Comprehensive Review of Fluorescence Applications in Gynecology.
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ABSTRACT:
SUBMITTER: Polom J
PROVIDER: S-EPMC8509149 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundOver time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.Methods and findingsWe propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.ConclusionsThe ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data.
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Project description:The sentinel lymph nodes (SLNs) is a group of lymph nodes initially involved in the metastatic spread of cancer cells. SLN mapping refers to intraoperative localization and biopsy of SLNs with specific tracers to assess lymph node metastases. It is widely used in a variety of tumor surgeries for its high sensitivity and high negative predictive value. In the evaluation of the status of lymph node metastases in gynecological malignancies, it has received increasingly more attention due to its minor invasiveness, few complications, and high diagnosis rate. The National Comprehensive Cancer Network (NCCN) guidelines provide an excellent introduction to the indications and methods of SLN techniques in vulvar, cervical, and endometrial cancers, but they provide little explanation about some specific issues. In this review, we summarize different dyes and injection methods and discuss the indications of application and the clinical trials of SLN mapping in gynecological malignant tumors, aiming to provide a reference for the rational application of sentinel techniques in gynecology malignant tumors before relevant guidelines are updated.
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Project description:The authors present a systematic review of randomized and observational, retrospective and prospective studies to compare between robotic surgery as opposed to laparoscopic, abdominal, and vaginal surgery for the treatment of both benign and malignant gynecologic indications. The comparison focuses on operative times, surgical outcomes, and surgical complications associated with the various surgical techniques. PubMed was the main search engine utilized in search of study data. The review included studies of various designs that included at least 25 women who had undergone robotic gynecologic surgery. Fifty-five studies (42 comparative and 13 non-comparative) met eligibility criteria. After careful analysis, we found that robotic surgery was consistently connected to shorter post-surgical hospitalization when compared to open surgery, a difference less significant when compared to laparoscopic surgery. Also, it seems that robotic surgery is highly feasible in gynecology. There are quite a few inconsistencies regarding operative times and estimated blood loss between the different approaches, though in the majority of studies estimated blood loss was lower in the robotic surgery group. The high variance in operative times resulted from the difference in surgeon's experience. The decision whether robotic surgery should become mainstream in gynecological surgery or remain another surgical technique in the gynecological surgeon's toolbox requires quite a few more randomized controlled clinical trials. In any case, in order to bring robotic surgery down to the front row of surgery, training surgeons is by far the most important goal for the next few years.
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Project description:Background Core outcome sets (COSs) are the minimum outcomes which should be measured and reported by researchers investigating a specific condition. The definition of standards of COSs vary across different health-related areas. This investigated the characteristics of COSs regarding obstetrics and gynecology (OG) and examined the reports and designs of standards of OG COSs. Methods A comprehensive search was conduced on the COMET database on December 20, 2019 to identify systematic reviews on COSs. Two reviewers independently evaluated whether the reported OG COS met the reporting requirements as stipulated in the Core Outcome Set-STAndards for Reporting (COS-STAR) statement checklist and the minimum design recommendations as outlined in the Core Outcome Set-STAndards for Development (COS-STAD) checklist. Results Forty-four OG COSs related to 26 topics were identified. None of them met all the 25 standards of COS-STAR statement which representing 18 items considered essential for transparent and complete reporting list for all COS studies (range: 6.0-24.0, median: 14.0). The compliance rates to 16 standards of methods and result sections ranged from 27.3%–68.2%. Total COS-STAR compliance items for OG COSs with the prior protocol was significantly higher than without prior protocol (MD = 3.846, 95% CI: 0.835–6.858, P = 0.012). None of the OG COSs met all the 12 criteria in the COS-STAD minimum standards (range: 3.0-11.0, median: 5.0). The compliance rates for all three standards of stakeholders involved and all four standards of the consensus process were lower than 60%. Conclusions Methodological and reporting standards of OG COSs should be improved.
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Project description:BackgroundThe applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.ObjectiveThe aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.MethodsThe PubMed database was searched for citations indexed with "artificial intelligence" and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: "obstetrics"; "gynecology"; "reproductive techniques, assisted"; or "pregnancy." All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.ResultsThe PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.ConclusionsIn OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.
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