Deep Learning in Early Osteoarthritis Detection via Biomarkers Deep Learning for Differential Diagnosis in Equine Osteoarthritis: Exploring Synovial Fluid Biomarkers
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2024
Författare
Kaewchino, Sirada
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In the past, radiographic techniques have been used to diagnose osteoarthritis (OA),
a common joint disease leading to lameness in racehorses. However, these methods
typically only reveal the disease once irreversible damage has occurred. In this
thesis, the focus shifts from imaging to biological biomarkers for earlier detection.
Using synovial fluid biomarkers, this work explores the potential for deep machine
learning models to accurately classify early stages of OA, thereby enabling timely
interventions to prevent disease progression. In addition, a user-friendly web appli cation was developed to assist practitioners in making real-time diagnoses. Based
on preliminary results, deep learning approaches, particularly those involving neu ral networks, can effectively differentiate between stages of OA, offering a promising
tool for diagnosis and treatment. These findings suggest significant potential for
improving diagnostic accuracy and, consequently, treatment outcomes in veterinary
medicine.
Beskrivning
Ämne/nyckelord
Deep Machine learning, engineering, biomarkers, osteoarthritis, diagno sis, treatment