Laparoscopic cholecystectomy is a standard treatment for cholelithiasis. In situations where laparoscopic cholecystectomy is dangerous, a surgeon may be forced to change from laparoscopy to an open procedure. Data from the literature shows that 2 to 15% of laparoscopic cholecystectomies are converted to open surgery during surgery for various reasons. The aim of this study was to identify the risk factors for the conversion of laparoscopic cholecystectomy to open surgery. A retrospective analysis of medical records and operation protocols was performed. The study group consisted of 263 patients who were converted into open surgery during laparoscopic surgery, and 264 randomly selected patients in the control group. Conversion risk factors were assessed using logistic regression analysis that modeled the probability of a certain event as a function of independent factors. Statistically significant factors in the regression model with all explanatory variables were age, emergency treatment, acute cholecystitis, peritoneal adhesions, chronic cholecystitis, and inflammatory infiltration. The use of predictive risk assessments or nomograms can be the most helpful tool for risk stratification in a clinical scenario. With such predictive tools, clinicians can optimize care based on the known risk factors for the conversion, and patients can be better informed about the risks of their surgery.
Background and study aims Several computer-assisted polyp detection systems have been proposed, but they have various limitations, from utilizing outdated neural network architectures to a requirement for multi-graphics processing unit (GPU) processing, to validating on small or non-robust datasets. To address these problems, we developed a system based on a state-of-the-art convolutional neural network architecture able to detect polyps in real time on a single GPU and tested on both public datasets and full clinical examination recordings. Methods The study comprised 165 colonoscopy procedure recordings and 2678 still photos gathered retrospectively. The system was trained on 81,962 polyp frames in total and then tested on footage from 42 colonoscopies and CVC-ClinicDB, CVC-ColonDB, Hyper-Kvasir, and ETIS-Larib public datasets. Clinical videos were evaluated for polyp detection and false-positive rates whereas the public datasets were assessed for F1 score. The system was tested for runtime performance on a wide array of hardware. Results The performance on public datasets varied from an F1 score of 0.727 to 0.942. On full examination videos, it detected 94 % of the polyps found by the endoscopist with a 3 % false-positive rate and identified additional polyps that were missed during initial video assessment. The system’s runtime fits within the real-time constraints on all but one of the hardware configurations. Conclusions We have created a polyp detection system with a post-processing pipeline that works in real time on a wide array of hardware. The system does not require extensive computational power, which could help broaden the adaptation of new commercially available systems.
Technical innovation and entrepreneurship drive economic growth and prosperity. The success of the innovation process depends on utilizing new and existing technical knowledge expeditiously and in novel ways. Many new ideas are the result of the convergence of knowledge from seemingly unrelated domains and/or fields of interest. Moreover, innovative ideas tend to emerge from a combination of experience, published information, and dialogue. This process of collaboration and team science to promote innovation should be an important component of a broadly based Engineering Education program. As many have noted, knowledge can either be explicit or tacit. Nonaka 1 identified four basic patterns for creating knowledge in any organization, with the tacit-to-tacit and explicit-to-tacit knowledge transfer being the most difficult to initiate, particularly between experts from vastly different technical domains. If the innovation process is to advance, the tacit-to-tacit and the explicit-to-tacit knowledge transfers must be facilitated within an organization. Peoria NEXT is an organization, established in 2001, to support the culture of discovery, the creation of innovation and the implementation of commercialization in the areas of life science, material science, and engineering science in Peoria, Illinois USA. Recognizing that the innovation process would be enhanced by collaboration among the over 300 research scientists and engineers from a wide range of domains, the Peoria NEXT organization initiated 11 Knowledge Communities to stimulate the tacit-to-tacit knowledge transfer and the explicit-totacit knowledge transfer to aid in the ideation phase of the innovation process. The Knowledge Communities were established for a one-year period followed by an evaluation period and consisted of groups of 8 to 10 scientists, academics and engineers. The Knowledge Communities were established for a one-year period followed by an evaluation period and consisted of groups of 8 to 10 scientists, academics and engineers. These groups served as a forum to collect, evaluate and embellish new ideas from the various researchers. The Knowledge Communities bridged traditional intellectual disciplines and ranged from Medical and Engineering Robots to Health Systems and Biotechnology to Ethics. Members of the Knowledge Communities came from primarily four organizations with distinct research cultures and with different reward systems. The results of the first-year effort resulted in several successful collaborations, but also clearly determined that the members of each organization have different value propositions for participation. The faculty collaborations can be translated into educational paradigms, particularly for the engineering senior design classes.
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