sensors. In the next phase of development, a motion sensor was added to capture wrist motion of the trainee and to obtain richer information of the suturing process. The current system consists of a graphical user interface (GUI) that captures data during a radial suturing task that can be analyzed using force, motion and vision metrics to assess and inform surgical suturing skill training

임상술기 향상을 위한 딥러닝 기법 연구: 대장내시경 진단 및 로봇수술 술기 평가에 적용

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 의용생체공학전공, 2020. 8. 김희찬.This paper presents deep learning-based methods for improving performance of clinicians. Novel methods were applied to the following two clinical cases and the results were evaluated. In the first study, a deep learning-based polyp classification algorithm for improving clinical performance of endoscopist during colonoscopy diagnosis was developed. Colonoscopy is the main method for diagnosing adenomatous polyp, which can multiply into a colorectal cancer and hyperplastic polyps. The classification algorithm was developed using convolutional neural network (CNN), trained with colorectal polyp images taken by a narrow-band imaging colonoscopy. The proposed method is built around an automatic machine learning (AutoML) which searches for the optimal architecture of CNN for colorectal polyp image classification and trains the weights of the architecture. In addition, gradient-weighted class activation mapping technique was used to overlay the probabilistic basis of the prediction result on the polyp location to aid the endoscopists visually. To verify the improvement in diagnostic performance, the efficacy of endoscopists with varying proficiency levels were compared with or without the aid of the proposed polyp classification algorithm. The results confirmed that, on average, diagnostic accuracy was improved and diagnosis time was shortened in all proficiency groups significantly. In the second study, a surgical instruments tracking algorithm for robotic surgery video was developed, and a model for quantitatively evaluating the surgeons surgical skill based on the acquired motion information of the surgical instruments was proposed. The movement of surgical instruments is the main component of evaluation for surgical skill. Therefore, the focus of this study was develop an automatic surgical instruments tracking algorithm, and to overcome the limitations presented by previous methods. The instance segmentation framework was developed to solve the instrument occlusion issue, and a tracking framework composed of a tracker and a re-identification algorithm was developed to maintain the type of surgical instruments being tracked in the video. In addition, algorithms for detecting the tip position of instruments and arm-indicator were developed to acquire the movement of devices specialized for the robotic surgery video. The performance of the proposed method was evaluated by measuring the difference between the predicted tip position and the ground truth position of the instruments using root mean square error, area under the curve, and Pearsons correlation