SATA CommHealth

Singapore, Singapore

SATA CommHealth

Singapore, Singapore
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Free blood pressure and eye checks will also be given; Highly subsidised diabetic foot screening will be available; Nutrition talks on preventing diabetes will be held for public Singapore, Singapore, November 10, 2016 --( The public will be able to call the hotline to book the screening tests or for a seat at the nutrition talk. This is also available to anyone who meets the medical condition and would like to have the digital retina photography test conducted to screen for diabetic retinopathy. Diabetics can also opt to undergo diabetic foot screening at a highly subsidised price of $5. According to a new report by the International Diabetes Federation (IDF), it revealed that Singapore has the second-highest proportion of diabetics among developed nations. It said 10.53 percent of people in Singapore aged between 20 and 79 are estimated to have the chronic disease, after correcting for age differences between the countries. Only the United States fared worse, with a percentage of 10.75. This represents an increase from 4.7 percent in 1984 and 9 per cent in 2004. The Ministry of Health has declared war on diabetes in April this year and has outlined three steps. The steps outlined include promoting a healthy lifestyle and reduce obesity rates in order to cut down on new diabetes cases; strengthen early screening and intervention to identify the disease early among those at risk or undiagnosed and thirdly, support better disease control to slow disease progression, and reduce complications. Five in six people who have diabetic retinopathy (DR), or damage to the blood vessels in the eye, are unaware that they have the condition based on a study published in May 2015 by the British Journal of Ophthalmology. DR is the top cause of blindness among working age adults in Singapore, causing an estimated 600 people to be totally blind and over 8,000 to lose their sight in one eye. With Singapore having an estimated 600,000 diabetics aged 18 to 69, this means about 180,000 have the eye condition and only 30,000 have been diagnosed. (Source: Singapore Eye Research Institute) Dr K Thomas Abraham, Chief Executive Officer, SATA CommHealth on the initiative, “We support World Diabetes Day due to the high incidence of diabetics and that many Singaporeans who may be unaware that they may be diabetic or pre-diabetic. It is in SATA CommHealth’s interest to help to detect and prevent diabetes from the onset of this chronic disease as it is part of our mission of promoting lifelong health and serving the community. Moreover, as our government is going all out to combat this chronic disease, we want to create awareness of this debilitating condition.” On site at its medical centre at Bedok and Jurong East Community Health Centre, nutrition talks will also be conducted by our resident dietician who will address common questions on food & diabetes, on the common notions of limiting certain foods such as sugar to prevent diabetes. There will be sharing of simple, practical dietary & lifestyle tips at the talks. Diabetes Health Checks schedule Date : 14 - 16 November 2016 Venue: SATA CommHealth Jurong Medical Centre Date : 17 - 18 & 21 November 2016 Venue: SATA CommHealth Woodlands Medical Centre Date : 22 - 24 November 2016 Venue: SATA CommHealth Uttamram Medical Centre Date : 25 , 28 - 29 November 2016 Venue: SATA CommHealth Ang Mo Kio Medical Centre Date : 30 November 2016 Venue: SATA CommHealth Tanjong Pagar Clinic Nutrition Talks Date: 15 November 2016 Time: 12.30 – 1.30pm Venue: Jurong East Community Health Centre Date: 22 November 2016 Time: 12.30 - 1.30pm Venue: SATA CommHealth Uttamram Medical Centre Singapore, Singapore, November 10, 2016 --( PR.com )-- In support of World Diabetes Day 2016 (WDD), SATA CommHealth, a social enterprise cum voluntary welfare organisation will offer free point-of-care blood glucose, blood pressure