In this paper, an explainable deep learning-based classifier based on adaptive sinc filters for Parkinson's Disease diagnosis (PD) along with determining its severity, based on analyzing the gait cycle (SincPD) is presented. Considering the effects of PD on the gait cycle of patients, the proposed method utilizes raw data in the form of vertical Ground Reaction Force (vGRF) measured by wearable sensors placed in soles of subjects' shoes. The proposed method consists of Sinc layers that model adaptive bandpass filters to extract important frequency-bands in gait cycle of patients along with healthy subjects. Therefore, by considering these frequencies, the reasons behind the classification a person as a patient or healthy can be explained. In this method, after applying some preprocessing processes, a large model equipped with many filters is first trained. Next, to prune the extra units and reach a more explainable and parsimonious structure, the extracted filters are clusters based on their cut-off frequencies using a centroid-based clustering approach. Afterward, the medoids of the extracted clusters are considered as the final filters. Therefore, only 15 bandpass filters for each sensor are derived to classify patients and healthy subjects. Finally, the most effective filters along with the sensors are determined by comparing the energy of each filter encountering patients and healthy subjects.

Researchers at MIT have developed an AI system that can diagnose Parkinson's disease and track its progression, simply by monitoring someone's breathing patterns as they sleep. The device looks like an internet router and can be mounted on the wall in a bedroom. It emits radio waves and then a neural network analyzes the reflected waves to assess breathing patterns. Crucially, the technology may be able to assist in diagnosing Parkinson's disease much earlier than many conventional techniques and it is highly convenient and non-invasive compared with traditional diagnostics. It may also be particularly beneficial in testing new treatments for Parkinson's as a non-invasive method to monitor disease progression.

The application of methods based on artificial intelligence and machine learning opens the door to technology to diagnose Parkinson's.

Artificial Intelligence (AI) will fully transform health care. It can improve outcomes and patient experience while democratizing access to healthcare services. AI can help improve the experience of healthcare practitioners, enabling them to reduce burnout and spend more time in serious direct patient care. AI can help healthcare systems manage population health proactively through the allocation of resources with a view to maximum impact. Using a mobile or web based Intelligent Digital Medical Assistant through which to provide remote video based medical consultations and almost instantaneously extracting knowledge from the video in order to support or suggest a diagnosis, while at the same time using the same system to organize and follow up on these interactions, is the way to use AI technology to improve outcomes and efficiency in primary healthcare, particularly in the midst of a global pandemic! If you want to learn more and be at the forefront of health care technology, join Footchat the definitive free online healthcare community.

Parkinson's disease, a neurodegenerative disorder that affects movement, impacts more than ten million people worldwide; roughly 60,000 are diagnosed each year. Early detection can forestall the onset of severe symptoms, but it's easier said than done -- no specific test exists to diagnose …

"Siri, do I have Parkinson's?" That might sound flippant, but actually new research shows that it's possible to detect Parkinson's symptoms simply by using algorithms to detect changes in voice recordings. Parkinson's, a degenerative disorder of the central nervous system, is usually diagnosed through analysis of symptoms along with expensive medical imaging to rule out other conditions--though there is currently no concrete method for detecting it. Max Little, from the University of Oxford, has different ideas. He's been developing software that learns to detect differences in voice patterns, in order to spot distinctive clues associated with Parkinson's.