Endorobotic submucosal dissection of rectal lesions using the single port robot DaVinci-SP: initial experience of the first 10 cases.

BACKGROUND: Endoluminal surgery is increasingly recognized as a mode of treatment for colorectal neoplasms with the latest robotic single port platform Da Vinci-SP (Intuitive Surgical, Sunnyvale) facilitating submucosal dissection of benign rectal neoplasm. METHOD: In this study, we present our initial experience with endorobotic submucosal dissection in the management of benign rectal neoplasms using the Da Vinci-SP (Intuitive Surgical, Sunnyvale) between 2020 and 2021. The primary endpoint was the successful completion of submucosal dissection. RESULTS: During the study period, 10 patients underwent endorobotic submucosal dissection for benign rectal neoplasms. The median age of the patients was 68 (range: 59-78) years, and the mean BMI was 29.38 (range: 22.9-38.5). The mean size of the lesion was 42.3 mm (range: 20-65 mm) and was located 7.3 cm (mean) from the anal verge (range: 2-10 cm). The mean operative time was 91.3 minutes (range: 57-137 minutes). All 10 dissections were completed successfully en bloc. There were no intraoperative complications or mortality. All patients were discharged on the same day and commenced on a normal diet. Late bleeding was observed in one patient receiving anticoagulation, and one patient had urinary retention. Pathology revealed two adenocarcinomas, six tubulovillous adenomas, one tubular adenoma, and one sessile serrated adenoma. There was no recurrence in all other patients during the median follow-up of 6 months (range: 4-16 months). CONCLUSIONS: Endorobotic submucosal dissection using a single-port robotic platform is safe and feasible for benign rectal mucosal neoplasms. The semiflexible platform offers an alternative to endoscopic submucosal dissection with favourable outcomes.

Artificial intelligence and digital pathology: clinical promise and deployment considerations.

Artificial intelligence (AI) presents an opportunity in anatomic pathology to provide quantitative objective support to a traditionally subjective discipline, thereby enhancing clinical workflows and enriching diagnostic capabilities. AI requires access to digitized pathology materials, which, at present, are most commonly generated from the glass slide using whole-slide imaging. Models are developed collaboratively or sourced externally, and best practices suggest validation with internal datasets most closely resembling the data expected in practice. Although an array of AI models that provide operational support for pathology practices or improve diagnostic quality and capabilities has been described, most of them can be categorized into one or more discrete types. However, their function in the pathology workflow can vary, as a single algorithm may be appropriate for screening and triage, diagnostic assistance, virtual second opinion, or other uses depending on how it is implemented and validated. Despite the clinical promise of AI, the barriers to adoption have been numerous, to which inclusion of new stakeholders and expansion of reimbursement opportunities may be among the most impactful solutions.