Prospective Multicenter Study to Clarify the Frequency of Peristomal Skin Disorders and Appropriate Evaluation Time in Patients with Malignant Rectal Tumors.

OBJECTIVE: This study aimed to understand the effectiveness of regular care in reducing the incidence of severe peristomal skin disorders, as well as to identify their risk factors. SUMMARY BACKGROUND DATA: Peristomal skin disorders occur frequently in outpatient settings and require appropriate intervention. It remains, however, to be demonstrated when the need to follow up these patients decreases and whether assessing severity of peristomal skin disorders is useful. METHODS: This prospective, multicenter, observational cohort study was conducted in six regional high-volume Japanese hospitals. The primary endpoint of the study was the effectiveness of regular follow-up in reducing the incidence of severe peristomal skin disorders via a scoring system at a defined regular outpatient visit. Propensity score matching was performed to compare a control group and patients with severe peristomal skin disorders. RESULTS: In total, 217 patients between December 2019 and December 2021 were enrolled, and 191 patients were analyzed. Multivariate analysis showed that loop stoma (odds ratio [OR], 5.017; 95% confidence interval [CI], 1.350-18.639; P=0.016) and stoma height of <10 mm (OR, 7.831; 95% CI, 1.760-34.838; P=0.007) were independent risk factors for all peristomal skin disorders. After propensity score matching, the incidence of the disorders was not significantly different between the specified evaluation timing and historical control groups (75.7% vs. 77.2%, P=0.775), and the incidence of the severe disorders based on the ABCD and DET scores (5.9% vs. 19.1%, P<0.001 and 1.5% vs. 29.4%, P<0.001, respectively) was significantly lower in the specified evaluation timing group than in the historical control group. CONCLUSION: Regular peristomal skin disease follow-up and scoring, as well as appropriate stoma care at the stoma outpatient visit did not change the frequency of peristomal skin disease, but severe peristomal skin disorders were prevented. Additionally, risk factors for peristomal skin disorders were found to be height <10 mm and loop stoma.

Dissection layer selection based on an understanding of pelvic fascial anatomy in transanal total mesorectal excision.

This study aimed to review the historical transition of rectal cancer surgery and recent evidence regarding transanal total mesorectal excision (TaTME). Additionally, it outlined the anatomical landmarks and technical considerations essential for successful TaTME. Anatomical studies and surgical techniques were analyzed to identify key landmarks and procedural steps crucial for TaTME. TaTME offers improved visibility and maneuverability even in the deep and narrow pelvis and is expected to contribute to tumor radical cure rates. By securing the circumferential resection margin and distal margin while preserving pelvic autonomic nerve function, TaTME holds promise for maintaining postoperative urinary and sexual functions. Key anatomical landmarks include the endopelvic fascia posteriorly, the S4-pelvic splanchnic nerve laterally, and the prostate or posterior vaginal wall anteriorly. Selecting the appropriate dissection layer based on tumor depth and ensuring precise incision of the tendinous arch of the pelvic fascia contributes to successful TaTME outcomes. TaTME represents a significant advancement in rectal cancer surgery, offering improved outcomes through meticulous attention to anatomical detail and precise dissection techniques. Understanding the historical context of rectal cancer surgery alongside recent evidence on TaTME is essential for optimizing patient outcomes and expanding the safe implementation of this innovative approach.

Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model.

BACKGROUND: There is an increasing demand for automated surgical skill assessment to solve issues such as subjectivity and bias that accompany manual assessments. This study aimed to verify the feasibility of assessing surgical skills using a surgical phase recognition model. METHODS: A deep learning-based model that recognizes five surgical phases of laparoscopic sigmoidectomy was constructed, and its ability to distinguish between three skill-level groups-the expert group, with a high Endoscopic Surgical Skill Qualification System (ESSQS) score (26 videos); the intermediate group, with a low ESSQS score (32 videos); and the novice group, with an experience of < 5 colorectal surgeries (27 videos)-was assessed. Furthermore, 1 272 videos were divided into three groups according to the ESSQS score: ESSQS-high, ESSQS-middle, and ESSQS-low groups, and whether they could be distinguished by the score calculated by multiple regression analysis of the parameters from the model was also evaluated. RESULTS: The time for mobilization of the colon, time for dissection of the mesorectum plus transection of the rectum plus anastomosis, and phase transition counts were significantly shorter or less in the expert group than in the intermediate (p = 0.0094, 0.0028, and < 0.001, respectively) and novice groups (all p < 0.001). Mesorectal excision time was significantly shorter in the expert group than in the novice group (p = 0.0037). The group with higher ESSQS scores also had higher AI scores. CONCLUSION: This model has the potential to be applied to automated skill assessments.

Surgical step recognition in laparoscopic distal gastrectomy using artificial intelligence: a proof-of-concept study.