analysis. Furthermore, motion metrics were calculated from the movement of surgical instruments, and a machine learning-based robotic surgical skill evaluation model was developed based on these metrics. These models were used to evaluate clinicians, and results were similar in the developed evaluation models, the Objective Structured Assessment of Technical Skill (OSATS), and the Global Evaluative Assessment of Robotic Surgery (GEARS) evaluation methods. In this study, deep learning technology was applied to colorectal polyp images for a polyp classification, and to robotic surgery videos for surgical instruments tracking. The improvement in clinical performance with the aid of these methods were evaluated and verified.본 논문은 의료진의 임상술기 능력을 향상시키기 위하여 새로운 딥러닝 기법들을 제안하고 다음 두 가지 실례에 대해 적용하여 그 결과를 평가하였다. 첫 번째 연구에서는 대장내시경으로 광학 진단 시, 내시경 전문의의 진단 능력을 향상시키기 위하여 딥러닝 기반의 용종 분류 알고리즘을 개발하고, 내시경 전문의의 진단 능력 향상 여부를 검증하고자 하였다. 대장내시경 검사로 암종으로 증식할 수 있는 선종과 과증식성 용종을 진단하는 것은 중요하다. 본 연구에서는 협대역 영상 내시경으로 촬영한 대장 용종 영상으로 합성곱 신경망을 학습하여 분류 알고리즘을 개발하였다. 제안하는 알고리즘은 자동 기계학습 (AutoML) 방법으로, 대장 용종 영상에 최적화된 합성곱 신경망 구조를 찾고 신경망의 가중치를 학습하였다. 또한 기울기-가중치 클래스 활성화 맵핑 기법을 이용하여 개발한 합성곱 신경망 결과의 확률적 근거를 용종 위치에 시각적으로 나타나도록 함으로 내시경 전문의의 진단을 돕도록 하였다. 마지막으로, 숙련도 그룹별로 내시경 전문의가 용종 분류 알고리즘의 결과를 참고하였을 때 진단 능력이 향상되었는지 비교 실험을 진행하였고, 모든 그룹에서 유의미하게 진단 정확도가 향상되고 진단 시간이 단축되었음을 확인하였다. 두 번째 연구에서는 로봇수술 동영상에서 수술도구 위치 추적 알고리즘을 개발하고, 획득한 수술도구의 움직임 정보를 바탕으로 수술자의 숙련도를 정량적으로 평가하는 모델을 제안하였다. 수술도구의 움직임은 수술자의 로봇수술 숙련도를 평가하기 위한 주요한 정보이다. 따라서 본 연구는 딥러닝 기반의 자동 수술도구 추적 알고리즘을 개발하였으며, 다음 두가지 선행연구의 한계점을 극복하였다. 인스턴스 분할 (Instance Segmentation) 프레임웍을 개발하여 폐색 (Occlusion) 문제를 해결하였고, 추적기 (Tracker)와 재식별화 (Re-Identification) 알고리즘으로 구성된 추적 프레임웍을 개발하여 동영상에서 추적하는 수술도구의 종류가 유지되도록 하였다. 또한 로봇수술 동영상의 특수성을 고려하여 수술도구의 움직임을 획득하기위해 수술도구 끝 위치와 로봇 팔-인디케이터 (Arm-Indicator) 인식 알고리즘을 개발하였다. 제안하는 알고리즘의 성능은 예측한 수술도구 끝 위치와 정답 위치 간의 평균 제곱근 오차, 곡선 아래 면적, 피어슨 상관분석으로 평가하였다. 마지막으로, 수술도구의 움직임으로부터 움직임 지표를 계산하고 이를 바탕으로 기계학습 기반의 로봇수술 숙련도 평가 모델을 개발하였다. 개발한 평가 모델은 기존의 Objective Structured Assessment of Technical Skill (OSATS), Global Evaluative Assessment of Robotic Surgery (GEARS) 평가 방법과 유사한 성능을 보임을 확인하였다. 본 논문은 의료진의 임상술기 능력을 향상시키기 위하여 대장 용종 영상과 로봇수술 동영상에 딥러닝 기술을 적용하고 그 유효성을 확인하였으며, 향후에 제안하는 방법이 임상에서 사용되고 있는 진단 및 평가 방법의 대안이 될 것으로 기대한다.Chapter 1 General Introduction 1 1.1 Deep Learning for Medical Image Analysis 1 1.2 Deep Learning for Colonoscipic Diagnosis 2 1.3 Deep Learning for Robotic Surgical Skill Assessment 3 1.4 Thesis Objectives 5 Chapter 2 Optical Diagnosis of Colorectal Polyps using Deep Learning with Visual Explanations 7 2.1 Introduction 7 2.1.1 Background 7 2.1.2 Needs 8 2.1.3 Related Work 9 2.2 Methods 11 2.2.1 Study Design 11 2.2.2 Dataset 14 2.2.3 Preprocessing 17 2.2.4 Convolutional Neural Networks (CNN) 21 2.2.4.1 Standard CNN 21 2.2.4.2 Search for CNN Architecture 22 2.2.4.3 Searched CNN Training 23 2.2.4.4 Visual Explanation 24 2.2.5 Evaluation of CNN and Endoscopist Performances 25 2.3 Experiments and Results 27 2.3.1 CNN Performance 27 2.3.2 Results of Visual Explanation 31 2.3.3 Endoscopist with CNN Performance 33 2.4 Discussion 45 2.4.1 Research