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Benchmarking Diet Quality to Assess Nutritional Risk in Hemodialysis Patients: Applying Adequacy and Moderation Metrics of the Hemodialysis-Healthy Eating Index

Sualeheen A, Khor BH, Balasubramanian GV, Sahathevan S, Chinna K, Mat Daud ZA, et al.

J Ren Nutr, 2022 Nov;32(6):726-738.
PMID: 35182714 DOI: 10.1053/j.jrn.2022.02.002

OBJECTIVES: This study modified Healthy Eating Index (HEI) based on hemodialysis (HD)-specific nutritional guidelines and investigated associations between the diet quality (DQ) and nutritional risk in HD patients.
METHODS: The HD-HEI tool adapted the Malaysian Dietary Guidelines 2010 framework according to HD-specific nutrition guidelines. This HD-HEI was applied to 3-day dietary records of 382 HD patients. Relationships between HD-HEI scores and nutritional parameters were tested by partial correlations. Binary logistic regression models adjusted with confounders were used to determine adjusted odds ratio (adjOR) with 95% confidence interval (CI) for nutritional risk based on HD-HEI scores categorization.
RESULTS: The total HD-HEI score (51.3 ± 10.2) for this HD patient population was affected by ethnicity (Ptrend < .001) and sex (P = .003). No patient achieved "good" DQ (score: 81-100), while DQ of 54.5% patients were classified as "needs improvement" (score: 51-80) and remaining as "poor" (score: 0-51). Total HD-HEI scores were positively associated with dietary energy intake (DEI), dietary protein intake (DPI), dry weight, and handgrip strength, but inversely associated with Dietary Monotony Index (DMI) (all P

Matched MeSH terms: Benchmarking
Fulltext Measuring the potential for sustainable intensification of aquaculture in Bangladesh using life cycle assessment

Henriksson PJG, Belton B, Jahan KM, Rico A

Proc Natl Acad Sci U S A, 2018 03 20;115(12):2958-2963.
PMID: 29507224 DOI: 10.1073/pnas.1716530115

Food production is a major driver of global environmental change and the overshoot of planetary sustainability boundaries. Greater affluence in developing nations and human population growth are also increasing demand for all foods, and for animal proteins in particular. Consequently, a growing body of literature calls for the sustainable intensification of food production, broadly defined as "producing more using less". Most assessments of the potential for sustainable intensification rely on only one or two indicators, meaning that ecological trade-offs among impact categories that occur as production intensifies may remain unaccounted for. The present study addresses this limitation using life cycle assessment (LCA) to quantify six local and global environmental consequences of intensifying aquaculture production in Bangladesh. Production data are from a unique survey of 2,678 farms, and results show multidirectional associations between the intensification of aquaculture production and its environmental impacts. Intensification (measured in material and economic output per unit primary area farmed) is positively correlated with acidification, eutrophication, and ecotoxicological impacts in aquatic ecosystems; negatively correlated with freshwater consumption; and indifferent with regard to global warming and land occupation. As production intensifies, the geographical locations of greenhouse gas (GHG) emissions, acidifying emissions, freshwater consumption, and land occupation shift from the immediate vicinity of the farm to more geographically dispersed telecoupled locations across the globe. Simple changes in fish farming technology and management practices that could help make the global transition to more intensive forms of aquaculture be more sustainable are identified.

Matched MeSH terms: Benchmarking
Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Page DB, Broeckx G, Jahangir CA, Verbandt S, Gupta RR, Thagaard J, et al.

J Pathol, 2023 Aug;260(5):514-532.
PMID: 37608771 DOI: 10.1002/path.6165

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Matched MeSH terms: Benchmarking
Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms

Tanimu B, Hamed MM, Bello AD, Abdullahi SA, Ajibike MA, Shahid S

Environ Sci Pollut Res Int, 2024 Feb;31(10):15986-16010.
PMID: 38308777 DOI: 10.1007/s11356-024-32128-0

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.

