Prof. Mohamad Sawan (FIEEE, FCAE, FEIC)
Mohamad Sawan is Chair Professor in Westlake University, Hangzhou, China, and Emeritus Professor in Polytechnique Montreal, Canada. He is founder and director of the Cutting-Edge Net of Biomedical Research And INnovation (CenBRAIN) in Westlake University, Hangzhou, China. He received the Ph.D. degree from University of Sherbrooke, Canada. Dr. Sawan research activities are bridging micro/nano electronics with biomedical engineering to introduce smart medical devices dedicated to improving the quality of human life. For almost three decades, he was focusing on building wearable and wirelessly powered implantable microsystems to restore lost sensory abilities and mitigate malfunctioning organs through monitoring healthcare parameters (signals, images, pressure, etc) and neuromuscular electrical/optic stimulation. He is Co-Founder, Associate Editor and was Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems (2016-2019). He is founder of the Polystim Neurotech Laboratory, and Co-founder of the International IEEE-NEWCAS and the International IEEE-BioCAS Conference. He was General Chair of both the 2016 IEEE International Symposium on Circuits and Systems, and the 2020 IEEE International Medicine, Biology and Engineering Conference (EMBC). He was awarded the Canada Research Chair in Smart Medical Devices (2001-2015), and was leading the Microsystems Strategic Alliance of Quebec, Canada (1999-2018). Dr. Sawan published more than 1000 peer reviewed papers, three books, 13 book chapters, and 12 patents and 17 other pending patents. He received several awards, among them the Zhejiang Westlake Friendship Award, the Qianjiang Friendship Ambassador Award, the Shanghai International Collaboration Award, the Queen Elizabeth II Golden Jubilee Medal, and the Medal of Merit from the President of Lebanon. Dr. Sawan is Fellow of the IEEE, Fellow of the Canadian Academy of Engineering, Fellow of the Engineering Institutes of Canada, and “Officer” of the National Order of Quebec.
Speech Title: "Intelligent Biosensors for the Diagnosis and Subsequent Treatment of Brain Diseases"
Abstract: Software and hardware neuromorphic implementations are becoming major research targets to mimic brain efficient operation. Consequently, machine and deep learning techniques are occupying large parts of emerging neural network-based chipsets. Neurodegenerative diseases are among main applications requiring building custom smart medical devices intended for the diagnosis, treatment, and prediction of health conditions. This talk covers signal processing, bioelectronic circuits and systems intended to implement brain interfaces dealing with multidimensional design challenges such as small volume devices, reliable implantable systems, efficient power management, low-power and high-data rate wireless communication methods, etc. Case studies include various wearable, and implantable devices intended for neurorecording and closed-loop neuromodulation for several applications such as vision, epilepsy, stroke, addictions, etc. Also, cells monitoring and manipulations are consistent options for efficient neural diseases studies.
Prof. Peter W. Macfarlane
The University of Glasgow, UK
Professor Macfarlane is Emeritus Professor and Hon Senior Research Fellow at the University of Glasgow. He was Professor in Medical Cardiology from 1991 – 1995 and Professor of Electrocardiology from 1995 – 2010. His major interest throughout has been the application of computer techniques to ECG interpretation. The work of his team has been adopted commercially and the University of Glasgow ECG interpretation program developed in his laboratory is currently used worldwide. He is particularly interested in differences in ECG appearances due to age, gender and ethnicity, and as a result, he has influenced international guidelines for the ECG definition of acute myocardial infarction. In addition, he has established an ECG Core Laboratory for handling ECGs recorded in national and international clinical trials and epidemiological studies. Professor Macfarlane is a Fellow of many learned Societies, including the Royal Society of Edinburgh. In 2000, he was awarded a DSc on the basis of his contribution to research in his own field. He was also jointly awarded the 1998 Rijlant International Prize in Electrocardiology by the Belgian Royal Academy of Medicine. In January 2014, he was awarded a CBE for Services to Healthcare.
Speech Title: "Automated 12 Lead ECG Analysis – From Alpha to Omega via Delta and Omicron!"
Abstract: The first human ECG lead was recorded in 1887 and the standard 12 lead ECG was established by 1942. Since then, there have been thousands of studies evaluating the usefulness of the ECG in the diagnosis and treatment of patients. Technology has advanced enormously and automated processing of the ECG is commonplace. Even some wrist watches now have the capability of recording and interpreting a single lead ECG. Artificial intelligence (AI) is also involved in the latest research in this field. Long term patient monitoring in the community is now also perfectly feasible and “wearables” is almost a household word. The lecture will review the development of electrocardiography from its inception through the development of automated ECG analysis to its current usage which even includes assessment of left ventricular dysfunction. The presentation will also include some recent work involving Covid patients, and comment on the advantages and disadvantages of different aspects of the analytical techniques now available. The value of the ECG is indisputable but current enthusiasm for the use of AI based methods, including prediction of events, requires to be considered objectively in respect of overall accuracy.
Dr. Nan Wu
Peking Union Medical College, China
Nan Wu, MD, associate chief surgeon at Department of Orthopedic Surgery, Peking Union Medical College Hospital, assistant professor of Peking Union Medical College and Chinese Academy of Medical Sciences, doctoral supervisor, chief director of Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences and director of Youth Department of Peking Union Medical College Hospital. Dr. Wu completed his doctor degree at Peking Union Medical College and post-doctor training at Baylor College of Medicine. Dr. Wu has received several research, teaching and service awards. He has published over 90 highly cited peer-reviewed publications and has been invited as a speaker at over 100 Conference worldwide. He serves as a surgeon of spinal diseases, especially spinal deformities and is interested in the genetic and genomics factors in the pathogenesis of spinal deformities. He applies the genetic and genomics techniques to the diagnosis and treatment of spinal deformity. Dr. Wu is an expert in diagnosing and treating spinal deformities through precise assessment of a patient followed by various therapy. His research has shown for the first time the main genetic cause of congenital scoliosis and the finding was published in the New England Journal of Medicine. He is currently the Principal Investigator of several Multiple-center trails investigating the pathogenesis, diagnosis, treatment, and prognosis of spinal deformities.
Speech Title: "PhenoApt Leverages Clinical Expertise to Prioritize Candidate Genes via Machine Learning"
Abstract: In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous ﬁltering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapid identiﬁcation of causative genes. Since the interpretations of ES data require comprehensive clinical analyses, taking clinical expertise into consideration can speed the mocular diagnoses of Mendelian disorders. To leverage clinical expertise to prioritize candidate genes, we developed PhenoApt, a phenotype-driven gene prioritization tool that allows users to assign a customized weight to each phenotype, via a machine-learning algorithm. Using the ability to rank causative genes in top-10 lists as an evaluation metric, baseline analysis demonstrated that PhenoApt outperformed previous phenotype-driven gene prioritization tools by a relative increase of 22.7%–140.0% in three independent, real-world, multi-center cohorts (cohort 1, n=185; cohort 2, n=784; and cohort 3, n=208). Additional trials showed that, by adding weights to clinical indications, which should be explained by the causative gene, PhenoApt performance was improved by a relative increase of 37.3% in cohort 2 (n=471) and 21.4% in cohort 3 (n=208). Moreover, PhenoApt could assign an intrinsic weight to each phenotype based on the likelihood of its being a Mendelian trait using term frequency-inverse document frequency techniques. When clinical indications were assigned with intrinsic weights, PhenoApt performance was improved by a relative increase of 23.7% in cohort 2 and 15.5% in cohort 3. For the integration of PhenoApt into clinical practice, we developed a user-friendly website and a command-line tool.