Identification Of Instruments Research Articles

Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition

Accurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. Artificial Intelligence (AI) models have shown the potential to automate this process. This study evaluates the accuracy of publicly available Large Language Models (LLMs)—ChatGPT-4, ChatGPT-4o, and Gemini—and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.0), in identifying surgical instruments from images. The study utilized a dataset of 92 high-resolution images of 25 surgical instruments (retractors, forceps, scissors, and trocars) photographed from multiple angles. Model performance was evaluated using accuracy, weighted precision, recall, and F1 score. ChatGPT-4o exhibited the highest accuracy (89.1%) in categorizing instruments (e.g., scissors, forceps). SID 2.0 (77.2%) and ChatGPT-4 (76.1%) achieved comparable accuracy, while Gemini (44.6%) demonstrated lower accuracy in this task. For precise subtype identification of instrument names (like “Mayo scissors” or “Kelly forceps”), all models had low accuracy, with SID 2.0 having an accuracy of 39.1%, followed by ChatGPT-4o (33.69%). Subgroup analysis revealed ChatGPT-4 and 4o recognized trocars in all instances. Similarly, Gemini identified surgical scissors in all instances. In conclusion, publicly available LLMs can reliably identify surgical instruments at the category level, with ChatGPT-4o demonstrating an overall edge. However, precise subtype identification remains a challenge for all models. These findings highlight the potential of AI-driven solutions to enhance surgical-instrument management and underscore the need for further refinements to improve accuracy and support patient safety.

Artificial Intelligence in Metamaterial Informatics for Sonic Frequency Mechanical Identification Tags

AbstractDesigning mechanical metamaterials to control wave propagation often requires extensive finite element analysis and discrete Fourier transform simulations before fabricating 3D printed structures and conducting experiments. Here, an alternative approach is presented to developing a metamaterial informatics framework by integrating dataset collection with artificial intelligence (AI), which can significantly accelerate the advancement of phononic wave chip technologies based on the triply periodic minimal surface (TPMS). Visualized data analysis is performed to evaluate the sensitivity of phononic band frequency numbers (BNF). Subsequently, various machine learning algorithms are compared for the prediction of sonic BNFs to create a unique identificable encoded mechanical identification tag (EMIT) interacting with sound waves. Then, for the mechanical decoding part with the help of acoustic analogy, a novel concept technology is developed that integrates 3D‐printed EMITs with a deep‐learning audio classifier for the ownership identification of instruments. Underwater application is discussed further for civil accident investigations, such as echolocating missing aircraft, divers, sunken ships, and containers with valuable cargo. These TPMS‐based EMITs represent the first‐generation passive sonic frequency identification (SFID) transponder‐tags, marking the advent of SFID transponder systems.

A Scoping Review of Existing Policy Instruments to Tackle Overweight and Obesity in India: Recommendations for a Social and Behaviour Change Communication Strategy

Background The Indian government is committed to addressing various manifestations of malnutrition, including overweight and obesity, inorder to improve individual health and well-being. The scoping review aims to map existing national policy instruments (programmes, schemes, regulations and guidelines) addressing overweight and obesity in India and analysing them for Social and Behaviour Change Communication (SBCC) strategies. Methods Systematic identification and selection of policy instruments using ‘Arksey and O’Malley’ framework was conducted from central government ministry websites, between March and June 2023. These instruments focused on nutrition and/or physical activity, targeting specific demographic groups like pregnant women, lactating mothers, children (0-5 years and 5-9 years), or adolescents (10-19 years); excluding those focusing on specific diseases like micronutrient deficiencies, wasting, and stunting. Based on search strategy six policy instruments were included and analysed for SBCC strategies. Results While many policy instruments incorporated SBCC plans; the ‘National Programme for Prevention of Non-Communicable Diseases (NP-NCD)’ stands out as a significant policy initiative specifically targeting the prevention of overweight and obesity within the broader context of Non-Communicable Diseases. It adopts a comprehensive approach addressing key drivers contributing to overweight/obesity across multiple levels of behavioural influence i.e., individual, interpersonal, community and organisation for health promotion. However, there’s need to strengthen SBCC strategies related to prevention and management of obesity, especially screening and counselling, to cover all age groups with a particular focus on adolescents and youth. SBCC strategies can also be incorporated into India’s Integrated Nutrition Support Programme (POSHAN 2.0) and/or Reproductive, Maternal, New-born, Child, Adolescent Health and Nutrition (RMNCAH+N) under the National Health Mission. Conclusion This paper underscores the necessity for comprehensive strategies to address multifaceted origins of overweight and obesity. The NP-NCD stands out as a noteworthy initiative, and there is considerable potential for other programmes to emulate SBCC strategies to bolster their overall effectiveness. Note : *Policy instruments throughout the paper have been used to cover programmes, schemes, regulations and guidelines.

