Role of artificial intelligence in hepatobiliary and pancreatic surgery

The utility of axial imaging among selected patients in the early postoperative period after pancreatectomy

BACKGROUND

Postoperative computed tomography imaging has been shown to play an important role in avoiding failure-to-rescue. We sought to examine the impact of the timing of such imaging studies on outcomes after pancreatectomy.

METHODS

Patients who underwent pancreatic resection at our institution from 2017 to 2022 were reviewed retrospectively to identify those undergoing computed tomography for any indication before discharge. Patients were subdivided by the postoperative day that the first computed tomography scan was obtained: immediate (postoperative day <3), early (postoperative day 3-7), and delayed (postoperative day >7).

RESULTS

Of 370 patients, 110 (30%) had a computed tomography during the initial surgical stay. The 3 timing groups were similar in age, comorbidities, pathology, operative time, and number of scans. When comparing the early with the delayed group, we found that antibiotic usage, percutaneous drainage, and overall invasive interventions during surgical stay were all similar. However, those patients who were scanned in the early period had significantly shorter length of stay (17.05 vs 22.82, P = .0008) and fewer composite days hospitalized (20.1 vs 24.9, P = .01) relative to the delayed group. Importantly, early computed tomography imaging was found to be the only independent predictor of a postoperative length of stay ≤15 days on multivariate analysis. Surgical stay mortality rates were significantly lower in the early compared with delayed group (0% vs 11%, P = .02). A change in treatment was observed in 59% after computed tomography, with 15% undergoing invasive interventions, 27% treated medically, and 16% with expectant management.

CONCLUSION

In our cohort, patients imaged early after pancreatectomy experienced shorter hospital stays and lower inpatient mortality relative to those scanned after the first postoperative week.

The integration of artificial intelligence in robotic surgery: A narrative review

BACKGROUND

The rise of high-definition imaging and robotic surgery has independently been associated with improved postoperative outcomes. However, steep learning curves and finite human cognitive ability limit the facility in imaging interpretation and interaction with the robotic surgery console interfaces. This review presents innovative ways in which artificial intelligence integrates preoperative imaging and surgery to help overcome these limitations and to further advance robotic operations.

METHODS

PubMed was queried for "artificial intelligence," "machine learning," and "robotic surgery." From the 182 publications in English, a further in-depth review of the cited literature was performed.

RESULTS

Artificial intelligence boasts efficiency and proclivity for large amounts of unwieldy and unstructured data. Its wide adoption has significant practice-changing implications throughout the perioperative period. Assessment of preoperative imaging can augment preoperative surgeon knowledge by accessing pathology data that have been traditionally only available postoperatively through analysis of preoperative imaging. Intraoperatively, the interaction of artificial intelligence with augmented reality through the dynamic overlay of preoperative anatomical knowledge atop the robotic operative field can outline safe dissection planes, helping surgeons make critical real-time intraoperative decisions. Finally, semi-independent artificial intelligence-assisted robotic operations may one day be performed by artificial intelligence with limited human intervention.

CONCLUSION

As artificial intelligence has allowed machines to think and problem-solve like humans, it promises further advancement of existing technologies and a revolution of individualized patient care. Further research and ethical precautions are necessary before the full implementation of artificial intelligence in robotic surgery.

The Future of Artificial Intelligence in Surgery

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon's perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon's perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon's cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

Robotic pancreas surgery for pancreatic cancer

Since the introduction of robotic pancreas surgery in the early 2000s, there has been significant increase in the adoption of the robot to perform complex pancreatic resections. However, utilization of the robot for pancreatic cancer has lagged behind due to concern for inferior oncologic outcomes. Furthermore, research in this field has previously been limited to small, single institution observational studies. Recent and ongoing randomized control trials in robotic distal pancreatectomy and robotic pancreatoduodenectomy have aimed to address concerns regarding the use of robotic techniques in pancreatic cancer. Together, these studies suggest similar, if not improved, outcomes with a robotic approach, including shorter hospital stays, expedited recovery with less post-operative complications, and equivalent resection rates, when compared to the standard open approaches. Additionally, surgical training in robotic pancreas surgery is of equal importance for patient safety. This review summarizes the available literature on the efficacy and safety of robotic pancreas surgery for pancreatic cancer, with specific focus on robotic distal pancreatectomy and robotic pancreatoduodenectomy.

