JOHANNESBURG, South Africa – 26 November, 2018 – Samsung, a leader in medical imaging technology, is showcasing its latest Ultrasound, Digital Radiography, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) concept and their software innovations at the Radiological Society of North America (RSNA) 2018 Annual Meeting at McCormick Place in Chicago.

“We are pleased that Samsung’s AI technologies have been successfully applied to the existing diagnostic imaging devices and have been well received in the market,” said Cambridge Mokanyane, Chief Marketing Officer at Samsung South Africa. “As a comprehensive diagnostic imaging solution provider, Samsung Medison will continue to strengthen their technologies through collaboration with hospitals and healthcare professionals. We aim to bring together radiologists and AI to fill in the gaps for improved healthcare management.”

Samsung showcases several types of AI based diagnostic imaging software:

• Ultrasound System – S-Detect for Breast is AI based software which analyses breast lesions using ultrasound images and has been implemented into Samsung’s ultrasound systems dedicated to Radiology. It assists in standardising reports and classification of suspicious breast lesions by incorporating BIRADS® ATLAS* (Breast Imaging-Reporting and Data System, Atlas)

A published study^[1] by radiology professor Tommaso Bartolotta from the University of Palermo in Italy, S-Detect for Breast has shown to assist various degrees of improvement in the overall diagnosis of breast lesions. When using S-Detect for Breast, the diagnostic accuracy for doctors with experience of four years or less improved from 0.83 to 0.87 (AUC, Area Under Curve). S-Detect for Breast could be a useful tool for supporting and guiding breast diagnosis among non-expert breast imaging physicians (Only shape and orientation descriptors are automatically classified in the United States).

• Digital Radiography – By using AI technology, the ‘Bone Suppression’ function, which reduces the bone signal from the chest x-ray image, clearly brings out the lung tissues obscured by the bones. ‘SimGrid’ has also been introduced as a solution to ease the workflow to replace grids, while providing excellent image quality with reduced scatter artefacts. According to a recently published study^[2], ‘SimGrid’ provided image quality almost as good as the images taken with a physical grid.

The Auto Lung Nodule Detection (ALND) is a CAD (Computer Aided Detection) solution based on AI technology used in the detection of lung nodules and is 510(k) pending. In a study^[3] in press in Journal of Thoracic Imaging, ALND improved the detection sensitivity of lung cancer nodules 3 cm or smaller in chest radiographs by seven percentage points to 92 percent, compared with the average of six chest radiologists.

• Computed Tomography – Samsung has introduced the intra-cranial haemorrhage package that combines a mobile stroke unit with a radiological computer aided triage and notification solution based on AI technology.
• Magnetic Resonance Imaging – Utilising AI technology, Samsung is developing a technology to display information such as knee cartilage thickness as well as images of knee arthritis patients. The software is a prototype and not for sale in any region of the world.

At the Samsung booth, attendees will find a separate ‘AI zone’ that allows visitors to experience Samsung’s AI technology more easily. Samsung will also host a symposium that will demonstrate how its image processing technology improves diagnostic accuracy and promote radiation safety for paediatric patients.

Through the release of new diagnostic tools that utilise diverse data based on AI technology, Samsung will continue to support healthcare professionals to improve the diagnostic accuracy.  For more information on Samsung’s healthcare business, and products, please visit https://www.samsunghealthcare.com.

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^{[1] Tommaso Vincenzo Bartolotta, et al. Focal breast lesion characterisation according to the BI‑RADS US lexicon: role of a computer‑aided decision‑making support. La radiologia medica. 2018 Mar 22}

^{[2] S. Y. Ahn, et al. The Potential Role of Grid-Like Software in Bedside Chest Radiography in Improving Image Quality and Dose Reduction: An Observer Preference Study. Korean J Radiol, 2018; 19(3):526-533}

^{[3] M.J. Cha, et al. Performance of Deep Learning Model in Detecting Operable Lung Cancer with Chest Radiographs. J Thorac Imaging, 2018 In press}