CHOLECYSTECTOMY : FUNDUS TO PORTA APPROACH.
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ABSTRACT:
SUBMITTER: Gupta HR
PROVIDER: S-EPMC5530266 | biostudies-other | 1996 Apr
REPOSITORIES: biostudies-other
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Project description:In previous studies the fundus first technique (FF) has been a cost-effective way to simplify the laparoscopic cholecystectomy (LC) and facilitate patient rehabilitation. The feasibility and safety profile when introducing FF as the standard technique were aimed in this study. Between 2004-2014, 29 surgeons performed 1425 LC with FF and 320 with a conventional technique. During the first year 56% were with FF and 98% during the last four years. More females, ultrasonic shears, urgent operations, daycare operations and a shorter operation time were found with FF. 63 (3.6%) complications occurred: 10 (0.6%) bleedings, 33 (1.9%) infections and 12 (0.7%) bile leakages. Leakage from cystic duct occurred in 4/112 (3.6%) when closed with ultrasonic shears and in 4/1633 (0.2%) with clips (p 0.008). A common bile duct lesion occurred in 1/1425 (0.07%) with FF and in 3/320 (0.9%) with the conventional approach (p 0.003). In a multivariate regression model, the conventional technique was a risk factor for bile duct injury with an odds ratio of 20.8 (95% CI 1.6-259.2). In conclusion FF was effectively established as the standard procedure and associated with lower rates of bile duct injuries. Clipless closure of the cystic duct increased the rate of leakage.
| S-EPMC6904718 | biostudies-literature
Project description:Although achieving the critical view of safety (CVS) is useful for avoiding vasculobiliary injury during laparoscopic cholecystectomy (LC), the CVS cannot always be achieved in cases of severe cholecystitis because of technical difficulties. Herein, we focused on segment IV of the liver and its diagonal line (D-line) as a feasible landmark for carrying out difficult LC. The D-line connects the right dorsal and left ventral corners of segment IV and is used as the vectoral landmark, which is where the gallbladder is first dissected to achieve CVS without misidentification. Conversion to subtotal cholecystectomy along the D-line is also feasible when gallbladder wall scarring is severe. We named this procedure the segment IV approach for LC. Sixty-two consecutive difficult LC (including 27 scheduled LC after percutaneous transhepatic gallbladder drainage [PTGBD] and 35 conservatively treated cases of Tokyo Guidelines [TG] grade II cholecystitis) were managed by the segment IV approach. Successful gallbladder extraction along the D-line was achieved in 44 (71%) cases; all of these cases also achieved CVS following total cholecystectomy. The other 18 (29%) cases were converted to subtotal cholecystectomy because gallbladder extraction along the D-line failed as a result of severe cholecystitis with inflammatory adhesion with surrounding structures. Median operative time and intraoperative blood loss were 135 (range, 54-290) min and 10 (range, 0-100) mL, respectively. No intra- or postoperative complications were observed. The segment IV approach is feasible for achieving CVS and for considering subtotal cholecystectomy in difficult LC cases where scarring of the gallbladder wall is present.
| S-EPMC7105843 | biostudies-literature
Project description:PURPOSE:To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA). DESIGN:A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios. PARTICIPANTS:A total of 59?812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol. METHODS:A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD. A second model was trained to predict CGA presence from the same population. A third model was trained to predict CGA presence from the subset of eyes with GA. For training and testing, 5-fold cross-validation was used. For comparison with human clinician performance, model performance was compared with that of 88 retinal specialists. MAIN OUTCOME MEASURES:Area under the curve (AUC), accuracy, sensitivity, specificity, and precision. RESULTS:The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUCs of 0.933-0.976, 0.939-0.976, and 0.827-0.888, respectively. The GA detection model had accuracy, sensitivity, specificity, and precision of 0.965 (95% confidence interval [CI], 0.959-0.971), 0.692 (0.560-0.825), 0.978 (0.970-0.985), and 0.584 (0.491-0.676), respectively, compared with 0.975 (0.971-0.980), 0.588 (0.468-0.707), 0.982 (0.978-0.985), and 0.368 (0.230-0.505) for the retinal specialists. The CGA detection model had values of 0.966 (0.957-0.975), 0.763 (0.641-0.885), 0.971 (0.960-0.982), and 0.394 (0.341-0.448). The centrality detection model had values of 0.762 (0.725-0.799), 0.782 (0.618-0.945), 0.729 (0.543-0.916), and 0.799 (0.710-0.888). CONCLUSIONS:A deep learning model demonstrated high accuracy for the automated detection of GA. The AUC was noninferior to that of human retinal specialists. Deep learning approaches may also be applied to the identification of CGA. The code and pretrained models are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.
