Triplet network for classification of benign and pre-malignant polyps

Colorectal polyps are critical indicators of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models, albeit with limited success. An early detection of CRC prevents further complications in the colon, which makes identification of abnormal tissue a crucial step during routinary colonoscopy. In this paper, a classification approach is proposed to differentiate between benign and pre-malignant polyps using features learned from a Triplet Network architecture. The study includes a total of 154 patients, with 203 different polyps. For each polyp an image is acquired with White Light (WL), and additionally with two recent endoscopic modalities:Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). The network is trained with the associated triplet loss, allowing the learning of non-linear features, which prove to be a highly discriminative embedding, leading to excellent results with simple linear classifiers. Additionally, the acquisition of multiple polyps with WL, BLI and LCI, enables the combination of the posterior probabilities, yielding a more robust classification result. Threefold cross-validation is employed as validation method and accuracy, sensitivity, specificity and area under the curve (AUC) are computed as evaluation metrics. While our approach achieves a similar classification performance compared to state-of-the-art methods, it has a much lower inference time (from hours to seconds, on a single GPU). The increased robustness and much faster execution facilitates future advances towards patient safety and may avoid time-consuming and costly histhological assessment.</p

Polyp malignancy classification with CNN features based on Blue Laser and Linked Color Imaging

In-vivo classification of benign and pre-malignant polyps is a laborious task that requires histophatology confirmation. In an effort to improve the quality of clinical diagnosis, medical experts have come up with visual models with only limited success. In this paper, a classification approach is proposed to differentiate between polypmalignancy, using features extracted from the Global Average Pooling (GAP) layer of a pre-trained Convolutional Neural Network (CNNs) . Two recently developed endoscopic modalities are used to improve the pipeline prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle the differences of unbalanced class distribution. The results are compared with a more general approach, showing how artificial examples can improve results on highly unbalanced problems. For the same reason, the combined features for each patient are extracted and trained using several machine learning classifiers without CNNs. Moreover to speed up computation, a recent GPU based Support Vector Machine (SVM) scheme is employed to substantially decrease the overload during training time. The presented methodology shows the feasibility of using the LCI and BLI techniques for automatic polypmalignancy classification and facilitates future advances to limit the need for time-consuming and costly histopathological assessment

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is limited by interobserver variability and generally only meets accepted standards in expert settings. Real-time decision-support software has been developed to detect and characterise polyps, and also to offer feedback on the technical quality of inspection. Some of the current algorithms, particularly with recent advances in artificial intelligence techniques, match human expert performance for optical biopsy. In this Review, we summarise the evidence for clinical applications of computer-aided diagnosis and artificial intelligence in colonoscopy

Quality Assurance of Computer-Aided Detection and Diagnosis in Colonoscopy

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field “Deep Learning,” have direct implications for computer-aided detection and diagnosis (CADe/CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice; polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect and discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both, CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine learning based CADe/CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing

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

SCALING ARTIFICIAL INTELLIGENCE IN ENDOSCOPY: FROM MODEL DEVELOPMENT TO MACHINE LEARNING OPERATIONS FRAMEWORKS

Questa tesi esplora l'integrazione dell'intelligenza artificiale (IA) in Otorinolaringoiatria – Chirurgia di Testa e Collo, concentrandosi sui progressi della computer vision per l’endoscopia e le procedure chirurgiche. La ricerca inizia con una revisione completa dello stato dell’arte dell'IA e della computer vision in questo campo, identificando aree per ulteriori sviluppi. L'obiettivo principale è stato quello di sviluppare un sistema di computer vision per l'analisi di immagini e video endoscopici. La ricerca ha coinvolto la progettazione di strumenti per la rilevazione e segmentazione di neoplasie nelle vie aerodigestive superiori (VADS) e la valutazione della motilità delle corde vocali, cruciale nella stadiazione del carcinoma laringeo. Inoltre, lo studio si è focalizzato sul potenziale dei foundation vision models, vision transformers basati su self-supervised learning, per ridurre la necessità di annotazione da parte di esperti, approccio particolarmente vantaggioso in campi con dati limitati. Inoltre, la ricerca ha incluso lo sviluppo di un'applicazione web per migliorare e velocizzare il processo di annotazione in endoscopia delle VADS, nell’ambito generale delle tecniche di MLOps. La tesi copre varie fasi della ricerca, a partire dalla definizione del quadro concettuale e della metodologia, denominata "Videomics". Include una revisione della letteratura sull'IA in endoscopia clinica, focalizzata sulla Narrow Band Imaging (NBI) e sulle reti neurali convoluzionali (CNN). Lo studio progredisce attraverso diverse fasi, dalla valutazione della qualità delle immagini endoscopiche alla caratterizzazione approfondita delle lesioni neoplastiche. Si affronta anche la necessità di standard nel reporting degli studi di computer vision in ambito medico e si valuta l'applicazione dell'IA in setting dinamici come la motilità delle corde vocali. Una parte significativa della ricerca indaga l'uso di algoritmi di computer vision generalizzati (“foundation models”) e la “commoditization” degli algoritmi di machine learning, utilizzando polipi nasali e il carcinoma orofaringeo come casi studio. Infine, la tesi discute lo sviluppo di ENDO-CLOUD, un sistema basato su cloud per l’analisi della videolaringoscopia, evidenziando le sfide e le soluzioni nella gestione dei dati e l’utilizzo su larga scala di modelli di IA nell'imaging medico.This thesis explores the integration of artificial intelligence (AI) in Otolaryngology – Head and Neck Surgery, focusing on advancements in computer vision for endoscopy and surgical procedures. It begins with a comprehensive review of AI and computer vision advancements in this field, identifying areas for further exploration. The primary aim was to develop a computer vision system for endoscopy analysis. The research involved designing tools for detecting and segmenting neoplasms in the upper aerodigestive tract (UADT) and assessing vocal fold motility, crucial in laryngeal cancer staging. Further, the study delves into the potential of vision foundation models, like vision transformers trained via self-supervision, to reduce the need for expert annotations, particularly beneficial in fields with limited cases. Additionally, the research includes the development of a web application for enhancing and speeding up the annotation process in UADT endoscopy, under the umbrella of Machine Learning Operations (MLOps). The thesis covers various phases of research, starting with defining the conceptual framework and methodology, termed "Videomics". It includes a literature review on AI in clinical endoscopy, focusing on Narrow Band Imaging (NBI) and convolutional neural networks (CNNs). The research progresses through different stages, from quality assessment of endoscopic images to in-depth characterization of neoplastic lesions. It also addresses the need for standards in medical computer vision study reporting and evaluates the application of AI in dynamic vision scenarios like vocal fold motility. A significant part of the research investigates the use of "general purpose" vision algorithms and the commoditization of machine learning algorithms, using nasal polyps and oropharyngeal cancer as case studies. Finally, the thesis discusses the development of ENDO-CLOUD, a cloud-based system for videolaryngoscopy, highlighting the challenges and solutions in data management and the large-scale deployment of AI models in medical imaging

Spatio-temporal classification for polyp diagnosis

Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-cancerous polyps. Computer-aided polyp characterisation can determine which polyps need polypectomy and recent deep learning-based approaches have shown promising results as clinical decision support tools. Yet polyp appearance during a procedure can vary, making automatic predictions unstable. In this paper, we investigate the use of spatio-temporal information to improve the performance of lesions classification as adenoma or non-adenoma. Two methods are implemented showing an increase in performance and robustness during extensive experiments both on internal and openly available benchmark datasets

Confident texture-based laryngeal tissue classification for early stage diagnosis support

none8siopenMoccia, Sara; De Momi, Elena; Guarnaschelli, Marco; Savazzi, Matteo; Laborai, Andrea; Guastini, Luca; Peretti, Giorgio; Mattos, Leonardo S.Moccia, Sara; De Momi, Elena; Guarnaschelli, Marco; Savazzi, Matteo; Laborai, Andrea; Guastini, Luca; Peretti, Giorgio; Mattos, Leonardo S

Advanced endoscopic imaging for diagnosis of inflammatory bowel diseases : present and future perspectives

Crohn's disease and ulcerative colitis are chronic inflammatory bowel diseases (IBD) causing severe damage of the luminal gastrointestinal tract. Differential diagnosis between both disease entities is sometimes awkward requiring a multifactorial pathway, including clinical and laboratory data, radiological findings, histopathology and endoscopy. Apart from disease diagnosis, endoscopy in IBD plays a major role in prediction of disease severity and extent (i.e. mucosal healing) for tailored patient management and for screening of colitis-associated cancer and its precursor lesions. In this state-of-the-art review, we focus on current applications of endoscopy for diagnosis and surveillance of IBD. Moreover, we will discuss the latest guidelines on surveillance and provide an overview of the most recent developments in the field of endoscopic imaging and IBD