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.

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.

Iatrogenic common bile duct injuries: Increasing complexity in the laparoscopic era: A prospective cohort study.

PURPOSE: Iatrogenic bile duct injury (BDI) is the most significant associated complication to laparoscopic cholecystectomy (LC). Little is known about the evolution of the pattern of BDI in the era of laparoscopy. The aim of the study is to assess the pattern of post-LC BDIs managed in a tertiary referral centre. METHODS: Post-LC BDI referred over two decades were studied. Demographic data, type of BDI (classified using the Strasberg System), clinical symptoms, diagnostic investigations, timing of referral, post-referral management and morbidity were analysed. The pattern of injury, associated vascular injuries rate and their management were compared over two time periods (1992-2004,2005-2014). RESULTS: 78 BDIs were referred. During the second time period Strasberg A injuries decreased from 14% to 0 and Strasberg E1increased from 4% to 23%, the rate of associated vascular injury was six time higher (3.6% versus 22.7%), more patients had an attempted repair at the index hospital (16% versus 35%) sand fewer patients could be managed without surgical intervention at the referral hospital (28% versus 4%). CONCLUSION: Complexity of referred BDIs and rate of associated vascular injuries have increased over time. These findings led to more patients managed requiring surgical intervention at the referral hospital.

Splinting proximal interphalangeal joint flexion contractures: a new design.

Proximal interphalangeal joint flexion contracture is a common and persistent problem in hand rehabilitation. This article discusses the advantages and disadvantages of several current splint designs for correcting this contracture and introduces an alternate design that uses wire in a 3-point pressure system. The advantages of this design include ease of fabrication, patient appeal, and effectiveness.

Flexor tendon injuries. Treatment of the acute problem.

The management of flexor tendon injuries continues to evolve as our knowledge of tendon biology and physiology improves. The concept of early motion after tendon repair is a key part of this evolutionary process. This section has provided a brief review of the history of early motion and presented a postoperative therapy program for flexor tendon repairs. Patient education, splinting (protective and corrective) and an exercise and activity program have been stressed. It must be emphasized that if early motion is used postoperatively, it must be done in conjunction with a closely supervised hand therapy program.