Matched MeSH terms: Benchmarking
Fulltext Global benchmarking of children's exposure to television advertising of unhealthy foods and beverages across 22 countries

Kelly B, Vandevijvere S, Ng S, Adams J, Allemandi L, Bahena-Espina L, et al.

Obes Rev, 2019 Nov;20 Suppl 2(Suppl 2):116-128.
PMID: 30977265 DOI: 10.1111/obr.12840

Restricting children's exposures to marketing of unhealthy foods and beverages is a global obesity prevention priority. Monitoring marketing exposures supports informed policymaking. This study presents a global overview of children's television advertising exposure to healthy and unhealthy products. Twenty-two countries contributed data, captured between 2008 and 2017. Advertisements were coded for the nature of foods and beverages, using the 2015 World Health Organization (WHO) Europe Nutrient Profile Model (should be permitted/not-permitted to be advertised). Peak viewing times were defined as the top five hour timeslots for children. On average, there were four times more advertisements for foods/beverages that should not be permitted than for permitted foods/beverages. The frequency of food/beverages advertisements that should not be permitted per hour was higher during peak viewing times compared with other times (P

Matched MeSH terms: Benchmarking
Fulltext A generative adversarial network for synthetization of regions of interest based on digital mammograms

Oyelade ON, Ezugwu AE, Almutairi MS, Saha AK, Abualigah L, Chiroma H

Sci Rep, 2022 Apr 13;12(1):6166.
PMID: 35418566 DOI: 10.1038/s41598-022-09929-9

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.

Matched MeSH terms: Benchmarking
Fulltext Benchmarking the transparency, comprehensiveness and specificity of population nutrition commitments of major food companies in Malaysia

Ng S, Sacks G, Kelly B, Yeatman H, Robinson E, Swinburn B, et al.

Global Health, 2020 04 17;16(1):35.
PMID: 32303243 DOI: 10.1186/s12992-020-00560-9

BACKGROUND: The aim of this study was to assess the commitments of food companies in Malaysia to improving population nutrition using the Business Impact Assessment on population nutrition and obesity (BIA-Obesity) tool and process, and proposing recommendations for industry action in line with government priorities and international norms.
METHODS: BIA-Obesity good practice indicators for food industry commitments across a range of domains (n = 6) were adapted to the Malaysian context. Euromonitor market share data was used to identify major food and non-alcoholic beverage manufacturers (n = 22), quick service restaurants (5), and retailers (6) for inclusion in the assessment. Evidence of commitments, including from national and international entities, were compiled from publicly available information for each company published between 2014 and 2017. Companies were invited to review their gathered evidence and provide further information wherever available. A qualified Expert Panel (≥5 members for each domain) assessed commitments and disclosures collected against the BIA-Obesity scoring criteria. Weighted scores across domains were added and the derived percentage was used to rank companies. A Review Panel, comprising of the Expert Panel and additional government officials (n = 13), then formulated recommendations.
RESULTS: Of the 33 selected companies, 6 participating companies agreed to provide more information. The median overall BIA-Obesity score was 11% across food industry sectors with only 8/33 companies achieving a score of > 25%. Participating (p

Matched MeSH terms: Benchmarking/methods*
Fulltext A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

Shirkhorshidi AS, Aghabozorgi S, Wah TY

PLoS One, 2015;10(12):e0144059.
PMID: 26658987 DOI: 10.1371/journal.pone.0144059

Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.

Matched MeSH terms: Benchmarking
Adaptive evolutionary artificial neural networks for pattern classification

Oong TH, Isa NA

IEEE Trans Neural Netw, 2011 Nov;22(11):1823-36.
PMID: 21968733 DOI: 10.1109/TNN.2011.2169426

This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.

