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BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy., Competing Interests: Y. Mori has received consultancy and lecture fees from Olympus Corporation. M. Misawa has received consultancy and lecture fees from Olympus Corporation. T. Berzin has received consultancy fees from Wision AI, Medtronic, and Boston Scientific. M.F. Byrne is CEO and shareholder of Satisfai Health; founder of AI4GI joint venture, a co-development agreement between Olympus America and AI4GI in artificial intelligence and colorectal polyps. J.E. East is on the clinical advisory boards of Boston Scientific, Lumendi (2017 – 2019), and Satisfai Health (2020), and has received speaker’s fees from Falk. W.E. Karnes is co-founder and chief medical officer of DocBot. A. Repici has received consultancy fees and is on the advisory board for Medtronic; he has received consultancy fees, research support, and is on the advisory board for FujiFilm. M.B. Wallace has received consultancy fees from Virgo Inc., Cosmo/Aries Pharmaceuticals, Anx Robotica (2019), Covidien, and GI Supply; he has received research grants from Fujifilm, Boston Scientific, Olympus, Medtronic, Ninepoint Medical, and Cosmo/Aries Pharmaceuticals; he also has stock/stock options in Virgo Inc.; he has provided consulting on behalf of Mayo Clinic to GI Supply (2018), Endokey, Endostart, Boston Scientific, and Microtek; he has also received general payments/minor food and beverage from Synergy Pharmaceuticals, Boston Scientific, and Cook Medical. D. Stoyanov is a shareholder in Odin Vision and Digital Surgery. L.B. Lovat is a minor shareholder and is on the advisory board of Odin Vision; he has received research grants from Medtronic, Pentax Medical, and DynamX; he is also on the advisory board for DynamX and Ninepoint Medical. O.F. Ahmad, S. Kudo, J.T. Anderson, J. Bernal, R. Bisschops, P. Chen, T. Eelbode, D.S. Elson, S.R. Gurudu, A. Histace, R. Singh, P.Valdastri and P.Wang declare that they have no conflict of interest., (© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier B.V.)

Multidimensional fluorescence imaging is a powerful molecular imaging modality that is emerging as an important tool in the study of biological tissues. Due to the large volume of multi-spectral data associated with the technique, it is often difficult to find the best combination of parameters to maximize the contrast between different tissue types. This paper presents a novel framework for the characterization of tissue compositions based on the use of time resolved fluorescence imaging without the explicit modeling of the decays. The composition is characterized through soft clustering based on manifold embedding for reducing the dimensionality of the datasets and obtaining a consistent differentiation scheme for determining intrinsic constituents of the tissue. The proposed technique has the benefit of being fully automatic, which could have significant advantages for automated histopathology and increasing the speed of intraoperative decisions. Validation of the technique is carried out with both phantom data and tissue samples of the human pancreas.