The University of Liverpool Doctoral Network in AI for Future Digital Health is directed at creating and maintaining a community of AI health care professionals that can apply the latest research within AI to Health Care. The vision is that of a world-class centre providing high-quality doctoral training within the domain of AI for Future Digital Health.
Each PhD project has been carefully co-created in collaboration with a health provider and/or a healthcare commercial partner so that the outcomes of the PhD research will be of immediate benefit. The network provides students with training, culminating in a PhD, in a collaborative environment that features, amongst other things, peer-to-peer and cohort-to-cohort based learning. On completion students will be well-placed to take up rewarding careers within the domain of AI and Digital Health.
Current Opportunities (Deadline 30 July)
8 December 2020
The AI for Future Digital Health workshop was held, on Wednesday 8 December 2020, as part of the British Computer Society Specialist Group in AI's 40th annual AI conference. The event is usually held in Peterhouse college in Cambridge, but this year, for obvious reasons was online. There were 412 registrations for the main conference, of which 84 attended the AI for Future Digital Health workshop. The workshop featured guest speaker Kerstin Bach from the Norwegian University of Science and Technology and showcased work in progress at the University of Liverpool under the umbrella of Liverpool University's Doctoral Network in AI for Future Digital Health. The workshops was chaired by Nirmalie Wiratunga from the Aberdeen Robert Gordon University, and Vitaliy Kurlin and Frans Coenen both from the University of Liverpool.
The guest speaker, Kerstin Bach presented a device, hosted on a smart phone and founded on Case-Based Reasoning (CBR), for prescribing self-management exercise routines for individuals suffering from lower backpain; the main cause of disability in Europe. It was interesting to note how well CBR can work with only a few examples. The current case base features 300 patients but many more are anticipated, obtained using the feedback loop that is a feature of the system.
The remainder of the workshop was divided into two session, four presentations per session. The first session featured talks on Leukaemia, mobility devices for young children, biomarkers for liver cancer and corneal disease by Muizdeen Raji, Peter Wright, Mohamed Elhalwagy and Weiqiang Chen respectively. The first talk was directed at using machine learning in the context of cytometry (the measurement of the characteristics of cells). This was couched as an unsupervised learning problem. The data was clustered using a self-organising map technique which was then used as a binary classifier. Muizdeen Raji observed that a particular challenge of using cytometry was data acquisition, especially given the current pandemic restrictions. The second presentation focused on children with mobility disabilities. Peter Wright emphasised the need for early mobility support and proposed a nobility device, fitted with sensors, to help disabled nine-month-olds to crawl. Great video simulating a toddler using the mobility device. The talk raised some interesting questions concerning the ethics of testing the device, is it OK to use real babies? Mohamed Elhalwagy noted that ultrasound techniques for detecting liver cancer did not work well with respect to tumours that were 5cm or less in length; the stage when intervention would be most effective. Instead, a mechanism using bio-markers was proposed. The presentation investigated a range of bio markers. In the end a combination of markers was proposed, the GALAD score, built into a linear regression model for predicting liver cancer. There was much discussion afterwards as to whether the assumed linearity between the dependent variable and the independent variables was valid. The final presentation, in this first session, was directed at the detection of corneal disease using Optical Coherence Tomography (OCT) scan technology. Weiqiang Chen observed that there were a range of challenges associated with this technology involving the nature of the hardware used, segmentation of the resulting 3D images and their sub-sequent reconstruction. The presentation focused on AI solutions with respect to these challenges.
