Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrutinizing CT scans, MRI, and colonoscopy views to identify polyps and tumors. This ability enables timely and accurate diagnoses, initiating treatment at earlier stages. AI has helped in personalized treatment planning because of its ability to integrate diverse patient data, including tumor characteristics, medical history, and genetic information. Integrating AI into clinical decision support systems guarantees evidence-based treatment strategy suggestions in multidisciplinary clinical settings, thus improving patient outcomes. This narrative review explores the multifaceted role of AI, spanning early detection of colorectal cancer, personalized treatment planning, polyp detection, lymph node evaluation, cancer staging, robotic colorectal surgery, and training of colorectal surgeons.

The role of Da Vinci Xi robotic simulation curriculum in enhancing skills in robotic colorectal surgery.

Robotic surgery in comparison to open and laparoscopic surgery allows better ergonomics, three-dimensional vision, and seven-degree freedom of movement. This ensures fast recovery, fewer postoperative complications, and safe oncological resections. Robotic surgery has revolutionized the field of colorectal surgery, providing surgeons with enhanced precision, dexterity, and visualization. To ensure safe and successful outcomes, surgeons must acquire competency and proficiency in robotic surgical techniques. Robotic simulation exercises have emerged as a valuable tool for training and skill development in robotic colorectal surgery. This research paper explores the importance and relationship between robotic simulation exercises and the acquisition of skills and competency required for carrying out safe colorectal surgery using a robotic platform. The authors discuss the benefits of virtual simulation-based training using the Da Vinci Xi skill simulator, and the evidence supporting its effectiveness in colorectal surgery. In this article, emphasis has been made on some important Da Vinci Xi skill simulator exercises for enhancing skills in robotic colorectal surgery.

Feasibility of robotic platform to perform R0 resection for locally advanced multi-visceral pelvic malignancy: an institutional experience on outcomes of robotic pelvic exenteration.

BACKGROUND: Exenteration surgery for multi-visceral pelvic malignancy is a complex life-changing operation with high perioperative morbidity and mortality. Traditional open surgery has long been the standard approach for pelvic exenteration for achieving Ro resection which is the main aim of surgery. In the current era of minimally invasive surgery, robotic-assisted pelvic exenteration has provided a promising alternative, offering potential advantages in terms of improved oncological outcomes and enhanced postoperative recovery. This study aims to explore the feasibility of a robotic platform for locally advanced multi-visceral pelvic malignancy. METHODS: A retrospective review from the prospectively maintained robotic colorectal surgery database at University Hospital Coventry and Warwickshire (UHCW) Trust was performed. Demographic details and clinical and surgical details were documented from the case records. Data was analysed using SPSS version 22. RESULTS: Thirteen female patients diagnosed with primary or recurrent pelvic malignancy who underwent robotic pelvic exenteration at UHCW between February 2019 and April 2023 at UHCW were included. The mean age of our patients was 60.4 (± 10.1) years. Complete Ro resection was achieved in all 13 (100%) cases on final histopathology. The median length of hospital stay was 15 days after this extensive surgery. Grade 3 morbidity on Clavien-Dindo classification was observed in four (30.7%) patients, while zero percent 30-day mortality was experienced in this study. At a median follow-up of 21 (3-53) months, we observed tumor recurrence in three (23.7%) patients, while death in four (30.7%) patients. Only few studies have highlighted outcomes of robotic pelvic exenteration, and our results were quite comparable to them. CONCLUSION: Robotic-assisted pelvic exenteration for primary or recurrent pelvic malignancy is feasible with improved oncological and acceptable postoperative outcomes.

The robotic learning curve for a newly appointed colorectal surgeon.

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies. However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formalized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve. Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3 to 2.5 h, while total operative time dropped from 6 h to 5.5 h as the surgeon became more experienced; however, this was not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after 30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.