Advancing indocyanine green fluorescence flap perfusion assessment via near infrared signal quantification.

Introduction: Intraoperative indocyanine green fluorescence angiography (ICGFA) perfusion assessment has been demonstrated to reduce complications in reconstructive surgery. This study sought to advance ICGFA flap perfusion assessment via quantification methodologies. Method: Patients undergoing pedicled and free flap reconstruction were subjected to intraoperative ICGFA flap perfusion assessment using either an open or endoscopic system. Patient demographics, clinical impact of ICGFA and outcomes were documented. From the ICGFA recordings, fluorescence signal quality, as well as inflow/outflow milestones for the flap and surrounding (control) tissue were computationally quantified post hoc and compared on a region of interest (ROI) level. Further software development intended full flap quantification, metric computation and heatmap generation. Results: Fifteen patients underwent ICGFA assessment at reconstruction (8 head and neck, 6 breast and 1 perineum) including 10 free and 5 pedicled flaps. Visual ICGFA interpretation altered on-table management in 33.3% of cases, with flap edges trimmed in 4 and a re-anastomosis in 1 patient. One patient suffered post-operative flap dehiscence. Laparoscopic camera use proved feasible but recorded a lower quality signal than the open system.Using established and novel metrics, objective ICGFA signal ROI quantification permitted perfusion comparisons between the flap and surrounding tissue. Full flap assessment feasibility was demonstrated by computing all pixels and subsequent outputs summarisation as heatmaps. Conclusion: This trial demonstrated the feasibility and potential for ICGFA with operator based and quantitative flap perfusion assessment across several reconstructive applications. Further development and implementation of these computational methods requires technique and device standardisation.

Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.

Colorectal cancer remains a major cause of cancer death and morbidity worldwide. Surgery is a major treatment modality for primary and, increasingly, secondary curative therapy. However, with more patients being diagnosed with early stage and premalignant disease manifesting as large polyps, greater accuracy in diagnostic and therapeutic precision is needed right from the time of first endoscopic encounter. Rapid advancements in the field of artificial intelligence (AI), coupled with widespread availability of near infrared imaging (currently based around indocyanine green (ICG)) can enable colonoscopic tissue classification and prognostic stratification for significant polyps, in a similar manner to contemporary dynamic radiological perfusion imaging but with the advantage of being able to do so directly within interventional procedural time frames. It can provide an explainable method for immediate digital biopsies that could guide or even replace traditional forceps biopsies and provide guidance re margins (both areas where current practice is only approximately 80% accurate prior to definitive excision). Here, we discuss the concept and practice of AI enhanced ICG perfusion analysis for rectal cancer surgery while highlighting recent and essential near-future advancements. These include breakthrough developments in computer vision and time series analysis that allow for real-time quantification and classification of fluorescent perfusion signals of rectal cancer tissue intraoperatively that accurately distinguish between normal, benign, and malignant tissues in situ endoscopically, which are now undergoing international prospective validation (the Horizon Europe CLASSICA study). Next stage advancements may include detailed digital characterisation of small rectal malignancy based on intraoperative assessment of specific intratumoral fluorescent signal pattern. This could include T staging and intratumoral molecular process profiling (e.g. regarding angiogenesis, differentiation, inflammatory component, and tumour to stroma ratio) with the potential to accurately predict the microscopic local response to nonsurgical treatment enabling personalised therapy via decision support tools. Such advancements are also applicable to the next generation fluorophores and imaging agents currently emerging from clinical trials. In addition, by providing an understandable, applicable method for detailed tissue characterisation visually, such technology paves the way for acceptance of other AI methodology during surgery including, potentially, deep learning methods based on whole screen/video detailing.

A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy.