The second session featured presentations on predicting influenza epidemics, prioritising pathology data, sleep pose recognition and cycling to improve public health by Yanhua Xu, Jing Qi, Omar Elnaggar and Aidan Watmuff respectively. The first presentation, the prediction of influenza, seemed particularly apt given the current circumstances (COVID-19 pandemic). The central theme of the presentation was that intermediary influenza strains from animals can provide a useful predictor of influenza in humans. Yanhua Xu proposed a mechanism founded on the BLAST (Basic Local Alignment Search Tool) algorithm for comparing human-animal protein sequence information to predict Influenza A epidemics. Jing Qi presented work on prioritising pathology results in the absence of a ground truth. Two solutions were presented, a point-based approach and a time series approach with the second argued to be the better. Much discussion at the end of the presentation as to whether a ranking approach would be more beneficial than the proposed binary approach, priority versus non-priority. The third presentation was directed at sleep pose recognition as a means of supporting the hypothesis that Plantar fasciitis (pain on the bottom of the heel and/or sole of the foot) is affected by sleep posture. Omar Elnaggar proposed a Support-Vector Machine (SVM) model to classify twelve alternative sleep poses. The final presentation considered the benefits that cycling can bring to public health. Aidan Watmuff, who confessed to being a keen cyclist himself, presented an unsupervised analysis of a 1,208 cyclists in the Liverpool City Region. There was lengthy discussion concerning the clustering algorithm used, k-modes, and whether alternative forms of unsupervised learning might be more appropriate.
Overall, the workshop demonstrated the extensive scope of the many health related applications that can benefit from AI. The presentation slides are available at: http://www.bcs-sgai.org/ai2020/?section=workshops.
The workshop programme was as follows:
|09:00||09:10||Welcome: Frans Coenen (Uni of Liverpool)|
|Session 1, Chair: Nirmalie Wiratunga (Aberdeen Robert Gordon University)|
|09:10||09:40||Kerstin Bach (Norwegian University of Science and Technology).
Inited speaker. AI-based recommendations to facilitate the self-management of low-back pain patients.
|09:40||10:00||Nagesh Kalakonda (Clatterbridge Cancer Centre), Vitaliy Kurlin (Uni of Liverpool), Raji Muizdeen (Uni of Liverpool), Joseph Slupsky, (Uni of Liverpool).
Machine learning for mass cytometry data of chronic lymphocytic leukemia.
|10:00||10:20||Iain Hennessey (Alder Hey Children's Hospital), Shan Luo(Uni of Liverpool), Farnaz Nickpour (Uni of Liverpool), Paolo Paolletti, (Uni of Liverpool), Peter Wright (Uni of Liverpool).
Future design of pediatric assistive mobility devices.
|10:20||10:40||Tim Cross (Royal Liverpool and Broadgreen Hospital Trust), Mohamed Elhalwagy (Uni of Liverpool), Ahmed Elsheikh (Uni of Liverpool), Philip Johnson (Uni of Liverpool), Vinzent Rolny (Roche Diagnostics GmbH):
Biomarkers based detection of liver cancer.
|10:40||11:00||Weiqiang Chen (Uni of Liverpool), Stephen Kaye (Royal Liverpool and Broadgreen Hospital Trust), Yaochun Shen Uni of Liverpool), Yalin Zheng Uni of Liverpool).
Segmentation of corneal images using a convolutional neural network.
|Session 2, Chair: Vitaliy Kurlin (Uni of Liverpool)|
|11:30||11:50||Neil French (Uni of Liverpool), Roberto Vivancos (Public Health England), Dominik Wojtczak (Uni of Liverpool), Yanhua Xu (Uni of Liverpool):
Machine learning in influenza A classification.
|11:50||12:10||Girvan Burnside (Uni of Liverpool), Paul Charnley (Wirral Teaching Hospital), Frans Coenen (Uni of Liverpool), Jing Qi (Uni of Liverpool):
Learning to prioritise pathology data in the absence of a ground truth.
|12:10||12:30||Frans Coenen (Uni of Liverpool), Omar Elnaggar (Uni of Liverpool), Andrew Hopkinson (Hopkinson Research), Paolo Paolletti, (Uni of Liverpool).
Wearable sensing for non-invasive human pose recognition during sleep.
|12:30||12:50||Dannie Arribas-Bel (Uni of Liverpool), Mark Green (Uni of Liverpool), Huw Jenkins (Liverpool City Region Combined Authority), Vitaliy Kurlin (Uni of Liverpool), Aidan Watmuff (Uni of Liverpool).
Using k-modes clustering to identify different types of cyclist in the Liverpool City Region.
|12:50||13:00||Close: Frans Coenen (Uni of Liverpool)|
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