| S-EPMC6810830 | biostudies-literature
Project description:Changes in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task.A method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection.The performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment.
| S-EPMC5905988 | biostudies-literature
Project description:Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our method extracts local information independently from multiple rectangular image patches and combines it efficiently through an attention mechanism that focuses on the abnormal regions of the eye (i.e. those that contain DR-induced lesions), thus resulting in a final image representation that is suitable for classification. Furthermore, by leveraging the attention mechanism our algorithm can seamlessly produce informative heatmaps that highlight the regions where the lesions are located. We evaluate our approach on the publicly available Kaggle, Messidor-2 and IDRiD retinal image datasets, in which it exhibits near state-of-the-art classification performance (AUC of 0.961 in Kaggle and 0.976 in Messidor-2), while also producing valid lesion heatmaps (AUPRC of 0.869 in the 81 images of IDRiD that contain pixel-level lesion annotations). Our results suggest that the proposed approach provides an efficient and interpretable solution against the problem of automated diabetic retinopathy grading.
| S-EPMC8275626 | biostudies-literature
Project description:BackgroundDespite some evidence of improved survival with intraoperative cholangiography during cholecystectomy, debate has raged about its benefit, in part because of its questionable benefit, time, and resources required to complete.MethodsAn International Prospective Register of Systematic Reviews-registered (ID CRD42018102154) meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using PubMed, Scopus, Web of Science, and Cochrane library from 2003 to 2018 was undertaken including search strategy "intraoperative AND cholangiogra* AND cholecystectomy." Articles scoring ≥ 16 for comparative and ≥ 10 for noncomparative using the Methodological Index for Non-Randomized Studies criteria were included. A dichotomous random effects meta-analysis using the Mantel-Haenszel method performed on Review Manager Version 5.3 was carried out.ResultsOf 2,059 articles reviewed, 62 met criteria for final analysis. The mean rate of intraoperative cholangiography was 38.8% (range 1.6%-96.4%).There was greater detection of bile duct stones during cholecystectomy with routine intraoperative cholangiography compared with selective intraoperative cholangiography (odds ratio = 3.28, confidence interval = 2.80-3.86, P value < .001). While bile duct injury during cholecystectomy was less with intraoperative cholangiography (0.39%) than without intraoperative cholangiography (0.43%), it was not statistically significant (odds ratio = 0.88, confidence interval = 0.65-1.19, P value = .41). Readmission following cholecystectomy with intraoperative cholangiography was 3.0% compared to 3.5% without intraoperative cholangiography (odds ratio = 0.91, confidence interval = 0.78-1.06, P value = .23).ConclusionThe use of intraoperative cholangiography still has its place in cholecystectomy based on the detection of choledocholithiasis and the potential reduction of unfavorable outcomes associated with common bile duct stones. This meta-analysis, the first to review intraoperative cholangiography use, identified a marked variation in cholangiography use. Retrospective studies limit the ability to critically define association between intraoperative cholangiography use and bile duct injury.
| S-EPMC8076912 | biostudies-literature
Project description:Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state. Also the diagnosis of diseases did not employ machine learning methods to categorize candidate fundus images into true positive and true negative ratio. Several candidate fundus images did not include more detailed feature selection technique for diabetic retinopathy. To apply machine learning methods and classify the candidate fundus images on the basis of sliding window a method called, Diabetic Fundus Image Recuperation (DFIR) is designed in this paper. The initial phase of DFIR method select the feature of optic cup in digital retinal fundus images based on Sliding Window Approach. With this, the disease state for diabetic retinopathy is assessed. The feature selection in DFIR method uses collection of sliding windows to obtain the features based on the histogram value. The histogram based feature selection with the aid of Group Sparsity Non-overlapping function provides more detailed information of features. Using Support Vector Model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy diseases. The ranking of disease level for each candidate set provides a much promising result for developing practically automated diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, specificity rate, ranking efficiency and feature selection time.