Virtual Reality for Preoperative Planning in Complex Surgical Oncology: A Single-Center Experience

INTRODUCTION

Virtual reality models (VRM) are three-dimensional (3D) simulations of two-dimensional (2D) images, creating a more accurate mental representation of patient-specific anatomy.

METHODS

Patients were retrospectively identified who underwent complex oncologic resections whose operations differed from preoperative plans between April 2018 and April 2019. Virtual reality modeling was performed based on preoperative 2D images to assess feasibility of use of this technology to create models. Preoperative plans made based upon 2D imaging versus VRM were compared to the final operations performed. Once the use of VRM to create preoperative plans was deemed feasible, individuals undergoing complex oncologic resections whose operative plans were difficult to define preoperatively were enrolled prospectively from July 2019 to December 2021. Preoperative plans made based upon 2D imaging and VRM by both the operating surgeon and a consulting surgeon were compared to the operation performed. Confidence in each operative plan was also measured.

RESULTS

Twenty patients were identified, seven retrospective and 13 prospective, with tumors of the liver, pancreas, retroperitoneum, stomach, and soft tissue. Retrospectively, VRM were unable to be created in one patient due to a poor quality 2D image; the remainder (86%) were successfully able to be created and examined. Virtual reality modeling more clearly defined the extent of resection in 50% of successful cases. Prospectively, all VRM were successfully performed. The concordance of the operative plan with VRM was higher than with 2D imaging (92% versus 54% for the operating surgeon and 69% versus 23% for the consulting surgeon). Confidence in the operative plan after VRM compared to 2D imaging also increased for both surgeons (by 15% and 8% for the operating and consulting surgeons, respectively).

CONCLUSIONS

Virtual reality modeling is feasible and may improve preoperative planning compared to 2D imaging. Further investigation is warranted.

Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review

This literature review delves into the transformative potential of artificial intelligence (AI) in the field of surgery, exploring its evolution, applications, and technological advancements. AI, with its ability to mimic human intelligence, presents a paradigm shift in surgical practices. The review critically analyzes a broad range of research, encompassing machine learning, deep learning, natural language processing, and their diverse applications in preoperative planning, surgical simulation, intraoperative guidance, and postoperative analysis. Ethical, legal, and regulatory considerations, as well as challenges and future directions, are also explored. The study underscores AI's ability to revolutionize surgical visualization and its role in shaping the future of healthcare.

Augmented reality-assisted navigation system contributes to better intraoperative and short-time outcomes of laparoscopic pancreaticoduodenectomy: a retrospective cohort study

BACKGROUND

Augmented reality (AR)-assisted navigation system are currently good techniques for hepatectomy; however, its application and efficacy for laparoscopic pancreatoduodenectomy have not been reported. This study sought to focus on and evaluate the advantages of laparoscopic pancreatoduodenectomy guided by the AR-assisted navigation system in intraoperative and short-time outcomes.

METHODS

Eighty-two patients who underwent laparoscopic pancreatoduodenectomy from January 2018 to May 2022 were enrolled and divided into the AR and non-AR groups. Clinical baseline features, operation time, intraoperative blood loss, blood transfusion rate, perioperative complications, and mortality were analyzed.

RESULTS

AR-guided laparoscopic pancreaticoduodenectomy was performed in the AR group ( n =41), whereas laparoscopic pancreatoduodenectomy was carried out routinely in the non-AR group ( n =41). There was no significant difference in baseline data between the two groups ( P >0.05); Although the operation time of the AR group was longer than that of the non-AR group (420.15±94.38 vs. 348.98±76.15, P <0.001), the AR group had a less intraoperative blood loss (219.51±167.03 vs. 312.20±195.51, P =0.023), lower blood transfusion rate (24.4 vs. 65.9%, P <0.001), lower occurrence rates of postoperative pancreatic fistula (12.2 vs. 46.3%, P =0.002) and bile leakage (0 vs. 14.6%, P =0.026), and shorter postoperative hospital stay (11.29±2.78 vs. 20.04±11.22, P <0.001) compared with the non-AR group.

CONCLUSION

AR-guided laparoscopic pancreatoduodenectomy has significant advantages in identifying important vascular structures, minimizing intraoperative damage, and reducing postoperative complications, suggesting that it is a safe, feasible method with a bright future in the clinical setting.

Three-dimensional visualization and virtual reality simulation role in hepatic surgery: Further research warranted

Artificial intelligence (AI) is the study of algorithms that enable machines to analyze and execute cognitive activities including problem solving, object and word recognition, reduce the inevitable errors to improve the diagnostic accuracy, and decision-making. Hepatobiliary procedures are technically complex and the use of AI in perioperative management can improve patient outcomes as discussed below. Three-dimensional (3D) reconstruction of images obtained via ultrasound, computed tomography scan or magnetic resonance imaging, can help surgeons better visualize the surgical sites with added depth perception. Pre-operative 3D planning is associated with lesser operative time and intraoperative complications. Also, a more accurate assessment is noted, which leads to fewer operative complications. Images can be converted into physical models with 3D printing technology, which can be of educational value to students and trainees. 3D images can be combined to provide 3D visualization, which is used for preoperative navigation, allowing for more precise localization of tumors and vessels. Nevertheless, AI enables surgeons to provide better, personalized care for each patient.

Applicability and reproducibility of the validated intraoperative bleeding severity scale (VIBe scale) in liver surgery: A multicenter study

BACKGROUND

Bleeding is an intraoperative and postoperative complication of liver surgery of concern, and yet evidence to support utility and reproducibility of bleeding scales for liver surgery is limited. We determined the reproducibility of the clinician-reported validated intraoperative bleeding severity scale and its clinical value of implementation in liver surgery.

METHODS

In this descriptive and observational multicenter study, we assessed the performance of liver surgeons instructed on the clinician-reported intraoperative bleeding severity scale using training videos that covered all 5 grades of bleeding severity. Surgeons were stratified according to years of surgical experience and number of surgeries performed per year based on a median split in low and high values. Intraobserver and interobserver agreement was assessed using Kendall's coefficient of concordance (Kendall's W).

RESULTS

Forty-seven surgeons from 10 hospitals in Spain participated in the study. The overall intraobserver concordance was 0.985, and the overall interobserver concordance was 0.929. For "high experience" surgeons, the intraobserver and interobserver agreement values were 0.990 and 0.941, respectively. For "low experience" surgeons, the intraobserver and interobserver agreement was 0.981 and 0.922, respectively. Regarding the annual number of surgeries, intraobserver and interobserver agreement values were 0.995 and 0.940, respectively, for surgeons performing >35 surgeries per year, with 0.979 and 0.923, respectively, for surgeons who perform ≤35 surgeries year.

CONCLUSION

The clinician-reported intraoperative bleeding severity scale shows high interobserver and intraobserver concordance, suggesting it is a useful tool for assessing severity of bleeding during liver surgery; years of surgical experience and number of annual procedures performed did not affect the applicability of the clinician-reported intraoperative bleeding severity scale.

Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

The role of artificial intelligence in pancreatic surgery: a systematic review

Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.

Gallbladder cancer: Historical treatment and new management options

Gallbladder cancer is a rare, aggressive malignancy that has a poor overall prognosis. Effective treatment consists of early detection and surgical treatment. With the wide spread treatment of gallbladder disease with minimally invasive techniques, the rate of incidental gallbladder cancer has seen an equitable rise along with stage migration towards earlier disease. Although the treatment remains mostly surgical, newer modalities such as regional therapy as well as directed therapy based on molecular medicine has led to improved outcomes in patients with advanced disease. We aim to summarize the management of gallbladder cancer along with the newer developments in this formidable disease process.

Current trends in three-dimensional visualization and real-time navigation as well as robot-assisted technologies in hepatobiliary surgery

With the continuous development of digital medicine, minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery. Due to the specificity and complexity of hepatobiliary surgery, traditional preoperative imaging techniques such as computed tomography and magnetic resonance imaging cannot meet the need for identification of fine anatomical regions. Imaging-based three-dimensional (3D) reconstruction, virtual simulation of surgery and 3D printing optimize the surgical plan through preoperative assessment, improving the controllability and safety of intraoperative operations, and in difficult-to-reach areas of the posterior and superior liver, assistive robots reproduce the surgeon’s natural movements with stable cameras, reducing natural vibrations. Electromagnetic navigation in abdominal surgery solves the problem of conventional surgery still relying on direct visual observation or preoperative image assessment. We summarize and compare these recent trends in digital medical solutions for the future development and refinement of digital medicine in hepatobiliary surgery.