Reliable Outcome Research Articles

Risk factors for cerebrospinal fluid leak after extended endoscopic endonasal surgery for adult patients with craniopharyngiomas: a multivariate analysis of 364 cases.

Over the last decade, the extended endoscopic endonasal approach (EEEA) has evolved as a credible surgical alternative for removing craniopharyngiomas. However, postoperative cerebrospinal fluid (CSF) leak remains one of the most pressing concerns. Craniopharyngiomas often invade the third ventricle, resulting in a higher rate of third ventricle opening after surgery and potentially increasing the risk of postoperative CSF leak. Identifying the risk factors associated with CSF leak after EEEA for craniopharyngiomas may have more clinical value. Nevertheless, there is a lack of systematic studies on the topic. Previous studies yielded inconsistent results, probably due to heterogeneous pathologies or small sample sizes. Hence, the authors present the largest known single-institution case series of the use of purely EEEA for craniopharyngiomas to systematically study the risk factors for postoperative CSF leak. The authors retrospectively reviewed 364 cases of adult patients with craniopharyngiomas who were treated at their institution from January 2019 to August 2022, and they analyzed the risk factors for postoperative CSF leak. The overall rate of postoperative CSF leak was 4.7%. In the univariate analysis, larger dural defect size (OR 8.293, 95% CI 3.711-18.534, p < 0.001) and lower preoperative serum albumin level (OR 0.812, 95% CI 0.710-0.928, p = 0.002) were associated with higher rates of postoperative CSF leak. Predominantly cystic tumors (OR 0.325, 95% CI 0.122-0.869, p = 0.025) were linked to decreased risk of postoperative CSF leak. However, postoperative lumbar drainage (OR 2.587, 95% CI 0.580-11.537, p = 0.213) and third ventricle opening (OR 1.718, 95% CI 0.548-5.384, p = 0.353) were not related to postoperative CSF leak. In the multivariate analysis, larger dural defect size (OR 8.545, 95% CI 3.684-19.821, p < 0.001) and lower preoperative serum albumin level (OR 0.787, 95% CI 0.673-0.919, p = 0.002) were identified as independent risk factors for postoperative CSF leak. The authors' repair technique yielded a reliable reconstructive outcome for high-flow CSF leak in EEEA for craniopharyngioma. Lower preoperative serum albumin level and larger dural defect size were identified as independent risk factors for postoperative CSF leak, potentially providing new insights into minimizing the risk of postoperative CSF leak. Third ventricle opening was not associated with postoperative CSF leak. Lumbar drainage may not be necessary for high-flow intraoperative leak, but this finding may require validation with a prospective randomized controlled trial in the future.

Analysis of the Hulls Floater Distance in the Manoeuvrability of the N219 Floatplane: An Experimental Approach in the Open Water Test

Floatplanes necessitate the utilization of floaters that possess the capacity to facilitate aircraft manoeuvring activities, hence safeguarding passengers against potential hazards associated with sea-based crashes. This paper is to analyse the impact of the manoeuvrability of the N219 floatplane on multiple variables, such as the distance between catamaran hulls, trim angle, and cruising speed. The selection of the open-free-running test method for the testing model is based on its advantages, including its costeffectiveness, simplicity, and demonstrated accuracy in yielding reliable outcomes. The floatplane model is outfitted with a remote-control system that is connected through a wireless communication system. The trajectory of the floatplane model is documented using a Global Positioning System (GPS) device, and the collected data is subsequently transformed into a trajectory line for the purpose of enabling analysis. The turning model test is used to find the best hull distance variation out of three options considering the parameters of tactical diameter (TD), advance (A), transfer (T), and radius (R). The tests are undertaken for three different combinations of trim angle and speed. The results show that S/L 0.5 is the ideal distances between the floater hulls while a trim angle of 0°, and a Froude number of 0.064. The condition defines the ratio of parameters and the length of the ship (Lpp), which includes the values of TD/Lpp, A/Lpp, T/Lpp, and R/Lpp as 2.49, 1.59, 1.60, and 1.26, respectively

Cognitive and Integrated Intervention Therapy in the Geriatric Population and its Benefits on Daily Living Activities and Quality of Life: A Narrative Review

