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21 November 2019 | Story Nonsindiso Qwabe | Photo Charl Devenish
Ultrasound read more
Checking out some features of the Samsung ultrasound system are, from the left: SSEM Mthembu Medical's Chase Hutchinson and Jannie Coetzee; Head of Anaesthesiology, Dr Edwin Turton; and Head of Undergraduate Training in Anaesthesiology, Prof Lomby Odendaal.

Medical students in the Faculty of Health Sciences at the UFS will now be able to learn how to perform procedures such as the precise location of a vein for intravenous lines and for diagnostic procedures such as detecting abnormalities in pregnancies, identifying gallstones, and diagnosing trauma-related injuries with ease.  This will be made possible by the placement of a one-of-a-kind ultrasound machine – putting them on par with cutting-edge global medical technology.

A first ever in the medical curriculum of undergraduate students at the UFS

The state-of-the-art, compact HS70A Samsung ultrasound system to the value of R1,4 million was unveiled in the Faculty of Health Sciences’ Clinical Simulation and Skills Unit on 19 November. A first ever in the medical curriculum of undergraduate students at the UFS, it is set to revolutionise the delivery of health-care education in the faculty, said Prof Lomby Odendaal, Teaching and Learning Coordinator for undergraduate anaesthesiology training in the Department of Anaesthesiology.

The ultrasound system was donated by SSEM Mthembu Medical and Samsung Korea.
Prof Odendaal said for the first time in the history of the undergraduate MB ChB curriculum, the ultrasound will be available to medical students from their third year. Students have never had the opportunity to be trained in using ultrasound this early in their careers.

Improved clinical training experience of students

Ultrasound is a diagnostic medical tool that uses sound waves to produce images of internal structures of the body. Prof Odendaal said ultrasound is important to determine pathology and diseases in the body and to provide point-of-care ultrasound. Having the ultrasound in the unit will transform the clinical training experience of students, training them to provide better treatment and medical care, even in constrained environments, to improve patient care.

“There is almost no structure in the body that cannot be examined using ultrasound. It makes the delivery of healthcare more effective. If you make a better diagnosis, the treatment and care will be much better. Ultrasound is so important lately that if you don’t do it, you will be left behind. That’s why we decided to bring this to the students. We can’t miss out on teaching our students about ultrasound, because we want them to be familiar with it by the time they finish their medical degree, so that, even if they go to smaller hospitals, they will be able to spread diagnostic care to the periphery,” Prof Odendaal said.

Streamlined workflow for patient care

“The cutting-edge technology and rich image quality of the ultrasound will deliver top-notch diagnoses to suit the diverse departments within the faculty,” said Chase Hutchinson, National Product Manager at SSEM Mthembu Medical. It comes with various pre-set models to cater for different needs and applications, allowing streamlined workflow for higher efficiency and patient care.

According to Prof Mathys Labuschagne, the Head of the Clinical Simulation and Skills Unit, ultrasound training will improve the quality of doctors graduating in the faculty. “We are really excited about this. You can diagnose many conditions using ultrasound and deliver point-of-care ultrasound; this will become a natural part of students’ training and clinical practice in future.”

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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