Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.Comment: 6 pages, 10 figures,2018 International Conference on Pattern Recognitio

Optimizing endoscopic strategies for colorectal cancer screening : improving colonoscopy effectiveness by optical, non-optical, and computer-based models

Introduction: Le cancer colorectal demeure un grave problème de santé publique au Canada. Les programmes de dépistage pourraient réduire l'incidence du cancer colorectal et la mortalité qui lui est associée. Une coloscopie de haute qualité est considérée comme un moyen rentable de prévenir le cancer en identifiant et en éliminant les lésions précurseurs du cancer. Bien que la coloscopie puisse servir de mesure préventive contre le cancer, la procédure peut imposer un fardeau supplémentaire à la santé publique par l'enlèvement et l'évaluation histologique de polypes colorectaux diminutifs et insignifiants, qui présentent un risque minime d'histologie avancée ou de cancer. La technologie de l'amélioration de l'image permettrait aux médecins de réséquer et de rejeter les polypes diminutifs ou de diagnostiquer et de laisser les polypes rectosigmoïdiens diminutifs sans examen histopathologique. Malgré la disponibilité de systèmes informatiques de caractérisation des polypes, la pratique du diagnostic optique reste limitée en raison de la crainte d'un mauvais diagnostic de cancer, d'une mauvaise surveillance des patients et des problèmes médico-légaux correspondants. Il est donc indispensable d'élaborer des stratégies alternatives de résection et d'élimination non optiques pour améliorer la précision et la sécurité du diagnostic optique et l'adapter à la pratique clinique. Ces stratégies doivent répondre à des critères cliniques simples et ne nécessitent pas de formation supplémentaire ni de dispositifs d'amélioration de l'image. De plus, la pratique sûre du diagnostic optique, la prise de décision appropriée concernant la technique de polypectomie ou l'intervalle de surveillance dépendent de l'estimation précise de la taille des polypes. La variabilité inter-endoscopistes dans la mesure de la taille des polypes exige le développement de méthodes fiables et validées pour augmenter la précision de la mesure de la taille. Une balance virtuelle intégrée à un endoscope haute définition est actuellement disponible pour le calcul automatique de la taille des polypes, mais sa faisabilité clinique n'a pas encore été établie. En dehors des points susmentionnés, une coloscopie de haute qualité nécessite l'examen complet de la muqueuse colique, ainsi que la visualisation de la valve iléocæcale et de l'orifice appendiculaire. À ce jour, aucune solution informatique n'a été capable d'assister les endoscopistes pendant les coloscopies en temps réel en détectant et en différenciant les points de repère cæcaux de façon automatique. Objectifs: Les objectifs de cette thèse sont : 1) d'étudier l'effet de la limitation du diagnostic optique aux polypes de 1 à 3 mm sur la sécurité du diagnostic optique pour le traitement des polypes diminutifs et l'acceptation par les endoscopistes de son utilisation dans les pratiques en temps réel tout en préservant ses potentiels de temps et de rentabilité ; 2) élaborer et examiner des stratégies non optiques de résection et d'élimination qui peuvent remplacer le diagnostic optique tout en offrant les mêmes possibilités d'économie de temps et d'argent ; 3) examiner la précision relative d'un endoscope à échelle virtuelle pour mesurer la taille des polypes ; 4) former, valider et tester un modèle d'intelligence artificielle qui peut prédire la complétude d'une procédure de coloscopie en identifiant les points de repère anatomiques du cæcum (c'est-à-dire la valve iléo-cæcale et l'orifice appendiculaire) et en les différenciant les uns des autres, des polypes et de la muqueuse normale. Méthodes: Pour atteindre le premier objectif de cette thèse, une analyse post-hoc de trois études prospectives a été réalisée pour évaluer la proportion de patients chez lesquels des adénomes avancés ont été découverts et le diagnostic optique a entraîné une surveillance retardée dans trois groupes de taille de polypes : 1–3, 1–5, et 1–10 mm. Pour atteindre le second objectif de cette thèse, deux stratégies non optiques ont été développées et testées dans deux études prospectives: une stratégie de résection et d'élimination basée sur la localisation qui utilise la localisation anatomique des polypes pour classer les polypes du côlon en non-néoplasiques ou néoplasiques à faible risque et une stratégie de résection et d'élimination basée sur les polypes qui attribue des intervalles de surveillance en fonction du nombre et de la taille des polypes. Dans les trois études, la concordance de l'attribution d'intervalles de surveillance basée sur un diagnostic optique à haute confiance ou sur des stratégies non optiques avec les recommandations basées sur la pathologie, ainsi que la proportion d'examens pathologiques évités et la proportion de communications immédiates d'intervalles de surveillance, ont été évaluées. Le troisième objectif de cette thèse a été abordé par le biais d'une étude de faisabilité pilote prospective qui a utilisé la mesure de spécimens de polypes immédiatement après leur prélèvement, suite à une polypectomie par un pied à coulisse Vernier comme référence pour comparer la précision relative des mesures de la taille des polypes entre