Current application of artificial intelligence in laparoscopic cholecystectomy

Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Surgical complications often occur due to lapses in judgment and decision-making. In laparoscopic cholecystectomy, achievement of the critical view of safety is commonly advocated to prevent bile duct injuries. However, bile duct injuries rates remain stable, probably due to inconsistent application or a poor understanding of critical view of safety. Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. AI-based CV techniques may leverage surgical video data to develop real-time automated decision support tools and surgeon training systems. The effectiveness of CV application in surgical procedures is still under early evaluation. The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. The MedLine, Scopus, and IEEE Xplore databases were searched for publications up to 2024. The keywords used in the search were “laparoscopic cholecystectomy”, “artificial intelligence”. The currently described applications of CV in laparoscopic cholecystectomy are limited. Most current research focus on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic cholecystectomy should focus on application in more scenarios, such as surgeon skill assessment and the development of more efficient models.

A Basic Study on Identification of Surgical Steel Instruments Using Ortho-image with Dimensional Information Obtained by RGB-D Camera

A Basic Study on Identification of Surgical Steel Instruments Using Ortho-image with Dimensional Information Obtained by RGB-D Camera

Comparative analysis of semantic segmentation and multi-boosted faster R-CNN for robotic tool detection in diabetes patient care

Diabetes, a common chronic illness, need meticulous patient care and treatment, which frequently necessitates advanced robotic help. The identification of robotic tools is critical in guaranteeing precise and safe medical procedures. With the growing use of robots in healthcare, reliable identification of instruments inside a patient care setting is critical for allowing smooth and efficient medical procedures. We used a Multi-Boosted Faster-Region-based Convolutional Neural Network (MBF-RCNN) to estimate Robotic Tool Detection in Diabetes Patient Care in the present research. Semantic Segmentation is a machine learning methodology centered on classifying pictures pixel-by-pixel, whereas Multi-Boosted Faster R-CNN recognizes objects using a variety of boosting strategies. Decimal Scaling Normalization is used during the preprocessing step to optimize the input data for both procedures. The effectiveness of both approaches is improved by doing extraction of features using Linear Discriminant Analysis (LDA) to find key trends in the information that is provided.The Multi-Boosted Faster-Region-based Convolutional Neural Network (MBF-RCNN) is an improved variation of the Faster R-CNN design that uses the boosting approach to improve object identification performance.Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic (ROC) curve among the assessment metrics used. The findings show that the suggested approaches are effective in identifying and localizing robotic equipment, which is critical for effective treatments in controlling diabetes.This research promotes computer vision applications in healthcare, particularly in the care of diabetic patients. It assesses the efficiency and dependability of Semantic Segmentation and MBF-RCNN for robotic tool identification, opening the way for more efficient and dependable automation for healthcare contexts.

