Differentiation of Native Vertebral Osteomyelitis : A Comprehensive Review of Imaging Techniques and Future Applications

Native vertebral osteomyelitis, also termed spondylodiscitis, is an antibiotic-resistant disease that requires long-term treatment. Without proper treatment, NVO can lead to severe nerve damage or even death. Therefore, it is important to accurately diagnose the cause of NVO, especially in spontaneous cases. Infectious NVO is characterized by the involvement of 2 adjacent vertebrae and intervertebral discs, and common infectious agents include Staphylococcus aureus, Mycobacterium tuberculosis, Brucella abortus, and fungi. Clinical symptoms are generally nonspecific, and early diagnosis and appropriate treatment can prevent irreversible sequelae. Advances in pathologic histologic imaging have led physicians to look more forward to being able to differentiate between tuberculous and septic spinal discitis. Therefore, research in identifying and differentiating the imaging features of these 4 common NVOs is essential. Due to the diagnostic difficulties, clinical and radiologic diagnosis is the mainstay of provisional diagnosis. With the advent of the big data era and the emergence of convolutional neural network algorithms for deep learning, the application of artificial intelligence (AI) technology in orthopedic imaging diagnosis has gradually increased. AI can assist physicians in imaging review, effectively reduce the workload of physicians, and improve diagnostic accuracy. Therefore, it is necessary to present the latest clinical research on NVO and the outlook for future AI applications.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:30

Enthalten in:

Medical science monitor : international medical journal of experimental and clinical research - 30(2024) vom: 31. März, Seite e943168

Sprache:

Englisch

Beteiligte Personen:

Zhu, Weijian [VerfasserIn]
Zhou, Sirui [VerfasserIn]
Zhang, Jinming [VerfasserIn]
Li, Li [VerfasserIn]
Liu, Pin [VerfasserIn]
Xiong, Wei [VerfasserIn]

Links:

Volltext

Themen:

Anti-Bacterial Agents
Journal Article
Review

Anmerkungen:

Date Completed 01.04.2024

Date Revised 05.04.2024

published: Electronic

Citation Status MEDLINE

doi:

10.12659/MSM.943168

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM370450191