A preoperative predictive scoring system to predict the ability to achieve the critical view of safety during laparoscopic cholecystectomy for acute cholecystitis

Onoe, Shunsuke; Maeda, Atsuyuki; Takayama, Yoshiaki; Fukami, Yasuyuki; Kikuchi, Yuji

doi:10.1016/j.hpb.2016.12.013

Cited by 22 publications

(21 citation statements)

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“…Figure 1 shows Calot's triangle in the gallbladder neck, with four landmarks exposed: the common bile duct, cystic duct, lower edge of the left medial liver segment, and Rouviere's sulcus. The use of these four landmarks to avoid BDI during LC has been introduced as the 'critical view of safety' method [13][14][15][16]. Considering the relatively high incidence of BDI in LC, it is doubtful whether the 'critical view of safety' technique is being effectively used in the operating room.…”

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confidence: 99%

Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy

et al. 2020

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Background The occurrence of bile duct injury (BDI) during laparoscopic cholecystectomy (LC) is an important medical issue. Expert surgeons prevent intraoperative BDI by identifying four landmarks. The present study aimed to develop a system that outlines these landmarks on endoscopic images in real time. Methods An intraoperative landmark indication system was constructed using YOLOv3, which is an algorithm for object detection based on deep learning. The training datasets comprised approximately 2000 endoscopic images of the region of Calot's triangle in the gallbladder neck obtained from 76 videos of LC. The YOLOv3 learning model with the training datasets was applied to 23 videos of LC that were not used in training, to evaluate the estimation accuracy of the system to identify four landmarks: the cystic duct, common bile duct, lower edge of the left medial liver segment, and Rouviere's sulcus. Additionally, we constructed a prototype and used it in a verification experiment in an operation for a patient with cholelithiasis. Results The YOLOv3 learning model was quantitatively and subjectively evaluated in this study. The average precision values for each landmark were as follows: common bile duct: 0.320, cystic duct: 0.074, lower edge of the left medial liver segment: 0.314, and Rouviere's sulcus: 0.101. The two expert surgeons involved in the annotation confirmed consensus regarding valid indications for each landmark in 22 of the 23 LC videos. In the verification experiment, the use of the intraoperative landmark indication system made the surgical team more aware of the landmarks. Conclusions Intraoperative landmark indication successfully identified four landmarks during LC, which may help to reduce the incidence of BDI, and thus, increase the safety of LC. The novel system proposed in the present study may prevent BDI during LC in clinical practice. Keywords Artificial intelligence • Bile duct injury • Deep learning • Landmark • Laparoscopic cholecystectomy Laparoscopic cholecystectomy (LC) is widely accepted worldwide [1]. LC is frequently performed by doctors who specialize in endoscopic surgery, and is considered an introductory level endoscopic surgery [2]. Currently, LC is the standard procedure for cholelithiasis and/or cholecystitis. Previous studies have described the standard procedure of

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confidence: 99%

Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy

et al. 2020

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“…We found no statistical significance between increased CRP and the peroperative gallbladder's state of being edematous and/or adherent to peripheral tissue. However, in their studies, Portinari et al, Lee et al and Oneo et al reported to have found a significant correlation between CRP and the patients with complicated cholelithiasis [6][7][8] . We found a statistical significance between the peroperative gallbladder's state of being edematous and/or adherent to peripheral tissues and the preoperative NR rate.…”

