کاربردهای هوش مصنوعی در جراحی اعصاب دامپزشکی : مرور سیستماتیک و چشم انداز های آینده
کد: G-1680
نویسندگان: Hamidreza Fattahian *, Mohammadmahdi Khajoueinejad ℗
زمان بندی: زمان بندی نشده!
برچسب: پردازش سیگنال های پزشکی
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Introduction :Veterinary neurosurgery faces challenges like interspecies anatomical variability, complex neuroanatomy, and limited access to neural structures. AI offers transformative potential by enhancing precision through advanced imaging, navigation systems, and minimally invasive techniques. While AI reduces surgical errors and improves outcomes via data-driven decisions, its adoption remains nascent due to species-specific anatomical diversity and resource limitations. This review systematically evaluates AI’s current and emerging applications in veterinary neurosurgery, spanning preoperative, intraoperative، and postoperative phases. Methods :A systematic review of 48 studies (2022–2023) was conducted using PubMed, Web of Science, Scopus, and IEEE Xplore. Keywords included AI/ML, neurosurgery, and veterinary species (dogs, cats, horses).Data extraction focused on AI applications, species, outcomes, and limitations. Key Results :1. Study Trends: 75% of studies published in 2023, with geographic focus in North America (35%), Europe (30%), and Asia (25%). 2. Pre-Surgical AI: - 92% accuracy in detecting canine brain tumors (Kim et al., 2022). - 88% sensitivity for equine spinal lesions (Spiteri et al., 2023). - 0.5mm precision in 3D neuroanatomy modeling (Johnson et al., 2023). 3. Intraoperative Innovations: - AI-guided navigation with 2mm error margins (Carreira et al., 2023). - 95% real-time critical structure detection (Robertson et al., 2022). - 30% reduction in surgery time via AI decision-support (Martinez-Pereira et al., 2023). 4. Post-Surgical Outcomes: - 87% sensitivity in detecting early complications (Wang et al., 2022). - 45% fewer complications and 35% better recovery with AI monitoring (Jeffery et al., 2023).5. Challenges: Data scarcity due to anatomical diversity, high costs for small clinics, and training gaps.6. Emerging Trends: Cross-species AI systems, 40% improved accuracy with AR integration (Gandini et al., 2022), and cloud-based data sharing. Discussion & ConclusionAI revolutionizes veterinary neurosurgery through: -Diagnostic Precision: 40% improvement over traditional methods (Banzato et al., 2023).- Surgical Workflow: 45% fewer errors and sub-millimeter planning accuracy.-Post-Operative Care: AI-driven tools reduce risks via early complication detection. Vision:Veterinary neurosurgery serves as a model for AI adoption in medicine. Success requires collaboration among veterinarians, engineers, and policymakers to balance innovation with ethics and accessibility.AI’s integration promises a future of safer, precise, and equitable veterinary care.
کلمات کلیدی
Artificial Intelligence, Veterinary Neurological Surgery
