Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
25 January 2024 | Story Leonie Bolleurs | Photo Sonia Small
Prof Corinna Walsh
Prof Corinna Walsh says the PEA POD Infant Body Composition System works by directly measuring an infant’s body weight and volume, and then uses these measurements to calculate the body fat percentage, fat mass, and fat-free mass.

Nutritional and growth patterns during early life have been associated with health, development, and well-being throughout the life cycle. It is also associated with risks for developing obesity and non-communicable diseases, such as cardiometabolic diseases, later in life. These are the findings of Prof Corinna Walsh, Professor in the Department of Nutrition and Dietetics.

Maternal and child health

”In line with national priorities, a strong research focus area of the Faculty of Health Sciences and the School of Health and Rehabilitation Sciences is maternal and child health,” she says. She goes on to mention that the Department of Nutrition and Dietetics has established a reputable research programme. This programme focuses primarily on the nutritional status of pregnant women and how the early environment to which they are exposed during and after pregnancy affects short- and long-term health outcomes of the offspring.

“In our previous work, the assessment of birth outcomes of infants was, however, limited by the lack of equipment to analyse body composition. The research that we can conduct with the PEA POD® provides us with immense additional potential,” remarks Prof Walsh.

She explains, “The PEA POD Infant Body Composition System is an infant-sized air displacement plethysmography system. It works by directly measuring an infant’s body weight and volume, and then uses these measurements to calculate the body fat percentage, fat mass, and fat-free mass.

According to her, the assessment of body volume takes two minutes. “The PEA POD technique also does not require collection of any fluids and does not expose the infant to radiation. It can be performed as often as required without any risks and be used up to a maximum of 8-10 kg body weight, from birth to about eight months,” she says.

Advanced technology

In the context of research on infant body weight and composition, there is a need for accurate measurement techniques that can differentiate between fat mass and fat-free mass. Prof Walsh is of the opinion that traditional measures such as body mass index (BMI) and weight for length have limitations in this regard, as they do not provide a clear distinction between these components. Furthermore, BMI may not be reliable for assessing adiposity or obesity in paediatric populations, and it can vary significantly with age and gender.

Addressing these challenges, the PEA POD equipment offers advanced technology that allows for highly accurate quantification of infant body composition. This technological capability opens up opportunities to study the effects of early-life nutrition on growth and the developmental mechanisms that may lead to later comorbidities. So, when it comes to researching infant body weight and composition, the PEA POD equipment plays a crucial role in providing precise data and insights.

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.

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept