AI in Cardiac Health Assessment and Intervention Support through Multimodal Data Analysis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: closed (16 September 2022) | Viewed by 603
Special Issue Editors
Interests: artificial intelligence; formal methods; biomedical signal processing; service based healthcare; intelligent internet of medical things
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; artificial intelligence; deep learning; neural networks; data science; information and communication management
Special Issues, Collections and Topics in MDPI journals
2. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW 2007, Australia
3. Griffith Business School, Griffith University, Brisbane, QLD 4111, Australia
Interests: optimisation models; data analytics; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We understand the cardiac system as a set of rules that govern a range of physiological processes, which work together to provide oxygen, nutrients, hormones, and other important substances to cells and organs in the body. A large number of interacting entities constantly communicate information to one another. This gives rise to highly complex communication and activity patterns, which manifest in electrical, chemical, and mechanical activity. Cardiac health assessment observes and analyses this activity. The challenge proposed by sensor applications is two-fold. First, we must establish measurements that reflect the multimodal nature of the cardiac system and yield relevant data for a particular assessment task. The second challenge is to make sense of this data and to design accurate medical decision support.
For this Special Issue, we call for papers that address the challenges of cardiac health assessment with multimodal measurements and AI-based analysis. We are interested in care pathways that incorporate multimodal data acquisition. This might include physiological signals, medical imaging, and biopsy. One interesting property of physiological measurement is that multiple signals can be acquired at the same time. For example, it is possible to monitor both electrical and acoustical activity of the human heart. AI algorithms might help to exploit this property to establish safe and reliable cardiac assessment. These algorithms might even be flexible enough to handle imaging and signal data that reflect electrical, chemical, and mechanical activity of the cardiac system. We are interested to learn how this truly multimodal AI-based data analysis can improve current care pathways and thereby improve outcomes for patients.
Topics of interest:
- Overall cardiac health assessment using deep learning based multimodal data analysis
- Specific cardiac condition and severity detection using deep learning
- Personalized intervention impact assessment with deep learning based data analysis
- Explainable personal care plan using deep learning
- Physiological signal acquisition and analysis for cardiac monitoring, including but not limited to electrocardiogram, RR-intervals, photoplethysmography, and heart sounds
- Cardiac imaging, such as heart ultrasound, X-ray, positron emission tomography, and magnetic resonance imaging
Optical image analysis for biopsy samples.
Dr. Oliver Faust
Dr. Prabal Datta Barua
Dr. Subrata Chakraborty
Guest Editors
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