| S-EPMC4431725 | biostudies-literature
Project description:A best evidence topic has been constructed using a described protocol. The three-part question addressed was: is single port laparoscopic cholecystectomy superior to standard cholecystectomy in post-operative pain? Using the reported search, 8083 papers were found. 8 studies were deemed to be suitable to answer the question. The outcomes assessed were post-operative pain differ in single or standard laparoscopic cholecystectomy, all study used VAS (visual analogue scale). The evidence showed no difference in post-operative pain for patients went for single laparoscopic in compared with standard laparoscopic cholecystectomy.
| S-EPMC7895687 | biostudies-literature
Project description:BackgroundTraining for the fundus examination using traditional teaching is challenging, resulting in low generalist physicians' confidence in performing the funduscopic examination. There is growing evidence suggesting a flexible e-learning video approach's value in teaching physical examination procedures. However, whether the flexible e-learning video approach is superior to the traditional, face-to-face (F2F) lecture-based teaching for the funduscopic exam and the cognitive processes supporting its effectiveness has not yet been determined.MethodsWe conducted a sequential explanatory mixed-method study to compare the flexible e-learning video approach's effectiveness versus the F2F lecture-based approach for teaching the funduscopic exam to medical students at Chiba University in Japan. Medical students were randomly assigned to either a flexible e-learning video approach group or a F2F lecture approach group. We then quantitatively measured the diagnostic accuracy of funduscopic findings before and after attending the specific classrooms. Next, we conducted student focus groups to explore the students' thinking processes in the flexible e-learning video approach vs. the F2F lecture-based teaching of fundus examination. The qualitative data were analyzed using the qualitative content analysis method.ResultsThe mean diagnostic accuracy scores in the post-test significantly increased from pre-test in the intervention group (36.6 to 63.4%, p < 0.001). Post-post comparisons across the two groups revealed a significant difference (intervention group 63.4% vs. control group 34.6%, p < 0.001). Six semi-structured focused group interviews were conducted (n = 36). In the flexible e-learning video approach group, we identified ten categories corresponding to four levels of the revised Bloom's taxonomy: remember, understand, apply, analyze. Five categories were identified in the traditional F2F lecture approach group corresponding to three revised Bloom's taxonomy levels: understand, apply, analyze. Interrater reliability was substantial (Cohen's kappa = 0.81).ConclusionsTeaching medical students funduscopic examination using the flexible e-learning video approach leads to improved diagnostic accuracy of funduscopic examinations. The flexible e-learning video teaching method enabled higher cognitive activity levels than the traditional, lecture-based classroom, as assessed using the revised Bloom's taxonomy.Trial registrationThis study was registered with the University Hospital Medical Information Network Clinical Trials Registry on 08/02/2020 (Unique trial number: UMIN 000039434 ).
| S-EPMC8364022 | biostudies-literature
Project description:Single-incision laparoscopic surgery also known as laparo-endoscopic single-site surgery for cholecystectomy is performed using a single umbilical skin incision through which a laparoscope and two instruments are introduced. It is virtually a "scarless" surgery. The present study was undertaken to evaluate the efficacy of single-incision laparoscopic cholecystectomy using conventional instruments and compare it with three-port laparoscopic cholecystectomy. Thirty patients who underwent single-incision cholecystectomy were compared to an equal number of patients who underwent three-port cholecystectomy. Both groups were assessed on the basis of operative time, intraoperative complications, postoperative pain, ambulation, hospital stay, and body image at first and third week. Single-incision cholecystectomy had the advantage of less postoperative pain, early ambulation, and better body image as compared to three-port cholecystectomy; the results being statistically significant. There was no statistically significant difference in operative time and hospital stay between the two groups. Single-incision laparoscopic cholecystectomy using conventional instruments is a safe and effective surgery. It gives better cosmetic results, almost scarless surgery, without increasing the cost of surgery.
| S-EPMC4692840 | biostudies-literature