A Scoping Review of Existing Policy Instruments to Tackle Overweight and Obesity in India: Recommendations for a Social and Behaviour Change Communication Strategy

Background The Indian government is committed to address various manifestations of malnutrition, including overweight and obesity, inorder to improve individual health and well-being. The scoping review aims to map existing national policy instruments (programmes, schemes, regulations and guidelines) addressing overweight and obesity in India and analysing them for Social and Behaviour Change Communication (SBCC) strategies. Methods Systematic identification and selection of policy instruments using ‘Arksey and O’Malley’ framework was conducted from central government ministry websites, between March and June 2023. These instruments focused on nutrition and/or physical activity, targeting specific demographic groups like pregnant women, lactating mothers, children (0-5 years and 5-9 years), or adolescents (10-19 years); excluding those focusing on specific diseases like micronutrient deficiencies, wasting, and stunting. Based on search strategy six policy instruments were included and analysed for SBCC strategies. Results While many policy instruments incorporated SBCC plans; the ‘National Programme for Prevention of Non-Communicable Diseases (NP-NCD)’ stands out as a significant policy initiative specifically targeting the prevention of overweight and obesity within the broader context of Non-Communicable Diseases. It adopts a comprehensive approach addressing key drivers contributing to overweight/obesity across multiple levels of behavioural influence i.e., individual, interpersonal, community and organisation for health promotion. However, there’s need to strengthen SBCC strategies related to prevention and management of obesity, especially screening and counselling, to cover all age groups with a particular focus on adolescents and youth. SBCC strategies can also be incorporated into India’s Integrated Nutrition Support Programme (POSHAN 2.0) and/or Reproductive, Maternal, New-born, Child, Adolescent Health and Nutrition (RMNCAH+N) under the National Health Mission. Conclusion This paper underscores the necessity for comprehensive strategies to address multifaceted origin of overweight and obesity. The NP-NCD stands out as a noteworthy initiative, and there is considerable potential for other programmes to emulate it SBCC strategies to bolster their overall effectiveness. Note : *Policy instrument’s throughout the paper has been used to cover programmes, schemes, regulations and guidelines.

Обеспечение прав граждан на благоприятную окружающую среду Республики Крым

The article is devoted to the identification of legal instruments for ensuring the rights of citizens to a favorable environment in the Republic of Crimea. It is indicated that the constituent element of the right of citizens to a favorable environment is the possibility of satisfying aesthetic, recreational, scientific, cultural interests. On the territory of the Republic of Crimea, an important role in ensuring this aspect is played by parks-monuments of gardening art. Based on the analysis of the practice of applying the norms of regional legislation, it was revealed that the existing legal regime of these objects within the framework of regional protected areas does not provide an adequate level of legal protection. It is proposed to strengthen the legal regime for the protection of parks-monuments of landscape art by transferring them from the lands of specially protected natural areas of regional significance to the lands of historical and cultural purposes.

Research on the identification and evolution of health industry policy instruments in China.

The application of health industry policies could be discovered more quickly and comprehensively through the automated identification of policy tools, which could provide references for the formulation, implementation, and optimization of subsequent policies in each province. This study applies the Bidirectional Encoder Representation from Transformer (BERT) model to identify policy tools automatically, utilizes Focal Loss to reduce the unbalance of a dataset, and analyzes the evolution of policy tools in each province, which contains time, space, and topic. The research demonstrates that the BERT model can improve the accuracy of classification, that supply and environment policy tools are more prevalent than demand tools, and that policy instruments are organized similarly in four major economic regions. Moreover, the policy's attention to topics related to healthcare, medicine, and pollution has gradually shifted to other topics, and the extent of policy attention continues to be concentrated on the health service industry, with less attention paid to the manufacturing industry from the keywords of the various topics.

Breastfeeding assessment and management instruments: a scoping review protocol

IntroductionDifferent aspects are involved in the breastfeeding process that may vary according to the target population, postpartum phase and evaluation scenario. The identification of such instruments, their constructs and applicability...