Physiotherapists have expanded their scope into a wide array of fields, including various cognitive-based therapies and mixed integrated intervention therapies. Multiple databases were used in present literature review, including Medline, PubMed, Scopus, Web of Science, and Pedro, which consisted of research studies, systematic reviews, and meta-analyses published between 2002 and 2022. The keywords searched were: cognitive, geriatric, daily living activities, life quality, integrated therapy. Out of the 163 articles retrieved, 52 primary matching articles were included in the review, and 111 articles were excluded as they did not meet the inclusion criteria. Among the matching articles, 39 showed benefits in Quality of Life (QoL) in geriatric individuals with interventions of cognitive therapy and other integrated therapies such as art therapy and music therapy, using valid and reliable outcome scales. The findings and corroborations from these studies indicated the positive effects of cognitive therapy and various integrated intervention therapies on daily living activities and life quality in the elderly population. There is limited data in the literature about the effects of cognitive and integrated intervention therapy in the elderly population. The primary aim of present review study was to analyse the existing available resources and evidence concerning the effects of cognitive therapy and integrated intervention therapy in geriatric individuals.

Ab. No. 101 A Conceptual Model of a Unified Wrist Sensor (UWS) for Non-Invasive Real-Time Preterm Infant Monitoring at NICU

Introduction: This study introduces a groundbreaking conceptual model for a Unified Wrist Sensor (UWS) designed to revolutionize non-invasive real-time monitoring of preterm infants in the Neonatal Intensive Care Unit (NICU). Current pain, sleep, and stress assessments in NICU settings are fraught with subjectivity and time constraints. The proposed UWS addresses these challenges by integrating multiple sensors into a single, non-restrictive wristband. Methods: The conceptual model employs a systematic approach, encompassing pre-processing, feature extraction, optimization, and learning models. Deep learning and machine learning techniques are harnessed to enhance accuracy in processing diverse physiological data. Standardization methods such as linear scaling, log scaling, or z-score are strategically applied to reduce variability, ensuring reliable outcomes across different individuals. Result: The discussion underscores the critical need for intelligent fusion structures that incorporate multiple sensor data and rich contextual information. The UWS aims to strike a balance between clinical utility and practicality, offering complementary, robust, and reliable multimodal sensor data. This approach provides a comprehensive understanding of pain, sleep, and stress patterns in preterm infants, paving the way for personalized treatment protocols. Conclusion: In conclusion, the proposed UWS emerges as a potential game-changer, appropriately reweighting data and enabling real-time monitoring of pain, stress, and sleep in NICU infants. The implications of this research are far-reaching, as the UWS can monitor physiological changes using multiple modalities, assess real-time patient outcomes, and dynamically adjust treatment protocols. Implication: This study contributes significantly to the field of neonatal care and introduces the prospect of seamlessly integrating advanced sensor technology into routine NICU practices.

Evaluating protocols for reproducible targeted metabolomics by NMR.

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Translation and validation of the Addiction Recovery Questionnaire (ARQ)

Substance abuse is a pandemic disease in Pakistan. The use of stimulants such as methamphetamine and ecstasy is common among adolescents, and it is increasing day by day. Our educational institutes are also facing this problem. So, in the present study, the Addiction Recovery Questionnaire (ARQ) was translated and validated in Pakistani culture to address the language barrier. The present study aimed to translate and validate the addiction recovery questionnaire on a sample of 140 substance users. Five independent translators who were experts in translation were selected to translate the ARQ in Urdu, which was finalized by the committee of 3 experts. Similarly, 3 English translators were contacted to translate the Urdu version back into English and shared with the Authors for their feedback. The author's recommendations were also incorporated. The alpha reliability of the ARQ was reported as α = 0.60, which shows the reliability of the measure. CFA was applied for the translated version for model fit, and to confirm the factor loading, the value of Chi-Square 63.20 with RMSEA of 0.04 indicates an adequate fit of the model. The reliability of the measure was first time established in the present study, and the CFA was also done to determine the construct-based factors of recovery. Hence, the ARQ is a reliable and brief recovery outcome that can give information about different dimensions of recovery, such as abstinence, normal living, and positive life expectancy.