and digital retinal photography screenings at its network of five medical centres from 14 November till the end of the month. The theme for WDD 2016 is "Eyes on Diabetes." Diabetes is a leading cause of blindness globally. Early detection and timely treatment of diabetic retinopathy can prevent vision loss and reduce the impact of diabetes on individuals, their caregivers and society. This initiative to support World Diabetes Day is in line with SATA CommHealth’s mission of promoting lifelong health and serving the community.The public will be able to call the hotline to book the screening tests or for a seat at the nutrition talk. This is also available to anyone who meets the medical condition and would like to have the digital retina photography test conducted to screen for diabetic retinopathy. Diabetics can also opt to undergo diabetic foot screening at a highly subsidised price of $5.According to a new report by the International Diabetes Federation (IDF), it revealed that Singapore has the second-highest proportion of diabetics among developed nations. It said 10.53 percent of people in Singapore aged between 20 and 79 are estimated to have the chronic disease, after correcting for age differences between the countries. Only the United States fared worse, with a percentage of 10.75. This represents an increase from 4.7 percent in 1984 and 9 per cent in 2004.The Ministry of Health has declared war on diabetes in April this year and has outlined three steps. The steps outlined include promoting a healthy lifestyle and reduce obesity rates in order to cut down on new diabetes cases; strengthen early screening and intervention to identify the disease early among those at risk or undiagnosed and thirdly, support better disease control to slow disease progression, and reduce complications.Five in six people who have diabetic retinopathy (DR), or damage to the blood vessels in the eye, are unaware that they have the condition based on a study published in May 2015 by the British Journal of Ophthalmology. DR is the top cause of blindness among working age adults in Singapore, causing an estimated 600 people to be totally blind and over 8,000 to lose their sight in one eye. With Singapore having an estimated 600,000 diabetics aged 18 to 69, this means about 180,000 have the eye condition and only 30,000 have been diagnosed. (Source: Singapore Eye Research Institute)Dr K Thomas Abraham, Chief Executive Officer, SATA CommHealth on the initiative, “We support World Diabetes Day due to the high incidence of diabetics and that many Singaporeans who may be unaware that they may be diabetic or pre-diabetic. It is in SATA CommHealth’s interest to help to detect and prevent diabetes from the onset of this chronic disease as it is part of our mission of promoting lifelong health and serving the community. Moreover, as our government is going all out to combat this chronic disease, we want to create awareness of this debilitating condition.”On site at its medical centre at Bedok and Jurong East Community Health Centre, nutrition talks will also be conducted by our resident dietician who will address common questions on food & diabetes, on the common notions of limiting certain foods such as sugar to prevent diabetes. There will be sharing of simple, practical dietary & lifestyle tips at the talks.Diabetes Health Checks scheduleDate : 14 - 16 November 2016Venue: SATA CommHealth Jurong Medical CentreDate : 17 - 18 & 21 November 2016Venue: SATA CommHealth Woodlands Medical CentreDate : 22 - 24 November 2016Venue: SATA CommHealth Uttamram Medical CentreDate : 25 , 28 - 29 November 2016Venue: SATA CommHealth Ang Mo Kio Medical CentreDate : 30 November 2016Venue: SATA CommHealth Tanjong Pagar ClinicNutrition TalksDate: 15 November 2016Time: 12.30 – 1.30pmVenue: Jurong East Community Health CentreDate: 22 November 2016Time: 12.30 - 1.30pmVenue: SATA CommHealth Uttamram Medical Centre Click here to view the list of recent Press Releases from SATA CommHealth


