Cybernetics and Computer Engineering, 2021, 2(204)
KUTSIAK O.A., PhD (Engineering),
Senior Researcher of Bioelectrical Control & Medical Cybernetics Department
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of MES of Ukraine,
40, Acad. Glushkov av. Kyiv, 03187, Ukraine
AI-TECHNOLOGY OF MOTOR FUNCTIONS DIAGNOSTICS AFTER A STROKE
Introduction. Diagnostics of motor functions plays an important role in the motor functions restoration after stroke. Synthesis of effective technologies for personalized assessment of motor functions disorders at different rehabilitation stages is an urgent scientific and applied task.
The purpose of the paper is to develop information technology for diagnostics of motor functions deficit after stroke, that uses artificial intelligence tools to increase the effectiveness of the diagnostic process.
Results. The theoretical and practical foundations to synthesize AI-technology for personal diagnostics of motor functions deficit, and the assessment of their restoration as a result of rehabilitation measures after stroke have been developed. For informational assistance to the physician in the diagnostic process, artificial intelligence is used. A new class of mobile digital medicine tools – the specialized software modules for motor functions diagnostics “MovementTestStroke 1.1 (PC)” installed in the PC-structure, and “MovementTestStroke 1.1 (MD)” installed in mobile platforms running under Android operation system have been developed. Software implementation — Visual Studio 2019, C# programming language. Structural and functional models of user – software modules interaction, algorithms for motor function deficit diagnostics, and UML-diagrams of these modules are presented.
Functional features of the technology: an expanded range of evidence criteria for personalized quantitative assessment of limb movements deficit, storage in the Database and display on the interface the results of deficit assessment, as well as the deficit dynamics during the rehabilitation course in a convenient form (tables, graphs) make it possible to reduce the physician’s error, prevent complications, identify the disorders specifics, compare the rehabilitation effectiveness of the upper and lower limbs, their distal and proximal parts, including fine motor skills of the hand, restoration of which helps to restore speech in motor or motor and sensory aphasia.
Conclusions. The usage of artificial intelligence tools to diagnose motor deficit will increase the diagnostic effectiveness, and, as a consequence, rehabilitation services for patients after stroke.
Keywords: diagnostics, motor functions, stroke, personal quantitative assessment, criteria, technology, artificial intelligence, software module, structural-functional model, algorithm, activity diagram.
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