Measuring the Impact of Human Resource Practices on Organization Performance: Scale Validation Based on Pilot Study

Validity and reliability are crucial when interpreting scores derived from psychometric instruments like questionnaires, symptom scales, educational tests, and observer ratings. These instruments find application in clinical practice, research, education, and administration. The traditional concepts of face, content, and criterion validity are being replaced by the unified concept of "construct validity," which pertains to the extent to which a score accurately represents the intended underlying construct. Validity can be envisioned as a meticulously structured argument, where evidence is compiled to either support or challenge proposed interpretations of findings. While reproducible (reliable) outcomes are essential, they alone do not suffice to form valid conclusions. This research aims to develop a scale to determine the impact of HRPs on an organization’s performance. The principal component factor analysis results reveal that the scale encompasses six distinct dimensions, each comprising four to five items. To establish the validity and reliability of the scale, various statistical analyses were conducted, including Cronbach Alpha, Average Variance Extracted, HTMT, and Fornell &amp; Larker. This piece offers a concise introduction to the concepts of validity and reliability concerning the overall effectiveness of test instruments. It outlines the essence and understanding of content, face, criteria, and construct validity, and highlights various methods for gauging and enhancing reliability. The use of PLS-SEM in conjunction with SPSS allowed thorough analysis. When an item fails to reach the statistical testing threshold level, it is reviewed and possibly revised. Although some things were not deemed valuable at first, they were later considered useful in gathering sufficient data to complete the primary study. This study identified key errors in its chosen instrument based on preliminary testing with suggestions to review or revisit particular item/s that may impact results while undertaking subsequent data analyses.

Development Of Google Form-Based Learning Outcome Evaluation Instruments For Animal Development Courses In The Biology Education Study Program FMIPA UNIMED

This study aimed to develop a valid and reliable google form-based learning outcome evaluation instrument for the Animal Development course. The instrument was developed following the 4D development model (define, design, develop, dessiminate). The research subjects were Biology Education Study Program (PSPB) class of 2021 class B students totaling 26 people. Data were obtained from validation questionnaires and student response questionnaires. The initial design of the learning outcome evaluation instrument consisted of 50 questions. Based on the validator's assessment, an instrument consisting of 25 valid and reliable questions was obtained. The results of validation by evaluation instrument experts obtained an average percentage of 98.6% with a very feasible category. Based on the validity analysis of the Aiken index, the value V = 0.942 was obtained with high validity criteria. Instrument reliability using Cronbach's Alpha obtained a value of 0.796 with high criteria (reliable), while the analysis of ICC reliability data obtained the results of inter-rater agreement of 0.796, and for each rater the consistency is 0.565. It can be concluded that the agreement between raters is very strong and each rater has a fairly good consistency. The results of the data analysis of student responses showed that each indicator was rated very well with an average percentage of 88%.theresa612

Perceived Effect of Monetary Policy Measures on Internally Generated Revenue (IGR) and Funding of Universities in Nigeria

The study looked into the perceived effect of monetary policy measures on internally generated revenue (IGR) and funding of universities in Nigeria. Ex post facto design was adopted for the study. Two hypotheses were tested at 0.05 alpha level. The sample size of 60 was drawn the through multistage procedure. A researcher’s developed instrument, 'Effect of Monetary Policy Measures on IGR and Funding of Universities Questionnaire (MPMIGRFUQ)' was used to collect data. The Cronbach Alpha method, with the coefficient of 0.92 was used to ascertain the reliability of the instrument. T-test was adopted for analysis and testing of the hypotheses. It was found that the mean ratings of male and female lecturers of banking and finance lecturers with regard to the effect of monetary policy measures on IGR and funding of universities did not differ significantly. The study recommended among others opinions that more researches through primary data should be carried out beyond the scope of opinions of banking and finance lecturers, by involving respondents from different walks of life, for more inclusive and reliable outcomes.

Limited Incision Rhinoplasty: An Approach for All Primary and Revision Cases.

