โก Quick Summary
This scoping review highlights the transformative role of Artificial Intelligence (AI) in enhancing Optical Coherence Tomography (OCT) for disease detection and cancer diagnosis. The integration of AI technologies has led to improved diagnostic precision and real-time clinical support, paving the way for better patient outcomes.
๐ Key Details
- ๐ Technology: Optical Coherence Tomography (OCT)
- ๐ค AI Techniques: Machine Learning (ML) and Deep Learning (DL)
- ๐ฅ Applications: Ophthalmology, Cardiology, Dermatology, Oncology
- ๐ Key Findings: Enhanced disease detection and improved patient healing outcomes
๐ Key Takeaways
- ๐ OCT technology has revolutionized disease detection across multiple medical fields.
- ๐ก AI integration allows for automatic disease detection and real-time image analysis.
- ๐ Convolutional neural networks show strong capabilities in distinguishing normal from abnormal tissues.
- ๐ Challenges include model validity and dataset biases that need addressing.
- ๐ Future research should focus on optimizing AI models for better clinical implementation.
- ๐ฉบ Improved outcomes for retinal conditions, cardiovascular issues, and cancer detection.
- ๐ Potential for AI and OCT to significantly enhance healthcare delivery and patient care.
๐ Background
Optical Coherence Tomography (OCT) is a non-invasive imaging technology that provides high-resolution images, making it invaluable in various medical fields such as ophthalmology, cardiology, and oncology. Despite its advantages, medical professionals often encounter challenges in interpreting complex images and achieving consistent accuracy. The advent of Artificial Intelligence (AI) offers promising solutions to these challenges, enhancing the capabilities of OCT in clinical settings.
๐๏ธ Study
This scoping review investigates the applications of OCT technology in the medical sector and explores how AI enhances its clinical performance. By analyzing peer-reviewed studies, the research highlights the advancements in automatic disease detection and real-time clinical support facilitated by AI technologies.
๐ Results
The integration of AI with OCT technology has led to significant improvements in disease detection, particularly in retinal conditions, cardiovascular problems, and epithelial cancers. AI systems, particularly those utilizing convolutional neural networks, have demonstrated a robust ability to accurately classify diseases and detect tumor margins, enabling earlier cancer detection and improved treatment accuracy.
๐ Impact and Implications
The findings from this study underscore the substantial potential of AI in transforming healthcare delivery. By enhancing the diagnostic capabilities of OCT, AI can lead to better patient outcomes and more effective treatment solutions in cancer medicine. The integration of these technologies could significantly improve clinical decision-making and patient care across various medical disciplines.
๐ฎ Conclusion
This scoping review illustrates the remarkable advancements brought about by the integration of AI with OCT technology. As we move forward, optimizing AI models and addressing dataset biases will be crucial for maximizing the benefits of these innovations in clinical practice. The future of healthcare looks promising with the continued development of AI-driven solutions for disease detection and treatment.
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Artificial intelligence in advancing optical coherence tomography for disease detection and cancer diagnosis: A scoping review.
Abstract
BACKGROUND: OCT (Optical Coherence Tomography) functions as a high-resolution non-invasive imaging technology that serves multiple applications within ophthalmology and cardiology and dermatology as well as oncology. The adoption of OCT technology showed major diagnostic progress but medical professionals still face obstacles in complex picture interpretation as well as inconsistent accuracy rates. The implementation of Artificial Intelligence (AI) systems that use machine learning (ML) and deep learning (DL) functions has enabled OCT to analyze images automatically while offering better diagnostic precision.
METHODS: The study investigates medical sector applications of OCT technology and scrutinizes how AI facilitates improved clinical performance of OCT. Research conducted on peer-reviewed studies analyzed how AI improves OCT technology by enabling automatic disease detection and real-time image modification and clinical support functions.
RESULTS: The medical field underwent a revolutionary change due to OCT technology that enables improved detection of diseases alongside better patient healing outcomes for retinal conditions and cardiovascular problems and epithelial cancer cases. Real-time surgical oncology decision-making occurs through AI by improving both OCT classification of diseases and detection of tumor margins simultaneously. Convolutional neural networks under artificial intelligence control exhibit strong ability to detect normal and abnormal tissues thus enabling earlier cancer detection with improved medical treatment accuracy. Solving active problems with two main issues stands as the key requirement for progress as clinicians work with uncertain model validity and incomplete dataset similarities in clinical settings.
CONCLUSIONS: Clinical procedures benefit from important improvements when OCT networks integrate AI command systems in their operations. Future research demands model optimization for AI technology together with the solution of dataset biases and better implementation in medical settings. AI integration with OCT technology points to substantial potential for healthcare detection developments as well as treatment solutions for individual patients in cancer medicine.
Author: [‘Alikarami M’, ‘Faraj TA’, ‘Hama NH’, ‘Hosseini AS’, ‘Habibi P’, ‘Samiei Mosleh I’, ‘Alavi M’, ‘Kashani M’, ‘Aminnezhad S’]
Journal: Eur J Surg Oncol
Citation: Alikarami M, et al. Artificial intelligence in advancing optical coherence tomography for disease detection and cancer diagnosis: A scoping review. Artificial intelligence in advancing optical coherence tomography for disease detection and cancer diagnosis: A scoping review. 2025; 51:110188. doi: 10.1016/j.ejso.2025.110188