PURPOSE: Laparoscopic distal gastrectomy (LDG) is a difficult procedure for early career surgeons. Artificial intelligence (AI)-based surgical step recognition is crucial for establishing context-aware computer-aided surgery systems. In this study, we aimed to develop an automatic recognition model for LDG using AI and evaluate its performance. METHODS: Patients who underwent LDG at our institution in 2019 were included in this study. Surgical video data were classified into the following nine steps: (1) Port insertion; (2) Lymphadenectomy on the left side of the greater curvature; (3) Lymphadenectomy on the right side of the greater curvature; (4) Division of the duodenum; (5) Lymphadenectomy of the suprapancreatic area; (6) Lymphadenectomy on the lesser curvature; (7) Division of the stomach; (8) Reconstruction; and (9) From reconstruction to completion of surgery. Two gastric surgeons manually assigned all annotation labels. Convolutional neural network (CNN)-based image classification was further employed to identify surgical steps. RESULTS: The dataset comprised 40 LDG videos. Over 1,000,000 frames with annotated labels of the LDG steps were used to train the deep-learning model, with 30 and 10 surgical videos for training and validation, respectively. The classification accuracies of the developed models were precision, 0.88; recall, 0.87; F1 score, 0.88; and overall accuracy, 0.89. The inference speed of the proposed model was 32 ps. CONCLUSION: The developed CNN model automatically recognized the LDG surgical process with relatively high accuracy. Adding more data to this model could provide a fundamental technology that could be used in the development of future surgical instruments.

Deep Learning-Based Real-Time Ureter Identification in Laparoscopic Colorectal Surgery.

BACKGROUND: Iatrogenic ureteral injury is a serious complication of abdominopelvic surgery. Identifying the ureters intraoperatively is essential to avoid iatrogenic ureteral injury. We developed a model that may minimize this complication. IMPACT OF INNOVATION: We applied a deep learning-based semantic segmentation algorithm to the ureter recognition task and developed a deep learning model called UreterNet. This study aimed to verify whether the ureters could be identified in videos of laparoscopic colorectal surgery. TECHNOLOGY, MATERIALS, AND METHODS: Semantic segmentation of the ureter area was performed using a convolutional neural network-based approach. Feature Pyramid Networks were used as the convolutional neural network architecture for semantic segmentation. Precision, recall, and the Dice coefficient were used as the evaluation metrics in this study. PRELIMINARY RESULTS: We created 14,069 annotated images from 304 videos, with 9537, 2266, and 2266 images in the training, validation, and test data sets, respectively. Concerning ureter recognition performance, the precision, recall, and Dice coefficient for the test data were 0.712, 0.722, and 0.716, respectively. Regarding the real-time performance on recorded videos, it took 71 milliseconds for UreterNet to infer all pixels corresponding to the ureter from a single still image and 143 milliseconds to output and display the inferred results as a segmentation mask on the laparoscopic monitor. CONCLUSIONS: UreterNet is a noninvasive method for identifying the ureter in videos of laparoscopic colorectal surgery and can potentially improve surgical safety. FUTURE DIRECTIONS: Although this deep learning model could lead to the development of an image-navigated surgical system, it is necessary to verify whether UreterNet reduces the occurrence of iatrogenic ureteral injury.

Predictive risk factors for resection surface-related complications after laparoscopic simultaneous resection of primary colorectal tumor and synchronous liver metastases: a single-center retrospective study.

BACKGROUND: Laparoscopic simultaneous resection (LSR) of primary colorectal tumors and synchronous colorectal liver metastases (sCRLM) has been recently performed. This study aimed to evaluate the postoperative outcomes after LSR and determine the risk factors for resection surface-related complications (RSRC), such as postoperative biliary fistula and liver-transection surface abscess. METHODS: Between 2009 and 2022, consecutive patients with sCRLM who underwent LSR were included. We retrospectively analyzed clinicopathological data, including intraoperative factors and postoperative outcomes. The difficulty level of all liver resections was classified according to the IWATE difficulty scoring system (DSS). We then performed univariate and multivariate analyses to identify the risk factors for RSRC. RESULTS: Of the 112 patients, 94 (83.9%) underwent partial hepatectomy and colorectal surgery. The median DSS score was 5 points (1-11), with 12 (10.7%) patients scoring ≥ 7 points. Postoperative complications were observed in 41 (36.6%) patients, of whom 16 (14.3%) experienced severe complications classified as Clavien-Dindo grade IIIa or higher. There was no postoperative mortality. The most common complication was RSRC (19 patients, 17.0%). Multivariate analysis identified American Society of Anesthesiologists (ASA) classification ≥ 3 [odds ratio (OR) 10.3, 95% confidence interval (CI) 1.37-77.8; P = 0.023], DSS score ≥ 7 points (OR 5.08, 95% CI 1.17-20.0; P = 0.030), and right-sided colectomy (OR 4.67, 95% CI 1.46-15.0; P = 0.009) as independent risk factors for RSRC. Postoperative hospital stays were significantly longer for patients with RSRC than for those without RSRC (22 days vs. 11 days; P < 0.001). CONCLUSION: Short-term outcomes of LSR for patients with sCRLM were acceptable in an experienced center. RSRC was the most common complication, and high-difficulty hepatectomy, right-sided colectomy, and ASA classification ≥ 3 were independent risk factors for RSRC.

