Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology.

Background And Aims: The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently.

Methods: A modified Delphi process was used to develop these consensus statements.

Results: Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions.

Automated colonoscopy withdrawal phase duration estimation using cecum detection and surgical tasks classification.

Colorectal cancer is the third most common type of cancer with almost two million new cases worldwide. They develop from neoplastic polyps, most commonly adenomas, which can be removed during colonoscopy to prevent colorectal cancer from occurring. Unfortunately, up to a quarter of polyps are missed during colonoscopies.

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.

Identifying key mechanisms leading to visual recognition errors for missed colorectal polyps using eye-tracking technology.

Background And Aim: Lack of visual recognition of colorectal polyps may lead to interval cancers. The mechanisms contributing to perceptual variation, particularly for subtle and advanced colorectal neoplasia, have scarcely been investigated. We aimed to evaluate visual recognition errors and provide novel mechanistic insights.

Computer-aided characterization of early cancer in Barrett's esophagus on i-scan magnification imaging: a multicenter international study.

Background And Aims: We aimed to develop a computer-aided characterization system that could support the diagnosis of dysplasia in Barrett's esophagus (BE) on magnification endoscopy.

Methods: Videos were collected in high-definition magnification white-light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and nondysplastic BE (NDBE) from 4 centers. We trained a neural network with a Resnet101 architecture to classify frames as dysplastic or nondysplastic.

Polyp detection on video colonoscopy using a hybrid 2D/3D CNN.

Colonoscopy is the gold standard for early diagnosis and pre-emptive treatment of colorectal cancer by detecting and removing colonic polyps. Deep learning approaches to polyp detection have shown potential for enhancing polyp detection rates. However, the majority of these systems are developed and evaluated on static images from colonoscopies, whilst in clinical practice the treatment is performed on a real-time video feed.

A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks.

Background And Aims: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection.

Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia.

Objectives: There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field.

Methods: An AI algorithm was evaluated on four video test datasets containing 173 polyps (35,114 polyp-positive frames and 634,988 polyp-negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia.

Automated Left Ventricle Ischemic Scar Detection in CT Using Deep Neural Networks.

The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows. Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated.

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey.

The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis. There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus. Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers, therefore curative endoscopic therapy can be offered.

Emerging role of cardiac computed tomography in heart failure.

Despite medical advancements, the prognosis of patients with heart failure remains poor. While echocardiography and cardiac magnetic resonance imaging remain at the forefront of diagnosing and monitoring patients with heart failure, cardiac computed tomography (CT) has largely been considered to have a limited role. With the advancements in scanner design, technology, and computer processing power, cardiac CT is now emerging as a valuable adjunct to clinicians managing patients with heart failure.

Image Data Analysis for Quantifying Scar Transmurality in MRI phantoms for Cardiac Resynchronisation Therapy.

The use of implantable cardiac devices has increased in the last 30 years. Cardiac resynchronisation therapy (CRT) is a procedure which involves implanting a coin sized pacemaker for reversing heart failure. The pacemaker electrode leads are implanted into cardiac myocardial tissue.

Mechanical Activation Computation from Fluoroscopy for Guided Cardiac Resynchronization Therapy.

Congestive heart failure is associated with significant morbidity and mortality, as first line treatments are not always effective in improving symptoms and quality of life. Furthermore, 30-50% of patients who are treated with cardiac resynchronization therapy (CRT), a minimally invasive intervention, do not respond when assessed by objective criteria such as cardiac remodeling. Positioning of the left ventricular lead in the latest activating myocardial region is associated with the best outcome.

Non-contrast enhanced simultaneous 3D whole-heart bright-blood pulmonary veins visualization and black-blood quantification of atrial wall thickness.

Purpose: Pre-interventional assessment of atrial wall thickness (AWT) and of subject-specific variations in the anatomy of the pulmonary veins may affect the success rate of RF ablation procedures for the treatment of atrial fibrillation (AF). This study introduces a novel non-contrast enhanced 3D whole-heart sequence providing simultaneous information on the cardiac anatomy-including both the arterial and the venous system-(bright-blood volume) and AWT (black-blood volume).

Methods: The proposed MT-prepared bright-blood and black-blood phase sensitive inversion recovery (PSIR) BOOST framework acquires 2 differently weighted bright-blood volumes in an interleaved fashion.

A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures.

Purpose: Catheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction.

Methods: For the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire-like structures in the X-ray images.

Real-Time X-MRI-Guided Left Ventricular Lead Implantation for Targeted Delivery of Cardiac Resynchronization Therapy.

Objectives: This study sought to test the feasibility of a purpose-built, integrated software platform to process, analyze, and overlay cardiac magnetic resonance (CMR) data in real time within a combined cardiac catheter laboratory and magnetic resonance imaging scanner suite (X-MRI) to guide left ventricular (LV) lead implantation.

Background: Suboptimal LV lead position is a major determinant of poor cardiac resynchronization therapy (CRT) response, and the optimal site is highly patient specific. Pacing myocardial scar is associated with poorer outcomes; conversely, targeting latest mechanical activation (LMA) may improve them.

3D/2D model-to-image registration by imitation learning for cardiac procedures.

Purpose: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e.

3D/2D Registration with superabundant vessel reconstruction for cardiac resynchronization therapy.

A key component of image guided interventions is the registration of preoperative and intraoperative images. Classical registration approaches rely on cross-modality information; however, in modalities such as MRI and X-ray there may not be sufficient cross-modality information. This paper proposes a fundamentally different registration approach which uses adjacent anatomical structures with superabundant vessel reconstruction and dynamic outlier rejection.

A Planning and Guidance Platform for Cardiac Resynchronization Therapy.

Patients with drug-refractory heart failure can greatly benefit from cardiac resynchronization therapy (CRT). A CRT device can resynchronize the contractions of the left ventricle (LV) leading to reduced mortality. Unfortunately, 30%-50% of patients do not respond to treatment when assessed by objective criteria such as cardiac remodeling.

Dynamic mapping of ventricular function from cardiovascular magnetic resonance imaging.

Heart failure is associated with substantial mortality and morbidity and remains the most common diagnosis in older patients. Based on experimental electrophysiologic studies, cardiac resynchronization therapy (CRT) for heart failure results in a maximum resynchronization effect when applied to the most delayed left ventricular (LV) site. Current clinical practice is to identify the optimal site using separate visualisation of scar and activation information.

An augmented reality framework for soft tissue surgery.

Augmented reality for soft tissue laparoscopic surgery is a growing topic of interest in the medical community and has potential application in intra-operative planning and image guidance. Delivery of such systems to the operating room remains complex with theoretical challenges related to tissue deformation and the practical limitations of imaging equipment. Current research in this area generally only solves part of the registration pipeline or relies on fiducials, manual model alignment or assumes that tissue is static.

Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data.

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transformation between both imaging systems, we employ a discriminative learning (DL) based approach to localize the TEE transducer in X-ray images. The successful application of DL methods is strongly dependent on the available training data, which entails three challenges: (1) the transducer can move with six degrees of freedom meaning it requires a large number of images to represent its appearance, (2) manual labeling is time consuming, and (3) manual labeling has inherent errors.