Significance 45 2.4.2 Limitations 47 2.5 Conclusion 49 Chapter 3 Surgical Skill Assessment during Robotic Surgery by Deep Learning-based Surgical Instrument Tracking 50 3.1 Introduction 50 3.1.1 Background 50 3.1.2 Needs 51 3.1.3 Related Work 52 3.2 Methods 56 3.2.1 Study Design 56 3.2.2 Dataset 59 3.2.3 Instance Segmentation Framework 63 3.2.4 Tracking Framework 66 3.2.4.1 Tracker 66 3.2.4.2 Re-identification 68 3.2.5 Surgical Instrument Tip Detection 69 3.2.6 Arm-Indicator Recognition 71 3.2.7 Surgical Skill Prediction Model 71 3.3 Experiments and Results 78 3.3.1 Performance of Instance Segmentation Framework 78 3.3.2 Performance of Tracking Framework 82 3.3.3 Evaluation of Surgical Instruments Trajectory 83 3.3.4 Evaluation of Surgical Skill Prediction Model 86 3.4 Discussion 90 3.4.1 Research Significance 90 3.4.2 Limitations 92 3.5 Conclusion 96 Chapter 4 Summary and Future Works 97 4.1 Thesis Summary 97 4.2 Limitations and Future Works 98 Bibliography 100 Abstract in Korean 116 Acknowledgement 119Docto

Evolution von Distanzmaßen für chirurgische Prozesse

Der Operationssaal ist ein hochkomplexes System mit dem Ziel patientenindividuelle Therapien zum Erfolg zu führen. Schwerpunkt dieser Arbeit ist der Arbeitsablauf des Chirurgen. Ein chirurgischer Prozess beinhaltet die durchgeführten Arbeitsschritte des Operateurs während eines Eingriffffs. Der protokollierte chirurgische Prozess ist Ausgangspunkt der Untersuchungen. Es wurde eine Methodik entwickelt, die mit statistischen und standardisierten Verfahren Unterschiede zwischen dem Ablauf verschiedener chirurgischer Prozesse messen kann. Dazu wurden die vier Distanzmaße Jaccard, Levenshtein, Adjazenz und Graphmatching auf chirurgische Prozesse angewandt. Eine Evaluation anhand von Daten einer Trainingsstudie zur Untersuchung laparoskopischer Instrumente in der minimalinvasiven Chirurgie bildet die Grundlage zur Bestimmung von Levenshteindistanz und Adjazenzdistanz als die Maße, die optimal geeignet sind Unterschiede zwischen chirurgen Prozessen zu messen. Die Retrospektivität der Distanzanalyse wird aufgehoben indem folgende Hypothese untersucht wird: Es gibt einen Zusammenhang zwischen der Distanz zur Laufzeit eines chirurgischen Eingriffs mit der Distanz nach kompletten Ablauf des Eingriffs. Die Hypothese konnte bestätigt werden. Der Zusammenhang zwischen Prozessablauf und Qualität des Prozessergebnisses wird mit folgender Hypothese untersucht: Je größer die Distanz eines chirurgischen Prozesses zum Best Practice, desto schlechter ist das Prozessergebnis. In der Chirurgie ist der Best Practice der chirurgische Prozess, der als die beste Prozedur angesehen wird, um das angestrebte Therapieziel zu erreichen. Auch diese Hypothese konnte bestätigt werden. Die Anwendung der Distanzmaße in der klinischen Praxis erfolgte beispielhaft an Eingriffffen aus der Neurochirurgie (zervikale Diskektomie) und der HNO (Neck Dissection). Insgesamt wurde mit der in dieser Arbeit dargelegten grundlegenden Methodik der Distanzmaße bei der Analyse chirurgischer Prozesse ein Grundstein für vielfältige weitere Untersuchungen gelegt