Rhinoplasty was one of the most frequently performed aesthetic surgeries in the USA in 2022. Traditionally, the open approach has been preferred by the majority of surgeons often due to familiarity and ease of visualization. However, patient interest in closed and preservation rhinoplasty is driving a resurgence in the popularity of endonasal techniques. We present a series of 100 consecutive endonasal primary and revision rhinoplasty cases using bilateral isolated modified rim incisions. One-hundred consecutive patients underwent closed rhinoplasty via isolated modified rim incisions at a single-surgeon private practice. The senior author performs 100% endonasal rhinoplasty. A retrospective chart review was performed from 06/25/20 to 09/21/22. Information on demographics, complications, and need for revision was collected. Eighty-four patients underwent primary rhinoplasty, 11 underwent secondary rhinoplasty, 4 underwent tertiary rhinoplasty, and 1 underwent quaternary rhinoplasty. Isolated modified rim incisions were used in all cases except in cases of septoplasty when a unilateral Killian incision was added, or in cases of lateral osteotomy when vestibular stab incisions were added. Post-operatively, six (6.0%) patients required revision, all of which were performed under local anesthesia. Limited incision rhinoplasty is a reliable surgical approach that produces predictable results with a low revision rate. This technique is highly effective in minimizing soft tissue disruption to ensure safe, reliable, and effective outcomes in primary and revision rhinoplasty. It is an easier technique to learn compared to traditional endonasal and even arguably open rhinoplasty, thus lending itself to widespread adoption especially among novice rhinoplasty surgeons. Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Whole-Body Muscle Magnetic Resonance Imaging in 81 Patients with Spinal and Bulbar Muscular Atrophy: A Prospective Study.

Spinal and bulbar muscular atrophy (SBMA) is characterized by slow, progressive bulbar and limb muscle weakness; however, the pattern of progression of muscle fat infiltration remains unclear. We assessed the progression of muscle involvement in 81 patients with SBMA using whole-body muscle magnetic resonance imaging (MRI), alongside clinical and laboratory findings. This prospective study included patients with genetically confirmed SBMA who underwent whole-body muscle MRI. We analyzed muscle fat infiltration and the pattern of involved muscles using cluster analysis, visualizing the sequential progression of fat infiltration. Muscle clusters demonstrated correlation with clinical scales and laboratory findings. Additionally, linear regression analysis was performed to identify the MRI section most strongly associated with 6-minute walk test (6MWT). We included 81 patients with SBMA (age = 54.3 years). After categorizing the patients into 6 clusters based on the pattern of muscle fat infiltration, we observed that muscle involvement began in the posterior calf and progressed to the posterior thigh, pelvis, trunk, anterior thigh, medial thigh, anterior calf, and upper extremity muscles. These muscle clusters correlated significantly with disease duration (τ = 0.47, p < 0.001), 6MWT (τ = -0.49, p < 0.001), and serum creatinine level (τ = -0.46, p < 0.001). The whole-body MRI indicated the thigh as the section most significantly correlated with 6MWT. We used whole-body muscle MRI to determine the sequential progression of the fat infiltration in SBMA. Our findings may enable the identification of objective and reliable imaging outcome measures in the study of the natural history or future clinical trials of SBMA. ANN NEUROL 2024;95:596-606.

SELF-RATINGS OF HEALTH BY AGE AND BIRTH COHORT: FOR WHOM DO THEY PREDICT MORTALITY?

Abstract Global self-rated health as a predictor of mortality has become one of the most reliable outcomes in population-based follow-up studies. However, few studies focus on age or birth cohort differences in the mortality predictiveness of self-ratings of health. Personal knowledge of health accumulates throughout the life course, leading to the hypothesis that self-rated health would be most predictive in old age or in earlier birth cohorts. However, the pronounced effect of self-rated health on mortality means that those with the higher mortality risk of poor self-rated health in earlier adulthood would be less likely to survive to old age, potentially diminishing the effect through selective survival. In this paper we test for potential variations in the effects of age and birth cohort on the association between self-rated health and mortality in a large sample of adults aged 25-101 in the Panel Study of Income Dynamics (N=6437) followed from 1999-2019. We ask if self-rated health -- net of sociodemographic factors, physical and mental health conditions, and health risk behaviors – is equally predictive of mortality in three age groups spanning young, middle, and late adulthood, and in four birth cohorts. Fully adjusted models show that self-rated health was the most predictive for the group aged 41-64, and for those born 1946-1980, and that there were nearly null effects for those aged 25-40 or born 1981-1994. Thus, the effects of self-rated health on mortality vary by age and cohort, in ways that support both knowledge-based and selective survival interpretations of the association.

Ten quick tips for fuzzy logic modeling of biomedical systems.

Fuzzy logic is useful tool to describe and represent biological or medical scenarios, where often states and outcomes are not only completely true or completely false, but rather partially true or partially false. Despite its usefulness and spread, fuzzy logic modeling might easily be done in the wrong way, especially by beginners and unexperienced researchers, who might overlook some important aspects or might make common mistakes. Malpractices and pitfalls, in turn, can lead to wrong or overoptimistic, inflated results, with negative consequences to the biomedical research community trying to comprehend a particular phenomenon, or even to patients suffering from the investigated disease. To avoid common mistakes, we present here a list of quick tips for fuzzy logic modeling any biomedical scenario: some guidelines which should be taken into account by any fuzzy logic practitioner, including experts. We believe our best practices can have a strong impact in the scientific community, allowing researchers who follow them to obtain better, more reliable results and outcomes in biomedical contexts.

A Survey on Lung Cancer Prediction

Cancerous tissue that develops in lung is called a lung cancer. It's the primary reason people die from cancer, everywhere, with smoking being the primary cause of the disease. Lung cancer prediction involves using various methods and technologies is to identify the individuals who are at higher risk of developing the disease. The goal of lung cancer prediction is to detect the disease early, when it is most treatable, or to prevent it from developing altogether. Machine learning may be used to forecast the risk that someone will acquire lung cancer by analysing vast volumes of data to find patterns. In this study, a comparison of various ML-based algorithms for detecting lung cancer has been published. The techniques were used to look for cancer. There has been an explosion in the number of techniques available in recent years for diagnosing lung cancer. The majority of these techniques make use of CT scan pictures, while some make use of x-ray images. In addition, many different classifier strategies are combined with a wide variety of segmentation techniques in order to employ image recognition for the purpose of locating lung cancer nodules. According to the findings of this research, multi-gene genetic programming is superior to other approaches in terms of producing reliable outcomes.

Using Markov Random Field and Analytic Hierarchy Process to Account for Interdependent Criteria

The Analytic Hierarchy Process (AHP) has been a widely used multi-criteria decision-making (MCDM) method since the 1980s because of its simplicity and rationality. However, the conventional AHP assumes criteria independence, which is not always accurate in realistic scenarios where interdependencies between criteria exist. Several methods have been proposed to relax the postulation of the independent criteria in the AHP, e.g., the Analytic Network Process (ANP). However, these methods usually need a number of pairwise comparison matrices (PCMs) and make it hard to apply to a complicated and large-scale problem. This paper presents a groundbreaking approach to address this issue by incorporating discrete Markov Random Fields (MRFs) into the AHP framework. Our method enhances decision making by effectively and sensibly capturing interdependencies among criteria, reflecting actual weights. Moreover, we showcase a numerical example to illustrate the proposed method and compare the results with the conventional AHP and Fuzzy Cognitive Map (FCM). The findings highlight our method’s ability to influence global priority values and the ranking of alternatives when considering interdependencies between criteria. These results suggest that the introduced method provides a flexible and adaptable framework for modeling interdependencies between criteria, ultimately leading to more accurate and reliable decision-making outcomes.

Machine Learning and Image Processing Techniques for Rice Disease Detection: A Critical Analysis

Early rice disease detection is vital in preventing damage to agricultural product output and quantity in the agricultural field. Manual observations of rice diseases are tedious, costly, and time-consuming, especially when classifying disease patterns and color while dealing with non-native diseases. Hence, image processing and Machine Learning (ML) techniques are used to detect rice disease early and within a relatively brief time period. This article aims to demonstrate the performance of different ML algorithms in rice disease detection through image processing. We critically examined different techniques, and the outcomes of previous research have been reviewed to compare the performance of rice disease classifications. Performance has been evaluated based on the criteria of feature extraction, clustering, segmentation, noise reduction, and level of accuracy of disease detection techniques. This paper also showcases various algorithms for different datasets in terms of training methods, image preprocessing with clustering and filtering criteria, and testing with reliable outcomes. Through this review, we provide valuable insights into the current state of ML-based approaches for the early detection of rice diseases, and assist future research and improvement. In addition, we discuss several challenges that must be overcome in order to achieve effective identification of rice diseases.

