Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Artificial intelligence has been used to detect the most common causes of dementia and stroke -- small vessel damage, according to a study. Scientists at Imperial College London and the University of Edinburgh in Britain have created machine-learning software to identify and measure the severity of small vessel disease more accurately than some current methods. Their findings were published in the journal Radiology. The researchers said the tests at Charing Cross Hospital, part of Imperial College Healthcare National Health Service Trust, could pave the way for more personalized medicine and quicker diagnosis in an emergency setting. "This is the first time that machine learning methods have been able to accurately measure a marker of small vessel disease in patients presenting with stroke or memory impairment who undergo CT scanning," lead author Dr. Paul Bentley, a clinical lecturer at Imperial College London, said.

The researchers at Imperial College London have developed a new AI (artificial intelligence)-backed software that can identify and measure the severity of small vessel disease, which is one of the most common causes of stroke and dementia, using brain scans with AI can more accurately than all current method. For those unaware, small vessel diseases (SVD) is a common neurological disease among the aged that reduces blood flow to the deep white matter connections of the brain, ultimately killing the brain cells. It progresses with age but can be accelerated by hypertension and diabetes. This is a severe condition and the detection of its causes early could help doctors provide better treatment in an emergency situation, while also being capable of predicting the chances of someone developing dementia or immobility, due to slowly progressive SVD. Commenting on the software, Dr. Paul Bentley, lead author and clinical lecturer at Imperial College London says, This is the first time that machine learning methods have been able to accurately measure a marker of small vessel disease in patients presenting with stroke or memory impairment who undergo CT scanning.

New software, created by scientists at Imperial College London and the University of Edinburgh, has been able to identify and measure the severity of small vessel disease, one of the commonest causes of stroke and dementia. The study, published in Radiology, took place at Charing Cross Hospital, part of Imperial College Healthcare NHS Trust. Researchers say that this technology can help clinicians to administer the best treatment to patients more quickly in emergency settings -- and predict a person's likelihood of developing dementia. The development may also pave the way for more personalised medicine. "This is the first time that machine learning methods have been able to accurately measure a marker of small vessel disease in patients presenting with stroke or memory impairment who undergo CT scanning. Our technique is consistent and achieves high accuracy relative to an MRI scan -- the current gold standard technique for diagnosis. This could lead to better treatments and care for patients in everyday practice."