Matched MeSH terms: Benchmarking
Fulltext Improving the effectiveness of service delivery in the public healthcare sector: the case of ophthalmology services in Malaysia

Foo CY, Lim KK, Sivasampu S, Dahian KB, Goh PP

BMC Health Serv Res, 2015;15:349.
PMID: 26315283 DOI: 10.1186/s12913-015-1011-0

Rising demand of ophthalmology care is increasingly straining Malaysia's public healthcare sector due to its limited human and financial resources. Improving the effectiveness of ophthalmology service delivery can promote national policy goals of population health improvement and system sustainability. This study examined the performance variation of public ophthalmology service in Malaysia, estimated the potential output gain and investigated several factors that might explain the differential performance.

Matched MeSH terms: Benchmarking
Fulltext Brain and spinal tumour

Goh CH, Lu YY, Lau BL, Oy J, Lee HK, Liew D, et al.

Med J Malaysia, 2014 Dec;69(6):261-7.
PMID: 25934956 MyJurnal

This study reviewed the epidemiology of brain and spinal tumours in Sarawak from January 2009 till December 2012. The crude incidence of brain tumour in Sarawak was 4.6 per 100,000 population/year with cumulative rate 0.5%. Meningioma was the most common brain tumour (32.3%) and followed by astrocytoma (19.4%). Only brain metastases showed a rising trend and cases were doubled in 4 years. This accounted for 15.4% and lung carcinoma was the commonest primary. Others tumour load were consistent. Primitive neuroectodermal tumour (PNET) and astrocytoma were common in paediatrics (60%). We encountered more primary spinal tumour rather than spinal metastases. Intradural schwannoma was the commonest and frequently located at thoracic level. The current healthcare system in Sarawak enables a more consolidate data collection to reflect accurate brain tumours incidence. This advantage allows subsequent future survival outcome research and benchmarking for healthcare resource planning.

Matched MeSH terms: Benchmarking
Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model

Dikshit A, Pradhan B, Alamri AM

Sci Total Environ, 2021 Feb 10;755(Pt 2):142638.
PMID: 33049536 DOI: 10.1016/j.scitotenv.2020.142638

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.

Matched MeSH terms: Benchmarking
Fulltext Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset

M. Hafiz Fazren Abd Rahman, Wan Wardatul Amani Wan Salim, M. Firdaus Abd-Wahab

Biological and Natural Resources Engineering Journal, 2020;3(1):48-61.
MyJurnal

The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into meaningful deductions. Several machine learning tools have shown great promise in diabetes classification. However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. Moreover, real-world data are likely to be complex, incomplete and unorganized, thus, convoluting efforts to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using three different machine learning algorithms; Decision Tree, Support Vector Machine and Naïve Bayes. Data pre-processing methods are utilised to the raw data to improve model performance. This study uses datasets obtained from the IIUM Medical Centre for classification and modelling. Ultimately, the performance of each model is validated, evaluated and compared based on several statistical metrics that measures accuracy, precision, sensitivity and efficiency. This study shows that the random forest model provides the best overall prediction performance in terms of accuracy (0.87), sensitivity (0.9), specificity (0.8), precision (0.9), F1-score (0.9) and AUC value (0.93) (Normal).

Matched MeSH terms: Benchmarking
ADA-SR: Activity detection and analysis using security robots for reliable workplace safety

Zhang G, Jing W, Tao H, Rahman MA, Salih SQ, Al-Saffar A, et al.