Section: Discussionmentioning

confidence: 88%

Predictive factors in identifying operative risks in cholecystectomies

Kanlıöz

Ekici

2019

Preprint

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declare that they have no conflict of interest. Predictive factors in identifying operative risks in cholecystectomies AbstractPurpose : This study aims to forecast findings showing the difficulty of operation in cholecystectomy through pre-operative examinations and reduce morbidity and mortality with the predictive data obtained. Materials and Methods:In the preoperative period, the followings were measured in patients who will undergo cholecystectomy: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), WBC, Neutrophil ratio (NR), erythrocyte distribution range (RDW), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma glutamyl transferase (GGT), total bilirubin (TB), direct bilirubin (DB). Following the preoperative ultrasound (USG), the patients were recorded in two groups as patients with "normal" and "increased" gallbladder wall thickness. Also, the patients were asked if they underwent ERCP and whether they received antibiotic treatment in the last 10 days due to their disease in the preoperative period. The appearance of the peroperative gallbladder was recorded in two groups as "has a normal appearance" or "edematous and/or adherent to peripheral tissues". Whether or not there is a correlation between the preoperative findings and peroperative appearance was evaluated. The recordings and analyzes were made using SPSS statistics program. Correlation between the data were analyzed by Chi-square test. p˂0.05 was considered significant. Results:The study achieved statistically significant results for the correlation between the "gallbladder edema and/or adhesion to peripheral tissues" in the peroperative period and the following five parameters: increased WBC, increased NR, increased gallbladder wall thickness at USG, compulsory ERCP and receipt of antibiotic treatment for the disease in the last 10 days.(p˂0,05). Conclusion:Taking into consideration the presence, in the preoperative period, of some or all of the five criteria -namely, increased WBC, increased NR, increased gallbladder wall thickness at USG, receiving antibiotic treatment for the disease in the last 10 days and undergoing ERCP-in patients with cholelithiasis for whom cholecystectomy is envisaged would make it easier to estimate the degree of difficulty of the surgery and the possibility of encountering difficult and complicated cases.

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“…These are consistent with previous findings in the literature. [13][14][15]27 A pre-operative clinical diagnosis of AC was also an independent predictor of a likely 'complicated' LC. By combining all three pre-operative variables we produced a highly accurate pre-operative prediction model (AUC 0.86).…”

Section: Discussionmentioning

confidence: 96%

“…In the present study, an elevated pre‐operative CRP and the presence of pericholecystic fluid were associated with a ‘complicated’ LC (grade 3 or 4). These are consistent with previous findings in the literature . A pre‐operative clinical diagnosis of AC was also an independent predictor of a likely ‘complicated’ LC.…”

Section: Discussionmentioning

confidence: 99%

Predicting operative difficulty of laparoscopic cholecystectomy in patients with acute biliary presentations

Wennmacker

Bhimani

Dijk

et al. 2019

ANZ Journal of Surgery

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BackgroundEarly laparoscopic cholecystectomy (LC) is advocated in patients with an acute biliary presentation but may require some precaution. We aimed to assess the intra‐operative difficulty of cholecystectomy in patients who underwent early intervention, and to establish a prediction model for a ‘complicated’ LC.MethodsRetrospective analysis of prospectively collected data from patients presenting to the emergency department with acute biliary symptoms, and who subsequently underwent early LC between 2015 and 2018. Operative difficulty was assessed by standardized grading of intra‐operative findings (grades 1–4). Pre‐operative predictors for a ‘complicated’ LC (grades 3/4) were assessed using univariable and multivariable logistic regression analysis. A prediction model was created using variable regression coefficients. Cut‐off and accuracy of the model were assessed using a receiver operating characteristic curve.ResultsA total of 185 patients were included and 59% presented with acute cholecystitis. In this cohort 113 (61%) patients underwent a ‘complicated’ LC. A prediction model for a ’complicated’ LC (0–4.5 points) included: clinical diagnosis of acute cholecystitis (2 points), C‐reactive protein >10.5 mg/L (1.5 points) and pericholecystic fluid on pre‐operative imaging (1 point). A score ≥2.5 had a sensitivity of 77.7%, specificity of 81.7% and positive and negative predictive values of 87.0% and 69.9%, respectively.ConclusionEarly LC may be ‘complicated’ in up to 60% of cases. The presented prediction model uses readily available information in the emergency department and is a simple but accurate way to predict a likely ‘complicated’ LC in patients with acute biliary presentations.

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A preoperative predictive scoring system to predict the ability to achieve the critical view of safety during laparoscopic cholecystectomy for acute cholecystitis

Abstract: Using only three preoperative factors, the proposed scoring system provides an objective evaluation of the likelihood that CVS can be achieved in patients undergoing emergency LC for AC.

Cited by 22 publications

References 20 publications

Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy

Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy

Predictive factors in identifying operative risks in cholecystectomies

Predicting operative difficulty of laparoscopic cholecystectomy in patients with acute biliary presentations

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