Mohammad Ali Maraci
St Hilda's College
CDT Postgraduate Studentship in Healthcare Innovation (RCUK Digital Economy Programme grant number EP/G036861/1)
Engineering ultra sound image analysis solutions for resource-constrained environments
Ultrasound (US) has been shown to be a safe and effective imaging modality in detecting pregnancy complications such as breech presentation. The non-invasiveness of this technique, alongside its cost efficacy and availability have promoted its uptake in the developed world for routine pregnancy scans and examinations. However the use of US is far less common in low income countries, particularly in rural areas, as there is a lack of training for effective use of this technology and accurate interpretation of the images as well as a relatively high cost being associated with the current US devices.
Recent technological advancements in the field of US have led to lower-cost and portable US devices, facilitating the use of US in the developing world. In light of the factors that can affect the quality of image interpretation, we have implemented a machine learning technique to segment the fetal head from images obtained from a low-cost USB US device so as to further facilitate the effective implementation of this device in developing countries. Fetal images from a high-end US machine were also obtained as a comparison to the USB US device. The results presented here illustrate that the algorithm works successfully on images obtained from both devices and that statistically there is no significant difference between the performance of the algorithm on the two.