Introduction: Immediate post-mastectomy breast reconstruction offers benefits; however, complications can compromise outcomes. Intraoperative indocyanine green fluorescence angiography (ICGFA) may mitigate perfusion-related complications (PRC); however, its interpretation remains subjective. Here, we examine and develop methods for ICGFA quantification, including machine learning (ML) algorithms for predicting complications. Methods: ICGFA video recordings of flap perfusion from a previous study of patients undergoing nipple-sparing mastectomy (NSM) with either immediate or staged immediate (delayed by a week due to perfusion insufficiency) reconstructions were analysed. Fluorescence intensity time series data were extracted, and perfusion parameters were interrogated for overall/regional associations with postoperative PRC. A naïve Bayes ML model was subsequently trained on a balanced data subset to predict PRC from the extracted meta-data. Results: The analysable video dataset of 157 ICGFA featured females (average age 48 years) having oncological/risk-reducing NSM with either immediate (n=90) or staged immediate (n=26) reconstruction. For those delayed, peak brightness at initial ICGFA was lower (p<0.001) and significantly improved (both quicker-onset and brighter p=0.001) one week later. The overall PRC rate in reconstructed patients (n=116) was 11.2%, with such patients demonstrating significantly dimmer (overall, p=0.018, centrally, p=0.03, and medially, p=0.04) and slower-onset (p=0.039) fluorescent peaks with shallower slopes (p=0.012) than uncomplicated patients with ICGFA. Importantly, such relevant parameters were converted into a whole field of view heatmap potentially suitable for intraoperative display. ML predicted PRC with 84.6% sensitivity and 76.9% specificity. Conclusion: Whole breast quantitative ICGFA assessment reveals statistical associations with PRC that are potentially exploitable via ML.

Technical and functional design considerations for a real-world interpretable AI solution for NIR perfusion analysis (including cancer).

Near infrared (NIR) analysis of tissue perfusion via indocyanine green fluorescence assessment is performed clinically during surgery for a range of indications. Its usefulness can potentially be further enhanced through the application of interpretable artificial intelligence (AI) methods to improve dynamic interpretation accuracy in these and also open new applications. While its main use currently is for perfusion assessment as a tissue health check prior to performing an anastomosis, there is increasing interest in using fluorophores for cancer detection during surgical interventions with most research being based on the paradigm of static imaging for fluorophore uptake hours after preoperative dosing. Although some image boosting and relative estimation of fluorescence signals is already inbuilt into commercial NIR systems, fuller implementation of AI methods can enable actionable predictions especially when applied during the dynamic, early inflow-outflow phase that occurs seconds to minutes after ICG (or indeed other fluorophore) administration. Already research has shown that such methods can accurately differentiate cancer from benign tissue in the operating theatre in real time in principle based on their differential signalling and could be useful for tissue perfusion classification more generally. This can be achieved through the generation of fluorescence intensity curves from an intra-operative NIR video stream. These curves are processed to adjust for image disturbances and curve features known to be influential in tissue characterisation are extracted. Existing machine learning based classifiers can then use these features to classify the tissue in question according to prior training sets. The use of this interpretable methodology enables accurate classification algorithms to be built with modest training sets in comparison to those required for deep learning modelling in addition to achieving compliance with medical device regulations. Integration of the multiple algorithms required to achieve this classification into a desktop application or medical device could make the use of this method accessible and useful to (as well as useable by) surgeons without prior training in computer technology. This document details some technical and functional design considerations underlying such a novel recommender system to advance the foundational concept and methodology as software as medical device for in situ cancer characterisation with relevance more broadly also to other tissue perfusion applications.

Consensus conference statement on fluorescence-guided surgery (FGS) ESSO course on fluorescence-guided surgery.

BACKGROUND: Fluorescence-guided surgery (FGS) has emerged as an innovative technique with promising applications in various surgical specialties. However, clinical implementation is hampered by limited availability of evidence-based reference work supporting the translation towards standard-of-care use in surgical practice. Therefore, we developed a consensus statement on current applications of FGS. METHODS: During an international FGS course, participants anonymously voted on 36 statements. Consensus was defined as agreement ≥70% with participation grade of ≥80%. All participants of the questionnaire were stratified for user and handling experience within five domains of applicability (lymphatics & lymph node imaging; tissue perfusion; biliary anatomy and urinary tracts; tumor imaging in colorectal, HPB, and endocrine surgery, and quantification and (tumor-) targeted imaging). Results were pooled to determine consensus for each statement within the respective sections based on the degree of agreement. RESULTS: In total 43/52 (81%) course participants were eligible as voting members for consensus, comprising the expert panel (n = 12) and trained users (n = 31). Consensus was achieved in 17 out of 36 (45%) statements with highest level of agreement for application of FGS in tissue perfusion and biliary/urinary tract visualization (71% and 67%, respectively) and lowest within the tumor imaging section (0%). CONCLUSIONS: FGS is currently established for tissue perfusion and vital structure imaging. Lymphatics & lymph node imaging in breast cancer and melanoma are evolving, and tumor tissue imaging holds promise in early-phase trials. Quantification and (tumor-)targeted imaging are advancing toward clinical validation. Additional research is needed for tumor imaging due to a lack of consensus.

CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery.

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.

Fallibility of tattooing colonic neoplasia ahead of laparoscopic resection: a retrospective cohort study.

INTRODUCTION: Precise geographical localisation of colonic neoplasia is a prerequisite for proper laparoscopic oncological resection. Preoperative endoscopic peri-tumoural tattoo practice is routinely recommended but seldom scrutinised. METHODS: A retrospective review of recent consecutive patients with preoperative endoscopic lesional tattoo who underwent laparoscopic colonic resection as identified from our prospectively maintained cancer database with supplementary clinical chart and radiological, histological, endoscopic and theatre database/logbook interrogation. RESULTS: Some 210 patients with 'tattooed' colonic neoplasia were identified, of whom 169 underwent laparoscopic surgery (mean age 68 years, median BMI 27.8kg/m2, male-to-female ratio 95:74). The majority of tumours were malignant (149; 88%), symptomatic (133; 79%) and proximal to the splenic flexure (92; 54%). Inaccurate colonoscopist localisation judgement occurred in 12% of cases, 60% of which were corrected by preoperative staging computed tomography scan. A useful lesional tattoo was absent in 11/169 cases (6.5%) being specifically stated as present in 104 operation notes (61%) and absent in 10 (5.9%). Tumours missing overt peritumoral tattoos intraoperatively were more likely to be smaller, earlier stage and injected longer preoperatively (p=0.006), although half had histological ink staining. Eight lesions missing tattoos were radiologically occult. Four (44%) of these patients had on-table colonoscopy, and five (55%) needed laparotomy (conversion rate 55% vs 23% overall, p<0.005) with one needing a second operation to resect the initially missed target lesion. Mean (range) operative duration and postoperative length of stay of those missing tattoos compared with those with tattoos was 200 (78-300) versus 188 (50-597) min and 15.5 (4-22) versus 12(4-70) days (p>0.05). CONCLUSIONS: Tattoo in advance of attempting laparoscopic resection is vital for precision cancer surgery especially for radiologically unseen tumours to avoid adverse clinical consequence.

Performance effects of simulation training for medical students - a systematic review.

Objective: Simulation based medical education (SBME) is fast becoming embedded into undergraduate medical curricula with many publications now describing its various modes and student self-reported impacts. This systematic review synthesizes the available literature for evidence of performance effects of SBME as an adjunct within traditional teaching programmes. Methods: A narrative systematic review was conducted according to PRISMA guidelines using Ovid MEDLINE, EMBASE, and PubMed databases for studies, published in English, reporting on general medical and surgical undergraduate SBME between 2010 to 2020. Two reviewers independently assessed potential studies for inclusion. Methods and topics of simulation with their assessments were evaluated. Descriptive statistics were used to describe pooled student cohorts. Results: 3074 articles were initially identified using the search criteria with 92 full-text articles then screened for eligibility. Nineteen articles, including nine randomised trials, concerning 2459 students (median 79/study), were selected for review. Cardiac scenarios were commonest (n=6) with three studies including surgical topics. Nine studies used mannequin simulators (median time/session 17.5minutes) versus standardised patients in seven (median time/session=82 minutes). Educational impact was measured by written (n=10), checklist (n=5) and OSCEs (n=3) assessment either alone or in combination (n=1, OSCE/written assessment). All articles reported a positive effect of SBME on knowledge including improved retention in three. Conclusion: SBME, as an adjunct to existing curricula, improves knowledge-based performance of medical students at least in the short-term. Future studies should broaden its topics, assess longer term impacts and cost-effectiveness while also considering whether and what areas of traditional undergraduate learning it can replace.

Aerosols, airflow, and airspace contamination during laparoscopy.

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.

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.

Solving the problems of gas leakage at laparoscopy.

Gas leakage during minimally invasive surgery is an aerosolization hazard. Sensitive optical and thermographic imaging can demonstrate and differentiate between mechanistic categories, enabling engineering solutions to fortify surgical care against pollutants and pathogens affecting operating room teams. Areas for improvement.