les endoscopistes et un endoscope à échelle virtuelle. Enfin, le quatrième objectif de cette thèse a été évalué par l'enregistrement et l'annotation prospective de vidéos de coloscopie. Des images non modifiées de polype, de valve iléo-caecale, d'orifice appendiculaire et de muqueuse normale ont été extraites et utilisées pour développer et tester un modèle de réseau neuronal convolutionnel profond pour classer les images pour les points de repère qu'elles contiennent. Résultats: La réduction du seuil du diagnostic optique favoriserait la sécurité du diagnostic optique en diminuant de manière significative le risque d'écarter un polype avec une histologie avancée ou la mauvaise surveillance d'un patient avec de tels polypes. En outre, les stratégies non optiques de résection et d'élimination pourraient dépasser le critère de référence d'au moins 90% de concordance dans l'attribution des intervalles de surveillance post-polypectomie par rapport aux décisions basées sur l'évaluation pathologique. De plus, il a été démontré que l'endoscope à échelle virtuelle est plus précis que l'estimation visuelle de la taille des polypes en temps réel. Enfin, un modèle d'apprentissage profond s'est révélé très efficace pour détecter les repères cæcaux, les polypes et la muqueuse normale, à la fois individuellement et en combinaison. Discussion: La prédiction histologique optique des polypes de 1 à 3 mm est une approche efficace pour améliorer la sécurité et la faisabilité de la stratégie de résection et d'écartement dans la pratique. Les approches non optiques de résection et d'élimination offrent également des alternatives viables au diagnostic optique lorsque les endoscopistes ne sont pas en mesure de répondre aux conditions de mise en œuvre systématique du diagnostic optique, ou lorsque la technologie d'amélioration de l'image n'est pas accessible. Les stratégies de résection et de rejet, qu'elles soient optiques ou non, pourraient réduire les coûts supplémentaires liés aux examens histopathologiques et faciliter la communication du prochain intervalle de surveillance le même jour que la coloscopie de référence. Un endoscope virtuel à échelle réduite faciliterait l'utilisation du diagnostic optique pour la détection des polypes diminutifs et permet une prise de décision appropriée pendant et après la coloscopie. Enfin, le modèle d'apprentissage profond peut être utile pour promouvoir et contrôler la qualité des coloscopies par la prédiction d'une coloscopie complète. Cette technologie peut être intégrée dans le cadre d'une plateforme de vérification et de génération de rapports qui élimine le besoin d'intervention humaine. Conclusion: Les résultats présentés dans cette thèse contribueront à l'état actuel des connaissances dans la pratique de la coloscopie concernant les stratégies pour améliorer l'efficacité de la coloscopie dans la prévention du cancer colorectal. Cette étude fournira des indications précieuses pour les futurs chercheurs intéressés par le développement de méthodes efficaces de traitement des polypes colorectaux diminutifs. Le diagnostic optique nécessite une formation complémentaire et une mise en œuvre à l'aide de modules de caractérisation informatisés. En outre, malgré la lenteur de l'adoption des solutions informatiques dans la pratique clinique, la coloscopie assistée par l'IA ouvrira la voie à la détection automatique, à la caractérisation et à la rédaction semi-automatique des rapports de procédure.Introduction: Colorectal cancer remains a critical public health concern in Canada. Screening programs could reduce the incidence of colorectal cancer and its associated mortality. A high-quality colonoscopy is appraised to be a cost-effective means of cancer prevention through identifying and removing cancer precursor lesions. Although colonoscopy can serve as a preventative measure against cancer, the procedure can impose an additional burden on the public health by removing and histologically evaluating insignificant diminutive colorectal polyps, which pose a minimal risk of advanced histology or cancer. The image-enhance technology would enable physicians to resect and discard diminutive polyps or diagnose and leave diminutive rectosigmoid polyps without histopathology examination. Despite the availability of computer-based polyp characterization systems, the practice of optical diagnosis remains limited due to the fear of cancer misdiagnosis, patient mismanagement, and the related medicolegal issues. Thus, alternative non-optical resection and discard strategies are imperative for improving the accuracy and safety of optical diagnosis for adaptation to clinical practice. These strategies should follow simple clinical criteria and do not require additional education or image enhanced devices. Furthermore, the safe practice of optical diagnosis, adequate decision-making regarding polypectomy technique, or surveillance interval depends on accurate polyp size estimation. The inter-endoscopist variability in polyp sizing necessitates the development of reliable and validated methods to enhance the accuracy of size measurement. A virtual scale integrated into a high-definition endoscope is currently available for automated polyp sizing, but its clinical feasibility has not yet been demonstrated. In addition to the points mentioned above, a high-quality colonoscopy requires the complete examination of the entire colonic