Methodological foundations of somatoscopic research in automated decision support
systems
Authors: Poplavskyi Oleksandr , Shkuratov Oleksii , Kolisnyk Serhii , Poplavska Anna , Yovenko Dmytro 
Published: November 2024

Automated Decision Support Systems (ADSS) are widely applied in various fields of
science and technology, particularly in medicine, where their role in diagnostic,
prognostic, and therapeutic processes is undeniable. The use of computational
technologies in medical practice has become a necessary stage in the development of the
field; however, the increasing volume of data that requires processing, along with
heightened demands for accuracy, speed, and reliability of recommendations,
significantly complicates decision-making processes. The growing amount of data,
combined with the need to minimize the risks of erroneous decisions, drives the
scientific community to seek innovative information technologies that can ensure a high
level of accuracy in computational operations while minimizing the time required for
their execution. Somatoscopic measurements, which are a crucial element in assessing the
condition of the musculoskeletal system and overall physical posture of a patient,
require precise and systematic image analysis that allows the identification of
critically important anatomical markers. This process involves the accurate
determination of key points on images of anatomical structures, which serves as the
foundation for making precise predictions about the patient's condition and subsequent
treatment planning. In the context of continuously increasing volumes of medical data
and stricter requirements for the quality of decisions, the implementation of innovative
technologies, particularly deep learning, becomes increasingly relevant as it enhances
the processes of analysis and diagnosis. The aim of this study is to conduct a
comprehensive analysis of the methodological aspects of applying deep learning in the
context of automating the diagnosis of the musculoskeletal system and posture
assessment. The study emphasizes the use of specialized neural network architectures
that enable the identification of key anatomical points on images, thereby facilitating
a deeper and more accurate analysis of anatomical structures. This approach
significantly improves the efficiency of diagnostic processes by minimizing the
likelihood of errors in decision-making and optimizing the performance of medical
systems. The results of the conducted research demonstrate the significant potential of
deep learning algorithms in medical systems for the automated analysis of images,
allowing for substantial improvements in the accuracy and speed of decision-making. Such
automation contributes to reducing the risk of subjective errors associated with the
human factor, which is particularly important in complex clinical cases. Therefore, the
further development of research in this direction is of paramount importance for the
medical field, as it opens new opportunities for solving complex diagnostic tasks at an
innovative technological level. The integration of deep learning technologies into the
processes of extracting somatoscopic data not only enhances diagnostic efficiency but
also creates prerequisites for the development of new decision support systems that
optimize medical practice.