Digital surgery for gastroenterological diseases

DATA GOVERNANCE in digital surgery

Technological and computer advances have led to a "new era" of Surgery called Digital Surgery. In it, the management of information is the key. The development of Artificial Intelligence requires "Big Data" to create its algorithms. The use of digital technology for the systematic capture of data from the surgical process raises ethical issues of privacy, property, and consent. The use of these out-of-control data creates uncertainty and can be a source of mistrust and refusal by surgeons to allow its use, requiring a framework for the correct management of them. This paper exposes the current situation of Data Governance in Digital Surgery, the challenges posed and the lines of action necessary to resolve the areas of uncertainty that have arisen in the process, in which the surgeon must play a relevant role.

Fluorescence-guided surgery: comprehensive review

BACKGROUND

Despite significant improvements in preoperative workup and surgical planning, surgeons often rely on their eyes and hands during surgery. Although this can be sufficient in some patients, intraoperative guidance is highly desirable. Near-infrared fluorescence has been advocated as a potential technique to guide surgeons during surgery.

METHODS

A literature search was conducted to identify relevant articles for fluorescence-guided surgery. The literature search was performed using Medical Subject Headings on PubMed for articles in English until November 2022 and a narrative review undertaken.

RESULTS

The use of invisible light, enabling real-time imaging, superior penetration depth, and the possibility to use targeted imaging agents, makes this optical imaging technique increasingly popular. Four main indications are described in this review: tissue perfusion, lymph node assessment, anatomy of vital structures, and tumour tissue imaging. Furthermore, this review provides an overview of future opportunities in the field of fluorescence-guided surgery.

CONCLUSION

Fluorescence-guided surgery has proven to be a widely innovative technique applicable in many fields of surgery. The potential indications for its use are diverse and can be combined. The big challenge for the future will be in bringing experimental fluorophores and conjugates through trials and into clinical practice, as well as validation of computer visualization with large data sets. This will require collaborative surgical groups focusing on utility, efficacy, and outcomes for these techniques.

Real-time administration of indocyanine green in combination with computer vision and artificial intelligence for the identification and delineation of colorectal liver metastases

Introduction

Fluorescence guided surgery for the identification of colorectal liver metastases (CRLM) can be better with low specificity and antecedent dosing impracticalities limiting indocyanine green (ICG) usefulness currently. We investigated the application of artificial intelligence methods (AIM) to demonstrate and characterise CLRMs based on dynamic signalling immediately following intraoperative ICG administration.

Methods

Twenty-five patients with liver surface lesions (24 CRLM and 1 benign cyst) undergoing open/laparoscopic/robotic procedures were studied. ICG (0.05 mg/kg) was administered with near-infrared recording of fluorescence perfusion. User-selected region-of-interest (ROI) perfusion profiles were generated, milestones relating to ICG inflow/outflow extracted and used to train a machine learning (ML) classifier. 2D heatmaps were constructed in a subset using AIM to depict whole screen imaging based on dynamic tissue-ICG interaction. Fluorescence appearances were also assessed microscopically (using H&E and fresh-frozen preparations) to provide tissue-level explainability of such methods.

Results

The ML algorithm correctly classified 97.2 % of CRLM ROIs (n = 132) and all benign lesion ROIs (n = 6) within 90-s of ICG administration following initial mathematical curve analysis identifying ICG inflow/outflow differentials between healthy liver and CRLMs. Time-fluorescence plots extracted for each pixel in 10 lesions enabled creation of 2D characterising heatmaps using flow parameters and through unsupervised ML. Microscopy confirmed statistically less CLRM fluorescence vs adjacent liver (mean ± std deviation signal/area 2.46 ± 9.56 vs 507.43 ± 160.82 respectively p < 0.001) with H&E diminishing ICG signal (n = 4).

Conclusion

ML accurately identifies CRLMs from surrounding liver tissue enabling representative 2D mapping of such lesions from their fluorescence perfusion patterns using AIM. This may assist in reducing positive margin rates at metastatectomy and in identifying unexpected/occult malignancies.

Defining digital surgery for the future

Innovations in robotics, virtual and augmented reality, and artificial intelligence are being rapidly adopted as tools of “digital surgery”. Despite its quickly emerging role, digital surgery is not well understood. A recent study defines the term itself, and then specifies ethical issues specific to the field. These include privacy and public trust, consent, and litigation.