Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools: 2nd Edition

Dear Colleagues,

The use of automatic support tools for medical practice is continuously increasing. From family doctors to surgeons, specialists are using a wide number of devices which increase the level of accuracy of their diagnoses. Deep learning algorithms and data analyses in general are providing new possibilities for doctors. Many doctors now use user-friendly tools and devices in their daily practice, even though they may be skilled in advanced tools or research-level algorithms.

On the other hand, many of the newly proposed biomedical algorithms require offline tools, i.e., data must be saved and processed, and a result is provided to the doctor to be used to support patient diagnosis. For example, where a medical test is conducted in advance and the result is sent to the doctor afterwards. However, other common situations exist where providing important patient data to the doctor immediately is required.

Currently, medical devices provide real-time monitoring of patients by providing basic data (cardiac rhythm, blood pressure, and body temperature); however, it is uncommon to produce real-time diagnoses since most of the algorithms require complex computation, so real-time results cannot be provided.

Artificial intelligence algorithms have proven to be tools that improve the accuracy of detection in many medical scenarios; they are able to provide predictions and can learn new features while being used. However, real-time implementation is still a challenge as ideally results should be provided in a few seconds (at most) so that they can be helpful to the practitioner. So that this goal can be achieved, special hardware, as well as parallel software implementations, must be developed.

This Special Issue welcomes real-time implementations of algorithms that can serve as immediate decision-support tools in medical situations. This Special Issue is open, but not restricted, to any of the following biomedical areas where real-time implementation is proposed:

• Signal processing algorithms for real-time pathology detection;
• Image processing algorithms for real-time diagnosis;
• Artificial intelligence algorithms for real-time implementation (this may include online training);
• Real-time parallel software implementations;
• Hardware implementations for computation speed-up based on specific hardware: VLSI, FPGA, GPU, etc.;
• Hardware implementations for portability and/or low power;
• User tools for real-time decision-support systems;
• Implementations for real-time remote diagnosis.

Prof. Dr. Alfredo Rosado
Guest Editor

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• real-time pathology detection
• real-time diagnosis
• signal processing
• image processing
• artificial intelligence
• real-time parallel software
• implementations