mucosa, as well as the visualization of the ileocecal valve and appendiceal orifice. To date, no computer-based solution has been able to support endoscopists during live colonoscopies by automatically detecting and differentiating cecal landmarks. Aims: The aims of this thesis are: 1) to investigate the effect of limiting optical diagnosis to polyps 1–3mm on the safety of optical diagnosis for the management of diminutive polyps and the acceptance of endoscopists for its use in real-time practices while preserving its time- and cost-effectiveness potentials; 2) to develop and examine non-optical resect and discard strategies that can replace optical diagnosis while offering the same time- and cost-saving potentials; 3) to examine the relative accuracy of a virtual scale endoscope for measuring polyp size; 4) to train, validate, and test an artificial intelligence-empower model that can predict the completeness of a colonoscopy procedure by identifying cecal anatomical landmarks (i.e., ileocecal valve and appendiceal orifice) and differentiating them from one another, polyps, and normal mucosa. Methods: To achieve the first aim of this thesis, a post-hoc analysis of three prospective studies was performed to evaluate the proportion of patients in which advanced adenomas were found and optical diagnosis resulted in delayed surveillance in three polyp size groups: 1‒3, 1‒5, and 1‒10 mm. To achieve the second aim of this thesis, two non-optical strategies were developed and tested in two prospective studies: a location-based resect and discard strategy that uses anatomical polyp location to classify colon polyps into non-neoplastic or low-risk neoplastic and a polyp-based resect and discard strategy that assigns surveillance intervals based on polyp number and size. In all three studies, the agreement of assigning surveillance intervals based on high-confidence optical diagnosis or non-optical strategies with pathology-based recommendations, as well as the proportion of avoided pathology examinations and the proportion of immediate surveillance interval communications, was evaluated. The third aim of this thesis was addressed through a prospective pilot feasibility study that used the measurement of polyp specimens immediately after retrieving, following a polypectomy by a Vernier caliper as a reference to compare the relative accuracy of polyp size measurements between endoscopists and a virtual scale endoscope. Finally, the fourth aim of this thesis was assessed through prospective recording and annotation of colonoscopy videos. Unaltered images of polyp, ileocecal valve, appendiceal orifice and normal mucosa were extracted and used to develop and test a deep convolutional neural network model for classifying images for the containing landmarks. Results: Reducing the threshold of optical diagnosis would promote the safety of optical diagnosis by significantly decreasing the risk of discarding a polyp with advanced histology or the mismanagement of a patient with such polyps. Additionally, the non-optical resect and discard strategies could surpass the benchmark of at least 90% agreement in the assignment of post-polypectomy surveillance intervals compared with decisions based on pathologic assessment. Moreover, the virtual scale endoscope was demonstrated to be more accurate than visual estimation of polyp size in real-time. Finally, a deep learning model proved to be highly effective in detecting cecal landmarks, polyps, and normal mucosa, both individually and in combination. Discussion: Optical histology prediction of polyps 1‒3 mm in size is an effective approach to enhance the safety and feasibility of resect and discard strategy in practice. Non-optical resect and discard approaches also offer feasible alternatives to optical diagnosis when endoscopists are unable to meet the conditions for routine implementation of optical diagnosis, or when image-enhanced technology is not accessible. Both optical and non-optical resect and discard strategies could reduce additional costs related to histopathology examinations and facilitate the communication of the next surveillance interval in the same day as the index colonoscopy. A virtual scale endoscope would facilitate the use of optical diagnosis for the detection of diminutive polyps and allows for appropriate decision-making during and after colonoscopy. Additionally, the deep learning model may be useful in promoting and monitoring the quality of colonoscopies through the prediction of a complete colonoscopy. This technology may be incorporated as part of a platform for auditing and report generation that eliminates the need for human intervention. Conclusion: The results presented in this thesis will contribute to the current state of knowledge in colonoscopy practice regarding strategies for improving the efficacy of colonoscopy in the prevention of colorectal cancer. This study will provide valuable insights for future researchers interested in developing effective methods for treating diminutive colorectal polyps. Optical diagnosis requires further training and implementation using computer-based characterization modules. Furthermore, despite the slow adoption of computer-based solutions in clinical practice, AI-empowered colonoscopy will eventually pave the way for automatic detection, characterization, and semi-automated completion of procedure reports in the future

Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis

Background. The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. Methods. Semi-structured interviews and an online survey were used. Results. Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation.This work is part of the PICCOLO project, which has received funding from the European Union’s Horizon 2020 research and innovation Programme under grant agreement No. 732111. GR18199, funded by “Consejería de Economía, Ciencia y Agenda Digital, Junta de Extremadura” and co-funded by European Union (ERDF “A way to make Europe”). The funding bodies did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

Искусственный интеллект при колоректальном раке: обзор

The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.Цель исследования - оценка возможностей использования искусственного интеллекта (ИИ) в диагностике, лечении и прогнозировании колоректального рака (КРР), а также обсуждение потенциала ИИ в лечении КРР. Материал и методы. Проведен поиск научных публикаций в поисковых системах Web of Science, Scopus, PubMed, Medline и eLIBRARY. Было просмотрено более 100 источников по применению ИИ для диагностики, лечения и прогнозирования КРР. В обзор включены данные из 83 статей. Результаты. Проведен анализ литературы, посвященной применению искусственного интеллекта в медицине, особое внимание уделено его использованию при колоректальном раке. Обсуждаются этапы развития ИИ при КРР, включая молекулярную верификацию, лучевую диагностику, разработку лекарств и индивидуальное лечение. Подчеркнуты преимущества ИИ в анализе медицинских изображений, таких как КТ, МРТ и ПЭТ, что повышает точность диагностики. Рассматриваются такие проблемы развития ИИ, как стандартизация данных и интерпретируемость алгоритмов машинного обучения. Подчеркивается роль ИИ в выборе оптимальной тактики лечения и повышении эффективности хирургического вмешательства. Учитываются этические и нормативные аспекты ИИ, включая доверие пациентов, безопасность данных и ответственность в проведении операций с использованием ИИ. Обсуждаются преимущества ИИ в диагностике, лечении и прогнозировании колоректального рака, проблемы и перспективы улучшения результатов лечения

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