Management of Patellar Tendinopathy

Patellar tendinopathy is a common pathology typically seen in athletes involved in repetitive explosive jumping and running activities. Also known as jumpers’ knee, it is commonly seen in high-level basketball players. Typically, athletes continue to play with symptoms, which can be aggravated and progress to partial patellar tendon tears. When partial patellar tendon tears occur, prolonged recovery and decreased performance is commonly seen. The pathology and treatment can be frustrating for both the athlete and medical provider. Patellar tendinopathy typically does not involve inflammation but rather microinjury to the tendon fibers, which leads to mucoid degeneration, necrosis, and loss of transitional fibrocartilage. When partial tendon tears do occur, the typical location is posteromedially adjacent to the patella. Treatment involves a stepwise approach starting with nonoperative means, including activity modification, nonsteroidal anti-inflammatories, and physical therapy focused on eccentrics. Extracorporeal shock wave treatments and injections with platelet-rich plasma or bone marrow aspirate concentrate should be considered, with evolving literature to support their use. Ultrasound percutaneous tendon scrapping with a needle supplemented with the aforementioned injections is an emerging treatment option that the authors have found to be helpful, although further studies are required. Surgical intervention is considered after failure of nonoperative treatments, and typically occurs in tears greater than 50% of the tendon thickness and in tendons with increased thickness (>8.8 mm). Open or arthroscopic debridement can be considered, with no studies showing superior outcomes with either technique; however, no high-quality comparison studies exist. The authors prefer an open technique where, much like a bone–patellar tendon–bone harvest, the unhealthy proximal tendon and bone are excised with then closure of the healthy tendon with absorbable sutures. Suture anchor repair may also be used when necessary. In a recent systematic review, surgical management of patellar tendinopathy has been shown to result in improved patient-reported outcomes with return to sport at high levels.1 Treatment for the difficult and sometimes frustrating pathology of patellar tendinopathy continues to evolve, with biologic and less-invasive ultrasound-based treatments showing promise, and surgical intervention providing reliable outcomes.

Strategi Komunikasi Mandorumah dalam Mewujudkan Eksistensi Slogan Kontraktor Anti Molor

The prevalent cases of stalled contractors have drawn attention within the construction industry. However, Mandorumah, a contracting company, has taken a different approach as the "Anti-Delay Contractor." Through qualitative research in the form of a case study, the focus lies on the company's communication strategies concerning quality, timeliness, and efficiency. Through direct observation and in-depth interviews, it was revealed that Mandorumah fortifies its image through social media, implements ongoing evaluations based on customer feedback, and engages actively in two-way communication with clients. As a result, Mandorumah has established a reputation as a reliable contractor. Their communication strategy, emphasizing customer satisfaction, effective utilization of social media, and continuous service evaluations, successfully portrays the "Anti-Delay Contractor" identity, highlighting reliability and trust in delivering projects to high standards and on time. Mandorumah serves as an exemplar in addressing challenges within the construction industry by committing to reliable and trustworthy outcomes

Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning.

Bone segmentation and 3D reconstruction are crucial for total knee arthroplasty (TKA) surgical planning with Personalized Surgical Instruments (PSIs). Traditional semi-automatic approaches are time-consuming and operator-dependent, although they provide reliable outcomes. Moreover, the recent expansion of artificial intelligence (AI) tools towards various medical domains is transforming modern healthcare. Accordingly, this study introduces an automated AI-based pipeline to replace the current operator-based tibia and femur 3D reconstruction procedure enhancing TKA preoperative planning. Leveraging an 822 CT image dataset, a novel patch-based method and an improved segmentation label generation algorithm were coupled to a Combined Edge Loss UNet (CEL-UNet), a novel CNN architecture featuring an additional decoding branch to boost the bone boundary segmentation. Root Mean Squared Errors and Hausdorff distances compared the predicted surfaces to the reference bones showing median and interquartile values of 0.26 (0.19-0.36) mm and 0.24 (0.18-0.32) mm, and of 1.06 (0.73-2.15) mm and 1.43 (0.82-2.86) mm for the tibia and femur, respectively, outperforming previous results of our group, state-of-the-art, and UNet models. A feasibility analysis for a PSI-based surgical plan revealed sub-millimetric distance errors and sub-angular alignment uncertainties in the PSI contact areas and the two cutting planes. Finally, operational environment testing underscored the pipeline's efficiency. More than half of the processed cases complied with the PSI prototyping requirements, reducing the overall time from 35 min to 13.1 s, while the remaining ones underwent a manual refinement step to achieve such PSI requirements, performing the procedure four to eleven times faster than the manufacturer standards. To conclude, this research advocates the need for real-world applicability and optimization of AI solutions in orthopedic surgical practice.

Enhanced edge detection model for low resolution images

The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detection is crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes.