Musculoskeletal (MSK) Diseases: A Rapidly Rising Global Burden
The global aging population is currently at the peak in human history. Understanding the healthy, aging population and reducing the socioeconomic impact of age-related diseases are a key research priority for industrialized and developing countries, along with a better mechanistic understanding of the physiology of aging.
According to the WHO an estimated 1.71 billion individuals worldwide are affected by musculoskeletal conditions, making them the primary cause of disability globally. Among these conditions, low back pain stands out as the leading contributor to disability in 160 countries. The impact of musculoskeletal conditions extends beyond physical discomfort, as they significantly restrict mobility and dexterity, leading to early retirement, decreased well-being, and reduced social participation.
Due to population growth and aging trends, the number of people living with musculoskeletal conditions and related functional limitations is rapidly rising. This presents a significant and growing public health challenge that requires attention and effective strategies to address the increasing burden on individuals and societies.
Keyword AI
Digitalization, a phenomenon that has been a part of our lives for decades, has continued to expand and integrate into various aspects of our social life, society, and economy, including the field of medicine. The rapid advancement of technology, particularly in the realm of Artificial Intelligence (AI), has accelerated the process of digitalization, transforming numerous sectors, including healthcare.
AI refers to computer programs, known as algorithms, that extract and recognize disease-related characteristics and patterns from digital data. Through a process called “machine learning,” AI autonomously learns from data material and reinforces its own characteristics. This surpasses our human capacity for assimilating information, leading to more objective, accurate, and highly reproducible results. Moreover, AI enables structural analyses beyond human detection capabilities, enhancing diagnostic precision even for subtle details not visible to the naked eye or a magnifying glass.
AI in MSK Radiology
This is also the case in MSK Radiology, where rapid developments of digital image processing and analysis are on the way. In this discussion, I would like to focus on the specific use case of AI in the assessment of 2D radiographs: X-rays, owing to their widespread availability, cost-effectiveness, and quick performance, serve as the primary imaging modality for a diverse range of medical situations, particularly in emergency settings and routine screenings.
The status quo: Considering the diagnosis of X-ray images in orthopedics/traumatology, the diagnosis of X-ray images is still largely carried out manually with narrative image descriptions, which are very subjectively impacted. This is also reflected in the high inter-and intra-individual variability of the findings, and thus, especially in the assessment of diseases such as osteoarthritis, a low accuracy and comparability is present.
AI Disrupting the Status Quo of MSK Radiology Reporting
The current status quo in MSK radiology reporting involves non-structured and manual workflows for diagnostic and disease findings. This approach often results in late recognition of disease symptoms, leading to delayed treatments and non-standardized parameters. The agreement rate among physicians in disease classification can be as low as 30%. However, there is a drive to disrupt this status quo by introducing continuous AI-supported workflows with standardized and accurate disease findings. The aim is to empower healthcare professionals with powerful tools to assess, track, and predict diseases more effectively.
Through advanced software and deep learning techniques, healthcare professionals can augment and standardize the assessment process of 2D/3D imaging in musculoskeletal (MSK) radiology. This data analysis is performed with an objective to recognize MSK-relevant scoring and measuring tasks in imaging data, offering precise and detailed insights. Additionally, new bone health imaging biomarkers enrich the findings, providing valuable information about the state and progression of the disease. The ultimate objective is to detect and digitally document clinically relevant features in MSK imaging data that may go beyond what human readers can achieve within regular working hours.
By Richard Ljuhar, PhD – CEO & Co-founder ImageBiopsy Lab
After graduating with an engineering degree from the Vienna University of Technology, he gained international experience (USA, China) at one of the world’s leading women’s health companies, based in the greater Boston area. After returning to Europe to finish his PhD studies, Richard co-founded ImageBiopsy Lab on the premise to enable change the status quo of how bone diseases are being screened, diagnosed, treated and predicted. Researching and certifying novel imaging algorithms has become the core of ImageBiopsy Lab since its foundation in late 2017.