Machine Learning Predictive Modeling of Agricultural Sustainability Indicators

<p>Modern-day researchers are provided with data abundance that has its drawback: increased analysis complexity. Approaching this issue through traditional data analysis techniques provides only partial solutions to the complex situation. This research offers analytical and predictive models based on machine‐learning algorithms (linear regression, random forest, and generalized additive model) that can be used to assess and improve the Common Agricultural Policy (CAP) impact over agricultural sustainability in European Union (EU) countries, providing the identification of proper instruments that can be adopted by EU policymakers and CAP Council in financial management of the policy. The chosen methodology elaborates custom‐developed models based on a dataset containing 22 relevant indicators, considering three main dimensions contributing to the EU sustainable agriculture development goals in the CAP context: social, environment, and economic. The results showed that sustainable agriculture parameters influenced by the relevant indicators could be modeled with both linear and non-linear regression approaches by utilization of real-time data using machine learning. The predictive analytic models provide satisfactory performance and could be adopted by researchers and practitioners as policy impact monitoring and controlling tools, not only the EU but also for other countries that have or plan to adopt similar agricultural policies.</p><p><strong>Keywords: </strong>Agricultural policies, common agricultural policy, machine learning, rural development, sustainable agriculture</p>

Identification and Assessment of Outcome Measurement Instruments in Cauda Equina Syndrome: A Systematic Review.

This was a systematic review of surgically managed Cauda Equina Syndrome (CES) Outcome Measurement Instruments (OMI). A core outcome set (COS) defines agreed outcomes which should be reported as a minimum in any research study for a specific condition. This study identified OMIs used in the wider CES literature and compare these to the established CESCOS. To identify measurement methods and instruments in the CES surgical outcome evidence base, a systematic review was performed. Medline, Embase and CINAHL plus databases were queried. In addition, a secondary search for validation studies of measurement instruments in CES was undertaken. Identified studies from this search were subject to the COSMIN risk of bias assessment. In total, 112 studies were identified investigating surgical outcomes for CES. The majority (80%, n = 90) of these OMI studies were retrospective in nature and only 55% (n = 62) utilised a measurement method or instrument. The remaining 50 studies used study specific definitions for surgical outcomes defined within their methods. Of the 59 measurement instruments identified, 60% (n = 38 instruments) were patient reported outcome measures. Only one validated instrument was identified, which was a patient reported outcome measure. The validated instrument was not used in any study identified in the initial search (to identify measurement instruments). This review highlights the wide heterogeneity of measurement instruments used in surgically managed CES research. Subsequently, there is need for consensus agreement on which instrument or instruments should be used to measure each core outcome for CES surgical outcomes.

Maternal plasma cortisol’s effect on offspring birth weight: a Mendelian Randomisation study

BackgroundObservational studies and randomized controlled trials have found evidence that higher maternal circulating cortisol levels in pregnancy are associated with lower offspring birth weight. However, it is possible that the observational associations are due to residual confounding.MethodsWe performed two-sample Mendelian Randomisation (MR) using a single genetic variant (rs9989237) associated with morning plasma cortisol (GWAS; sample 1; N = 25,314). The association between this maternal genetic variant and offspring birth weight, adjusted for fetal genotype, was obtained from the published EGG Consortium and UK Biobank meta-analysis (GWAS; sample 2; N = up to 406,063) and a Wald ratio was used to estimate the causal effect. We also performed an alternative analysis using all GWAS reported cortisol variants that takes account of linkage disequilibrium. We also tested the genetic variant’s effect on pregnancy cortisol and performed PheWas to search for potential pleiotropic effects.ResultsThe estimated effect of maternal circulating cortisol on birth weight was a 50 gram (95% CI, -109 to 10) lower birth weight per 1 SD higher log-transformed maternal circulating cortisol levels, using a single variant. The alternative analysis gave similar results (-33 grams (95% CI, -77 to 11)). The effect of the cortisol variant on pregnancy cortisol was 2-fold weaker than in the original GWAS, and evidence was found of pleiotropy.ConclusionsOur findings provide some evidence that higher maternal morning plasma cortisol causes lower birth weight. Identification of more independent genetic instruments for morning plasma cortisol are necessary to explore the potential bias identified.

