Indocyanine Green Fluorescence Angiography (ICGFA) has been deployed to tackle malperfusion-related anastomotic complications. This study assesses variations in operator interpretation of pre-anastomotic ICGFA inflow in the gastric conduit. Utilizing an innovative online interactive multimedia platform (Mindstamp), esophageal surgeons completed a baseline opinion-practice questionnaire and proceeded to interpret, and then digitally assign, a distal transection point on 8 ICGFA videos of esophageal resections (6 Ivor Lewis, 2 McKeown). Annotations regarding gastric conduit transection by ICGFA were compared between expert users versus non-expert participants using ImageJ to delineate longitudinal distances with Shapiro Wilk and t-tests to ascertain significance. Expert versus non-expert correlation was assessed via Intraclass Correlation Coefficients (ICC). Thirty participants (13 consultants, 6 ICGFA experts) completed the study in all aspects. Of these, a high majority (29 participants) stated ICGFA should be used routinely with most (21, including 5/6 experts) stating that 11–50 cases were needed for competency in interpretation. Among users, there were wide variations in dosing (0.05–3 mg/kg) and practice impact. Agreement regarding ICGFA video interpretation concerning transection level among experts was ‘moderate’ (ICC = 0.717) overall but ‘good’ (ICC = 0.871) among seven videos with Leave One Out (LOO) exclusion of the video with highest disagreement. Agreement among non-experts was moderate (ICC = 0.641) overall and in every subgroup including among consultants (ICC = 0.626). Experts choose levels that preserved more gastric conduit length versus non-experts in all but one video (P = 0.02). Considerable variability exists with ICGFA interpretation and indeed impact. Even adept users may be challenged in specific cases. Standardized training and/or computerized quantitative fluorescence may help better usage.

In this article, we provide an evidence-based primer of current tools and evolving concepts in the area of intraprocedural artificial intelligence (AI) methods in colonoscopy and laparoscopy as a ‘procedure companion’, with specific focus on colorectal cancer recognition and characterisation. These interventions are both likely beneficiaries from an impending rapid phase in technical and technological evolution. The domains where AI is most likely to impact are explored as well as the methodological pitfalls pertaining to AI methods. Such issues include the need for large volumes of data to train AI systems, questions surrounding false positive rates, explainability and interpretability as well as recent concerns surrounding instabilities in current deep learning (DL) models. The area of biophysics-inspired models, a potential remedy to some of these pitfalls, is explored as it could allow our understanding of the fundamental physiological differences between tissue types to be exploited in real time with the help of computer-assisted interpretation. Right now, such models can include data collected from dynamic fluorescence imaging in surgery to characterise lesions by their biology reducing the number of cases needed to build a reliable and interpretable classification system. Furthermore, instead of focussing on image-by-image analysis, such systems could analyse in a continuous fashion, more akin to how we view procedures in real life and make decisions in a manner more comparable to human decision-making. Synergistical approaches can ensure AI methods usefully embed within practice thus safeguarding against collapse of this exciting field of investigation as another ‘boom and bust’ cycle of AI endeavour.

Introduction: Indocyanine green (ICG) quantification and assessment by machine learning (ML) could discriminate tissue types through perfusion characterisation, including delineation of malignancy. Here, we detail the important challenges overcome before effective clinical validation of such capability in a prospective patient series of quantitative fluorescence angiograms regarding primary and secondary colorectal neoplasia. Methods: ICG perfusion videos from 50 patients (37 with benign (13) and malignant (24) rectal tumours and 13 with colorectal liver metastases) of between 2- and 15-min duration following intravenously administered ICG were formally studied (clinicaltrials.gov: NCT04220242). Video quality with respect to interpretative ML reliability was studied observing practical, technical and technological aspects of fluorescence signal acquisition. Investigated parameters included ICG dosing and administration, distance–intensity fluorescent signal variation, tissue and camera movement (including real-time camera tracking) as well as sampling issues with user-selected digital tissue biopsy. Attenuating strategies for the identified problems were developed, applied and evaluated. ML methods to classify extracted data, including datasets with interrupted time-series lengths with inference simulated data were also evaluated. Results: Definable, remediable challenges arose across both rectal and liver cohorts. Varying ICG dose by tissue type was identified as an important feature of real-time fluorescence quantification. Multi-region sampling within a lesion mitigated representation issues whilst distance–intensity relationships, as well as movement-instability issues, were demonstrated and ameliorated with post-processing techniques including normalisation and smoothing of extracted time–fluorescence curves. ML methods (automated feature extraction and classification) enabled ML algorithms glean excellent pathological categorisation results (AUC-ROC > 0.9, 37 rectal lesions) with imputation proving a robust method of compensation for interrupted time-series data with duration discrepancies. Conclusion: Purposeful clinical and data-processing protocols enable powerful pathological characterisation with existing clinical systems. Video analysis as shown can inform iterative and definitive clinical validation studies on how to close the translation gap between research applications and real-world, real-time clinical utility.

Aim: Treatment pathways for significant rectal polyps differ depending on the underlying pathology, but pre‐excision profiling is imperfect. It has been demonstrated that differences in fluorescence perfusion signals following injection of indocyanine green (ICG) can be analysed mathematically and, with the assistance of artificial intelligence (AI), used to classify tumours endoscopically as benign or malignant. This study aims to validate this method of characterization across multiple clinical sites regarding its generalizability, usability and accuracy while developing clinical‐grade software to enable it to become a useful method.Methods: The CLASSICA study is a prospective, unblinded multicentre European observational study aimed to validate the use of AI analysis of ICG fluorescence for intra‐operative tissue characterization. Six hundred patients undergoing transanal endoscopic evaluation of significant rectal polyps and tumours will be enrolled in at least five clinical sites across the European Union over a 4‐year period. Video recordings will be analysed regarding dynamic fluorescence patterns centrally as software is developed to enable analysis with automatic classification to happen locally. AI‐based classification and subsequently guided intervention will be compared with the current standard of care including biopsies, final specimen pathology and patient outcomes.Discussion: CLASSICA will validate the use of AI in the analysis of ICG fluorescence for the purposes of classifying significant rectal polyps and tumours endoscopically. Follow‐on studies will compare AI‐guided targeted biopsy or, indeed, AI characterization alone with traditional biopsy and AI‐guided local excision versus traditional excision with regard to marginal clearance and recurrence.

Lay summary: Laparoscopic surgery has been undermined throughout the COVID-19 pandemic by concerns that it may generate an infectious risk to the operating team through aerosolization of peritoneal particles. There is anyway a need for increased awareness and understanding of the occupational hazard for surgical teams regarding unfiltered escape of pollutants generated by surgical smoke and other microbials. Here, the aerosol-generating nature of this access modality was confirmed through repeatable real-time methodology both qualitatively and quantitively to inform best practice and additional engineering solutions to optimize the operating room environment.