Work, 2021;68(3):935-943.
PMID: 33612535 DOI: 10.3233/WOR-203427

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease.
OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

Matched MeSH terms: Benchmarking
Fulltext Lean public emergency department efficiency evaluation by Slack-based Measure Data Envelopment Analysis

Tiong Ngee-Wen, Suhaiza Zailani, Azmin Azliza Aziz, Rashidi Ahmad

Malaysian Journal of Medicine and Health Sciences, 2020;16(2):105-111.
MyJurnal

Introduction: Lean healthcare outcome is usually measured with multiple key performance metrics but there is a lack of tools that enabled efficiency assessment. This research aimed to assess the efficiency among lean public emergen- cy departments (ED) through Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and evaluate the impact of lean on the efficiency in public emergency departments. Methods: A retrospective observational study design using data on the number of support staff, number of doctors, number of discharge, arrival to consultant and length of stay. Efficiency scores of 20 Malaysian public EDs were computed using SBM-DEA modelling and compared be- tween before and after lean implementation. Results: A total of 13 out of 20 public EDs exhibited improvement in ar- rival to consultant and length of stay upon lean implementation. However, only 9 out of the 13 public EDs have had an improvement in efficiency score. Conclusion: Lean healthcare demonstrated a positive impact on the efficiency level of some public EDs. The SBM-DEA model offers the benchmarking capability and slack elimination information that may complement the lean continuous improvement philosophy.

Matched MeSH terms: Benchmarking
Fulltext Implications of Web of Science journal impact factor for scientific output evaluation in 16 institutions and investigators' opinion

Wáng YX, Arora R, Choi Y, Chung HW, Egorov VI, Frahm J, et al.

Quant Imaging Med Surg, 2014 Dec;4(6):453-61.
PMID: 25525577 DOI: 10.3978/j.issn.2223-4292.2014.11.16

Journal based metrics is known not to be ideal for the measurement of the quality of individual researcher's scientific output. In the current report 16 contributors from Hong Kong SAR, India, Korea, Taiwan, Russia, Germany, Japan, Turkey, Belgium, France, Italy, UK, The Netherlands, Malaysia, and USA are invited. The following six questions were asked: (I) is Web of Sciences journal impact factor (IF) and Institute for Scientific Information (ISI) citation the main academic output performance evaluation tool in your institution? and your country? (II) How does Google citation count in your institution? and your country? (III) If paper is published in a non-SCI journal but it is included in PubMed and searchable by Google scholar, how it is valued when compared with a paper published in a journal with an IF? (IV) Do you value to publish a piece of your work in a non-SCI journal as much as a paper published in a journal with an IF? (V) What is your personal view on the metric measurement of scientific output? (VI) Overall, do you think Web of Sciences journal IF is beneficial, or actually it is doing more harm? The results show that IF and ISI citation is heavily affecting the academic life in most of the institutions. Google citation and evaluation, while is being used and convenient and speedy, has not gain wide 'official' recognition as a tool for scientific output evaluation.

Matched MeSH terms: Benchmarking
Fulltext An overview of fractional anisotropy as a reliable quantitative measurement for the corticospinal tract (CST) integrity in correlation with a Fugl-Meyer assessment in stroke rehabilitation

Zolkefley MKI, Firwana YMS, Hatta HZM, Rowbin C, Nassir CMNCM, Hanafi MH, et al.

J Phys Ther Sci, 2021 Jan;33(1):75-83.
PMID: 33519079 DOI: 10.1589/jpts.33.75

[Purpose] Understanding the essential mechanisms in post-stroke recovery not only provides important basic insights into brain function and plasticity but can also guide the development of new therapeutic approaches for stroke patients. This review aims to give an overview of how various variables of Magnetic Resonance-Diffusion Tensor Imaging (MR-DTI) metrics of fractional anisotropy (FA) can be used as a reliable quantitative measurement and indicator of corticospinal tract (CST) changes, particularly in relation to functional motor outcome correlation with a Fugl-Meyer assessment in stroke rehabilitation. [Methods] PubMed electronic database was searched for the relevant literature, using key words of diffusion tensor imaging (dti), corticospinal tract, and stroke. [Results] We reviewed the role of FA in monitoring CST remodeling and its role of predicting motor recovery after stroke. We also discussed the mechanism of CST remodeling and its modulation from the value of FA and FMA-UE. [Conclusion] Heterogeneity of post-stroke brain disorganization and motor impairment is a recognized challenge in the development of accurate indicators of CST integrity. DTI-based FA measurements offer a reliable and evidence-based indicator for CST integrity that would aid in predicting motor recovery within the context of stroke rehabilitation.