Ganesan K.,Ngee Ann Polytechnic | Acharya R.U.,Ngee Ann Polytechnic | Acharya R.U.,University of Malaya | Chua C.K.,Ngee Ann Polytechnic | And 3 more authors.
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine | Year: 2013

Mammograms are by far one of the most preferred methods of screening for breast cancer. Early detection of breast cancer can improve survival rates to a greater extent. Although the analysis and diagnosis of breast cancer are done by experienced radiologists, there is always the possibility of human error. Interobserver and intraobserver errors occur frequently in the analysis of medical images, given the high variability between every patient. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This article presents a classification pipeline to improve the accuracy of differentiation between normal, benign, and malignant mammograms. Several features based on higher-order spectra, local binary pattern, Laws' texture energy, and discrete wavelet transform were extracted from mammograms. Feature selection techniques based on sequential forward, backward, plus-l-takeaway-r, individual, and branch-and-bound selections using the Mahalanobis distance criterion were used to rank the features and find classification accuracies for combination of several features based on the ranking. Six classifiers were used, namely, decision tree classifier, fisher classifier, linear discriminant classifier, nearest mean classifier, Parzen classifier, and support vector machine classifier. We evaluated our proposed methodology with 300 mammograms obtained from the Digital Database for Screening Mammography and 300 mammograms from the Singapore Anti-Tuberculosis Association CommHealth database. Sensitivity, specificity, and accuracy values were used to compare the performances of the classifiers. Our results show that the decision tree classifier demonstrated an excellent performance compared to other classifiers with classification accuracy, sensitivity, and specificity of 91% for the Digital Database for Screening Mammography database and 96.8% for the Singapore Anti-Tuberculosis Association CommHealth database. © IMechE 2013.


Ganesan K.,Ngee Ann Polytechnic | Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,University of Malaya | Chua C.K.,Ngee Ann Polytechnic | And 2 more authors.
IEEE Transactions on Instrumentation and Measurement | Year: 2014

Mammography is one of the first diagnostic tests to prescreen breast cancer. Early detection of breast cancer has been known to improve recovery rates to a great extent. In most medical centers, experienced radiologists are given the responsibility of analyzing mammograms. But, there is always a possibility of human error. Errors can frequently occur as a result of fatigue of the observer, resulting in interobserver and intraobserver variations. The sensitivity of mammographic screening also varies with image quality. To offset different kinds of variability and to standardize diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This paper presents a one-class classification pipeline for the classification of breast cancer images into benign and malignant classes. Because of the sparse distribution of abnormal mammograms, the two-class classification problem is reduced to a one-class outlier identification problem. Trace transform, which is a generalization of the Radon transform, has been used to extract the features. Several new functionals specific to mammographic image analysis have been developed and implemented to yield clinically significant features. Classifiers such as the linear discriminant classifier, quadratic discriminant classifier, nearest mean classifier, support vector machine, and the Gaussian mixture model (GMM) were used. For automated diagnosis, the classification pipeline was tested on a set of 313 mammograms provided by the Singapore Anti-Tuberculosis Association CommHealth. A maximum accuracy rate of 92.48% has been obtained using GMMs. © 2013 IEEE.


Tan J.H.,Ngee Ann Polytechnic | Acharya U.R.,Ngee Ann Polytechnic | Tan C.,SATA CommHealth | Abraham K.T.,SATA CommHealth | Lim C.M.,Ngee Ann Polytechnic
Journal of Medical Systems | Year: 2012

Textural properties of normal and tuberculosis posterior-anterior chest radiographs were looked into in this investigation. The proposed computerized scheme segmented the lung field of interest using a user-guided snake algorithm and extracted the corresponding pixel data. For both normal and tuberculosis radiographs, the grayscale intensity distribution within the region of interest was analyzed to study their respective characteristics, and fed to classifiers for automated classification. Statistically the tuberculosis infected radiographs manifested a higher variance, third moment, entropy and a lower mean value in their intensity distributions, compared to their normal peers. The greater disparities between a particular radiograph and the confidence interval determined by our normal groups on some of the features were observed to be related to the level of haziness at the upper lobe. Lastly, the C4.5 (a decision tree based classifier)-adaboost achieved an accuracy of 94.9% in normal-tuberculosis classification. An integrated index, called tuberculosis index (TI), is proposed based on texture features to discriminate normal and tuberculosis chest radiographs using just one index or number. We hope this TI can be used as an adjunct tool by the radiographers in their daily screening. © 2011 Springer Science+Business Media, LLC.

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