Diagnosing arm injuries accurately and efficiently is crucial for effective medical treatment. Magnetic Resonance Imaging (MRI) provides detailed anatomical information, making it invaluable in clinical settings. This paper explores the integration of a Multi-Agent System (MAS) framework for enhancing MRI-based diagnosis of arm injuries. The MAS architecture involves agents for image segmentation, feature extraction, classification, and decision support, optimizing diagnostic accuracy through collaborative processing of MRI data. Methodologically, MRI images are preprocessed to enhance clarity and remove artifacts, followed by segmentation to delineate specific arm structures and tissues. Feature extraction agents derive relevant descriptors such as texture and shape, crucial for injury classification. A fuzzy logic-based decision-making mechanism integrates expert knowledge to refine diagnostic outcomes. Experimental evaluation employs real MRI datasets, comparing MAS-generated diagnoses with expert assessments. Results demonstrate significant correlation and diagnostic accuracy, validating MAS efficacy in identifying fractures, soft tissue injuries, and other pathologies. Challenges include computational complexity and integration of diverse MRI data sources. This paper underscores the potential of MAS in medical imaging, particularly MRI-based arm injury diagnosis, offering insights for future enhancements in clinical decision support systems and patient care management

MRI, Multi-Agent System (MAS), arm injuries, medical imaging, diagnosis, fuzzy logic, image segmentation, feature extraction, classification, clinical decision support

The paper focus on the diagnostic and treatment outcomes of 50 patients with various arm injuries provides valuable insights into orthopedic practice and healthcare management. The findings underscore several key points: Firstly, the diversity of injuries observed, ranging from fractures to sprains and tendonitis, highlights the complexity and varied nature of musculoskeletal conditions affecting the arm. This diversity emphasizes the need for tailored diagnostic approaches and treatment plans that consider individual patient characteristics and injury specifics. Secondly, the effectiveness of treatments administered based on accurate diagnoses is evident. Surgical interventions for fractures and conservative management strategies for less severe injuries like sprains and tendonitis resulted in favorable outcomes, with many patients achieving full recovery or significant improvement in mobility and function. The utilization of a multi-agent system (MAS) integrated with fuzzy logic rules proved instrumental in achieving precise diagnoses, particularly in cases where clinical presentations were ambiguous or complex. This approach facilitated a comprehensive evaluation of patient data, enabling healthcare providers to make informed decisions and optimize treatment strategies. Furthermore, the study underscores the role of technology, specifically MAS and fuzzy logic, in enhancing diagnostic accuracy and treatment efficacy in orthopedic settings. By leveraging these advanced methodologies, healthcare professionals can navigate the complexities of musculoskeletal injuries more effectively, leading to improved patient care and outcomes. Looking forward continued research and development in MAS and fuzzy logic applications hold promise for further enhancing diagnostic capabilities and refining treatment protocols across orthopedic and broader healthcare domains. This ongoing innovation is crucial for addressing the evolving challenges in musculoskeletal care and ultimately improving the quality of life for patients worldwide

Naveen Dalal (2019). Diagnosis of Arm Injuries using MRI Images and Multi-Agent System (MAS). International Journal on Emerging Technologies, 10(4): 484–489