Energy democracy: Conceptual framework and research agenda for the Portuguese case

The genesis of energy democracy is associated with the demand for a politicization of the energy transition. Its dimensions include democratic control of the energy sector, the redistribution of political and economic power, the universal right to renewable energy and social justice. Energy democracy provides a unique lens to critically assess the political, socioeconomic, and environmental implications of energy transitions. Therefore, based on the Portuguese case study, an analysis grid is developed that aims to facilitate the identification and evaluation of energy democratization instruments in the national context. A research agenda around energy futures in Portugal is also advanced.

CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection.

As medical treatments continue to advance rapidly, minimally invasive surgery (MIS) has found extensive applications across various clinical procedures. Accurate identification of medical instruments plays a vital role in comprehending surgical situations and facilitating endoscopic image-guided surgical procedures. However, the endoscopic instrument detection poses a great challenge owing to the narrow operating space, with various interfering factors (e.g. smoke, blood, body fluids) and inevitable issues (e.g. mirror reflection, visual obstruction, illumination variation) in the surgery. To promote surgical efficiency and safety in MIS, this paper proposes a cross-layer aggregated attention detection network (CLAD-Net) for accurate and real-time detection of endoscopic instruments in complex surgical scenarios. We propose a cross-layer aggregation attention module to enhance the fusion of features and raise the effectiveness of lateral propagation of feature information. We propose a composite attention mechanism (CAM) to extract contextual information at different scales and model the importance of each channel in the feature map, mitigate the information loss due to feature fusion, and effectively solve the problem of inconsistent target size and low contrast in complex contexts. Moreover, the proposed feature refinement module (RM) enhances the network's ability to extract target edge and detail information by adaptively adjusting the feature weights to fuse different layers of features. The performance of CLAD-Net was evaluated using a public laparoscopic dataset Cholec80 and another set of neuroendoscopic dataset from Sun Yat-sen University Cancer Center. From both datasets and comparisons, CLAD-Net achieves the of 98.9% and 98.6%, respectively, that is better than advanced detection networks. A video for the real-time detection is presented in the following link: https://github.com/A0268/video-demo.

A Comprehensive Review on the Role of Obesity in Functional Cognition (Part III): Risk Factors and Studying Challenges

Gaining a comprehensive understanding of the risk factors, devising efficacious therapies, and tackling the methodological obstacles inherent in investigating the cognitive consequences of obesity are imperative undertakings in both clinical and research contexts. The complex interaction between metabolic, inflammatory, and lifestyle factors highlights the necessity of adopting a holistic strategy to address cognitive impairments in patients with obesity. The resolution of methodological obstacles, including ethical concerns and the identification of suitable evaluation instruments, is crucial for the progression of our understanding in this domain and the enhancement of cognitive well-being among individuals impacted by obesity.

Revolutions in Chemistry: Assessment of Six 20th Century Candidates (The Instrumental Revolution; Hückel Molecular Orbital Theory; Hückel's 4n + 2 Rule; the Woodward-Hoffmann Rules; Quantum Chemistry; and Retrosynthetic Analysis).

Six 20th century candidates for revolutions in chemistry are examined, using a definitional scheme published recently by the author. Six groupings of 13 characteristics of revolutions in science are considered: causes and birthings of revolutions, relationships between the old and the new, conceptual qualities of the candidate revolutions, instrumental and methodological functions, social construction of knowledge and practical considerations, and testimonials. The Instrumental Revolution was judged to be a revolution in chemistry because of the enormous increase in community-wide knowledge provided by the new instruments and the intentionality in the identification of specific target instruments, in the mindfulness in their design, manufacture, testing, use, and ultimately commercialization. The Woodward-Hoffmann rules were judged to precipitate the Quantum Chemistry Revolution because of theoretical, practical, and social construction of knowledge characteristics. Neither Hückel molecular orbital theory nor Hückel's 4n + 2 rule was considered an initiator of a revolution in chemistry but rather participants in the Quantum Chemistry Revolution. Retrosynthetic analysis was not judged to initiate a revolution in chemistry.

