Enabling early and accurate diagnosis of disease is imperative to improve treatments’ success rates, and at UCL we conduct research in two main areas: medical imaging, and in vitro diagnostics.
Advancements in medical imaging assist clinicians in gathering information non-invasively, allowing them to diagnose without the need for surgical intervention and with ever increasing accuracy. In vitro diagnostics are tests done on samples such as blood or tissue that have been taken from the human body. They can be used to detect diseases, monitor a person’s health, or in precision medicine to identify patients who are likely to benefit from specific therapies.
Researchers at UCL are combining imaging and in vitro systems with machine learning techniques that are able to process vast amounts of data and spot patterns of biomarkers that signify disease. The implications of this are vast – allowing technology to make accurate diagnoses as well as human experts can and, in some cases, spotting new patterns that may enable even earlier diagnosis.
The adaption of portable digital and mobile technologies into diagnostic tools is also having significant global impact, enabling diagnosis of conditions even in low-resource or hard to access areas.
The case-studies below offer a glimpse of the healthcare engineering research activity within diagnostics at UCL. If you would like your research project to be featured here, please email email@example.com
FASt-Mal system uses machine learning to enable diagnosis in hard to reach areas.
A novel artificial intelligence (AI) system can diagnose eye disease as accurately as expert ophthalmologists.
Mobile phone-connected HIV tests, which link to online prevention and medical care are being developed for use in South African communities affected by HIV.
SuStaIn can uncover data-driven disease phenotypes.