Prospects for the Development of Intelligent Mechanical Ventilation Modes

Introduction. In modern clinical practice, mechanical ventilation is not only a primary method of intensive care for severe respiratory disorders of various origins but also a method of palliative therapy for patients with severe chronic respiratory failure. Consequently, there is a continuous pursuit of new ventilation modes capable of providing the most physiological and safe respiratory support for patients with various types of respiratory dysfunction. The aim of this review is to evaluate the intelligent modes of mechanical ventilation available in medical practice. Materials and methods. A literature search was conducted in the following electronic scientific databases: PubMed, Google Scholar, and eLibrary. Article selection was performed using relevant keywords. To enable a comparative analysis of the intelligent mechanical ventilation modes presented in the medical literature, including their advantages and disadvantages over other existing modes, the review also incorporated data from relevant clinical practice guidelines and medical textbooks. Results. Currently, there is a notable expansion in the range of mechanical ventilators being employed in clinical practice. This trend is accompanied by a proliferation of devices offering similar functionality under different commercial names, a factor that healthcare professionals must account for in their practical work. Conversely, the growing complexity of mechanical ventilation modes and techniques contributes to an increased workload and potential cognitive overload for intensive care unit staff. Discussion and conclusion. Despite the expanding range of mechanical ventilators employing various assisted modes of respiratory support, the need persists for the development of a fully automated mechanical ventilation control system. Such a system must be capable of providing maximum adaptation of the ventilation mode to the changing parameters of a patient's respiratory function. It must be able to both autonomously select optimal modes and parameters for mechanical ventilation and, when interacting with the user, present a selection of potential corrective adjustments.