Physical activity, sedentary behavior and sleep measured by smartwatch: a scoping review protocol

The health impacts of physical activity (PA), sedentary behavior (SB), and sleep are well established; thereby, the identification of instruments to assess and monitor these behaviors at a populational level is relevant. In this context, smartwatches, which are wristwatch-shaped devices equipped with sensors, have been identified as alternatives for objectively measuring PA, SB, and sleep. Therefore, this protocol aimed at describing the goals and methods of a scoping review to map the literature on the use of smartwatches to objectively measure PA, SB, and/or sleep across the lifespan (e.g., children, adolescents, adults, and elderly) and in different contexts. Studies will be included if they use smartwatches to objectively measure at least one of the behaviors (PA, SB, and sleep) in children, adolescents, adults, and older adults, published after 2013. No language filter will be applied. Searches will be carried out in six databases (Pubmed/Medline, Scopus, Web of Science, LILACS, IEEE Xplore Digital Library, Scielo, Health Technology Assessment Database, and Cochrane clinical trials) and two clinical trial repositories. The screening and data extraction will be performed independently by two authors who had previous experience in reviews and technologies. The synthesis of the results will follow the Joanna Briggs Institute framework for extracting the results in scoping reviews. The results can contribute to scientific progress by identifying gaps and research trends, guiding future studies, and informing companies, healthcare professionals, and the general public who use smartwatch as a measurement tool for physical activity, sedentary behavior, and sleep.

On the Organological Potential of Video Games

The various—and often unpredictable—musical applications given to new digital technologies have fundamentally altered our ways of creating, performing, representing, and understanding musical instruments, giving way to new sounding artifacts that pose unprecedented challenges to musical iconography and organology. The multifaceted nature of digital musical instruments has exposed the limitations of traditional organology, prompting a redefinition of the concept of instrumentality and an upsurge of new methodologies. This renovation, however, has yet to recognize the organological potential of video games, which often make use of visual depictions and reconfigurations of musical instruments in the creation and characterization of virtual universes and characters. Musical instruments play an essential role in regulating the tensions between fiction and reality that underlie videoludic experiences by constituting an active agent in the player’s interaction and emerging relationships with the gameworld and its music. Research on these artifacts has been scarce yet promising thus far, providing this article with a wide-ranging collection of references that have served to inform the proposal of a new cataloguing card model for the identification, examination, and classification of musical instruments represented in, and constituted by, video games and consoles. This model merges traditional organological knowledge inherited from hierarchical systems of classification—such as the Hornbostel-Sachs—with new paradigms and taxonomical categories. In so doing, this model aims to accommodate the dual nature of ludomusical instruments as both iconographical resources of undeniable sociocultural transcendence, and organological artifacts with a set of assigned roles within their virtual worlds. The ultimate goal is to provide a framework that fully recognizes the impact of ludomusical instruments in musical experiences and organological knowledge and expectations both inside and outside the medium.

Redefining Nepal’s Foreign Policy on the Basis of Its Soft-Power Assets

This paper examines the bilateral and multilateral relations of Nepal with soft power superpowers. It discusses the proper identification of soft power resources and instruments on their ground and mobilization of those to the targeted country using formal, public, and economic diplomacy with an aim of uplifting soft power ranks and indices. The homilies and diplomatic activities of Nepal have been examined using the soft power theory and the small state lens. In addition, it also aims to analyze Nepal's complex involvement in international affairs at the levels of governments, think tanks, research institutions, and academia. This paper has been prepared primarily based on desk research, mainly utilizing secondary data sources.