Preoperative risk factors for 90-day postoperative mortality outcome in patients with non-occlusive mesenteric ischemia.

PURPOSE: The mortality rate for non-occlusive mesenteric ischemia remains high even after patients survive the acute postoperative period with tremendous treatment efforts, including emergency surgery, which is challenging. The aim of this study was to explore the preoperative risk factors for 90-day postoperative mortality in patients with non-occlusive mesenteric ischemia. METHODS: This single-center, retrospective cohort study included patients diagnosed with non-occlusive mesenteric ischemia who underwent emergency surgery between August 2014 and January 2023. All patients were divided into survival-to-discharge and mortality outcome groups at the 90-day postoperative follow-up. Preoperative factors, including comorbidities, preoperative status of vital signs and consciousness, blood gas analysis, blood test results, and computed tomography, were compared between the two groups. RESULTS: Twenty patients were eligible, and 90-day mortality was observed in 10 patients (50%). The mortality outcome group had significantly lower HCO3- (20.9 vs. 14.6, p = 0.006) and higher lactate (4.4 vs. 9.4, p = 0.023) levels than did the survival outcome group. The median postoperative time to death was 19 [2-69] days, and five patients (50%) died after postoperative day 30, mainly because hemodialysis was discontinued because of hemodynamic instability in patients requiring hemodialysis. CONCLUSION: Low preoperative HCO3- and high lactate levels may be preoperative risk factors for 90-day postoperative mortality in patients with non-occlusive mesenteric ischemia. However, patients on hemodialysis die from discontinuing hemodialysis even after surviving the acute postoperative phase. Therefore, indications for emergency surgery in patients with risk factors for postoperative mortality should be carefully determined.

Free-Viewpoint Video in Open Surgery: Development of Surgical Arena 360.

OBJECTIVE: We aimed to apply the free-viewpoint video technology developed and introduced mainly for sports spectators to an open surgical video recording system. DESIGN: Prospective feasibility study. SETTING: University of Tsukuba Hospital, Ibaraki, Japan. PARTICIPANTS: Patients who underwent open pancreaticoduodenectomy for pancreatic cancer between December 2022 and March 2023 were included. The gastrojejunal anastomosis was the subject of the recording. RESULTS: Four surgeries were recorded with Surgical Arena 360, which is the free-viewpoint video system that we developed. The feasibility of performing a series of surgical procedures without interrupting the surgeon's line of sight or manipulation was demonstrated in all cases. CONCLUSIONS: Our study revealed that Surgical Arena 360, an open surgical video recording system developed by applying free-viewpoint video technology, can provide new insights into surgical support and clinical knowledge to surgeons by enabling secure capture of the open surgical field from multiple angles.

Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study.

BACKGROUND: The precise recognition of liver vessels during liver parenchymal dissection is the crucial technique for laparoscopic liver resection (LLR). This retrospective feasibility study aimed to develop artificial intelligence (AI) models to recognize liver vessels in LLR, and to evaluate their accuracy and real-time performance. METHODS: Images from LLR videos were extracted, and the hepatic veins and Glissonean pedicles were labeled separately. Two AI models were developed to recognize liver vessels: the "2-class model" which recognized both hepatic veins and Glissonean pedicles as equivalent vessels and distinguished them from the background class, and the "3-class model" which recognized them all separately. The Feature Pyramid Network was used as a neural network architecture for both models in their semantic segmentation tasks. The models were evaluated using fivefold cross-validation tests, and the Dice coefficient (DC) was used as an evaluation metric. Ten gastroenterological surgeons also evaluated the models qualitatively through rubric. RESULTS: In total, 2421 frames from 48 video clips were extracted. The mean DC value of the 2-class model was 0.789, with a processing speed of 0.094 s. The mean DC values for the hepatic vein and the Glissonean pedicle in the 3-class model were 0.631 and 0.482, respectively. The average processing time for the 3-class model was 0.097 s. Qualitative evaluation by surgeons revealed that false-negative and false-positive ratings in the 2-class model averaged 4.40 and 3.46, respectively, on a five-point scale, while the false-negative, false-positive, and vessel differentiation ratings in the 3-class model averaged 4.36, 3.44, and 3.28, respectively, on a five-point scale. CONCLUSION: We successfully developed deep-learning models that recognize liver vessels in LLR with high accuracy and sufficient processing speed. These findings suggest the potential of a new real-time automated navigation system for LLR.