Matched MeSH terms: Benchmarking
Fulltext TOURIST PERCEPTION TOWARDS RECREATION CENTRE IN MALAYSIA

NURUL SYUHADA SHARIFF, YUSNITA YUSOF, NOOR ZATUL IFFAH HUSSIN

UMT Journal of Undergraduate Research, 2020;2(3):103-114.
MyJurnal

Recreation centre become one of the centres for a family to bring their children for recreation and leisure activity. Moreover, the recreation centre is the place for education, research, and awareness to the public. The main objective of this study is to investigate factors that relate to tourist perception in their reference to their interest, expectations, satisfaction, and a general understanding of the recreation centre. The antecedent factors are awareness of the surrounding environment, visitor experiences, and destination image. This research using a quantitative method via a survey questionnaire and a domestic tourist as a sample. A sample is consist of 384 respondent of domestic tourists who visited the recreation centre in Malaysia. This survey has been done in Zoo Negara, Aquaria KLCC, and FRIM, Kepong. The results show the majority of respondents are female, age below 26 years old, single, obtained higher education, working, and had an income below RM1000. The respondents are mostly from Selangor and their purpose of visit to the recreation centre is for leisure and recreation. The major source of information to visit the recreation centre was from the internet. There were have a significant relationship between an antecedent factor with tourist perception towards the recreation centre in Malaysia. The result of this study will help marketers and management of recreation centres to understand the perceptions of their future visitors. Based on the study, it is should be used as an initial benchmark for the future study, however, they may execute a depth analysis on the tourism that related to the recreation centre in Malaysia.

Matched MeSH terms: Benchmarking
Fulltext Performance of dental undergraduate students using international caries detection and assessment system (icdas)

Sanus,M,A,, Nordin,M,A,, Rusli,M,R,, Mohamed,Z,N,h,

Compendium of Oral Science, 2020;7(1):13-19.
MyJurnal

Abstract
Objectives: This study aimed to assess intra- and inter-examiner reliability of International Caries Detection
and Assessment System (ICDAS) and modified epidemiology ICDAS (MOD) code by undergraduate dental
students with different clinical experiences.
Methods: A total of 150 dental undergraduate students with varying clinical experiences (0, 1 and 2 years of
clinical experience) were recruited. Participants received training through a theoretical lecture on ICDAS criteria
by an experienced National Benchmark Group (NBG) examiner and underwent e-learning program prior to
ICDAS calibration. Visual examination on extracted permanent teeth (N= 45) with different location and stages
of caries progression ranging from ICDAS scores 0 to 6, was performed using the ICDAS criteria. The
assessments were repeated after one hour. The data were analysed to evaluate inter-examiner and
intra-examiner reliability in the form of kappa scores using SPSS 23 Software.
Results: Mean kappa values for intra- and inter-examiner reliability for ICDAS code, were between 0.41 to
0.60, and 0.61 to 0.80 respectively. For MOD code, mean kappa values for intra- and inter-examiner reliability
were between 0.61 to 0.80. Good intra-examiner agreement (>0.61) was observed in both ICDAS and MOD
code for all groups.
Conclusion: All students performed similar agreement, therefore, clinical experience within 2 years does not
influence the performance of visual inspection in detecting caries using ICDAS. The results of the study shows
that ICDAS and modified epidemiology ICDAS codes has good reproducibility and is feasible to be used as a
tool in clinical practice as well as patient education.

Matched MeSH terms: Benchmarking
Fulltext Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MIB, Ramli ARB

EXCLI J, 2017;16:113-137.
PMID: 28435432 DOI: 10.17179/excli2016-701

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.

Matched MeSH terms: Benchmarking

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