Automatic purse-string suture skill assessment in transanal total mesorectal excision using deep learning-based video analysis

Variation in hospital performances after colorectal cancer surgery: A case-mix adjusted Dutch population based study

BACKGROUND

This study aimed to investigate hospital variability in postoperative mortality and anastomotic leakage (AL) after colorectal cancer surgery, as well as the association with hospital volume and teaching status.

MATERIALS AND METHODS

This nationwide population based study derived data from CRC patients who underwent a surgical resection with primary anastomosis from the Netherlands Cancer Registry between 2015 and 2020. Primary outcomes were 90-day mortality and AL for colon cancer (CC) patients, and AL for rectal cancer (RC) patients. Logistic regression modelling was used to evaluate the association between case-mix factors and hospital volume. Variability in outcomes between hospitals was analysed with Poisson regression.

RESULTS

This study included 44,101 CRC patients, comprising 35,164 CC patients, and 8937 RC patients. In the CC cohort, the unadjusted rates of AL ranged from 2.6 % to 14.4 %, and the unadjusted 90-day mortality rates ranged from 0.0 % to 6.7 %. In the RC cohort, the unadjusted rates of AL ranged from 0.0 % to 28.6 %. After case-mix adjustment, two hospitals performed significantly worse than expected regarding 90-day mortality in the CC cohort, and in both CC and RC cohorts, significant outliers were observed concerning AL. Amongst CC patients, low case volume (OR 1.26 95%CI 1.08-1.46) was significantly associated with AL.

CONCLUSION

Statistically significant variations in hospital performance were observed among Dutch hospitals after CRC surgery, but this effect could not be entirely attributed to hospitals' teaching status. Nevertheless, concentrating care has the potential to improve outcomes by enhancing individual surgical performance and optimizing care pathways.

Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.