arXiv:2408.07349v1 Announce Type: cross
Abstract: The increasing prevalence of retinal diseases poses a significant challenge to the healthcare system, as the demand for ophthalmologists surpasses the available workforce. This imbalance creates a bottleneck in diagnosis and treatment, potentially delaying critical care. Traditional methods of generating medical reports from retinal images rely on manual interpretation, which is time-consuming and prone to errors, further straining ophthalmologists’ limited resources. This thesis investigates the potential of Artificial Intelligence (AI) to automate medical report generation for retinal images. AI can quickly analyze large volumes of image data, identifying subtle patterns essential for accurate diagnosis. By automating this process, AI systems can greatly enhance the efficiency of retinal disease diagnosis, reducing doctors’ workloads and enabling them to focus on more complex cases. The proposed AI-based methods address key challenges in automated report generation: (1) Improved methods for medical keyword representation enhance the system’s ability to capture nuances in medical terminology; (2) A multi-modal deep learning approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports; (3) Techniques to enhance the interpretability of the AI-based report generation system, fostering trust and acceptance in clinical practice. These methods are rigorously evaluated using various metrics and achieve state-of-the-art performance. This thesis demonstrates AI’s potential to revolutionize retinal disease diagnosis by automating medical report generation, ultimately improving clinical efficiency, diagnostic accuracy, and patient care. [https://github.com/Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation]
The Role of Artificial Intelligence in Automating Medical Report Generation for Retinal Images
The increasing prevalence of retinal diseases presents a significant challenge to the healthcare system, as the demand for ophthalmologists exceeds the available workforce. This creates a bottleneck in diagnosis and treatment, leading to potential delays in critical care. In this context, the use of Artificial Intelligence (AI) shows promise in automating medical report generation for retinal images, thereby improving the efficiency of diagnosis and reducing the workload of doctors.
One of the primary advantages of AI is its ability to quickly analyze large volumes of image data and identify subtle patterns that are essential for accurate diagnosis. By automating the process of medical report generation, AI systems can significantly enhance the efficiency of diagnosing retinal diseases. This automation enables doctors to focus on more complex cases and allocate their limited resources more effectively.
This thesis explores the potential of AI in revolutionizing retinal disease diagnosis by automating medical report generation. The proposed AI-based methods address several key challenges in this regard:
- Improved methods for medical keyword representation: By enhancing the system’s ability to capture nuances in medical terminology, these methods improve the accuracy of AI-generated medical reports. This is crucial in ensuring that the reports accurately reflect the nuances and complexities of retinal diseases.
- Multi-modal deep learning approach: This approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports. By considering both the visual information from the retinal images and the textual information from medical keywords, the AI system can generate more accurate and informative reports.
- Techniques to enhance interpretability: It is essential for AI-based report generation systems to be transparent and interpretable in a clinical setting. This fosters trust and acceptance among clinicians, enabling them to understand and validate the generated reports. By incorporating techniques for visual explanation, the proposed methods enhance the interpretability of the AI system.
The evaluation of these AI-based methods using various metrics demonstrates their state-of-the-art performance. By leveraging AI, retinal disease diagnosis can be transformed, leading to improved clinical efficiency, diagnostic accuracy, and patient care.
The Multidisciplinary Nature and Relation to Multimedia Information Systems
The concept of automating medical report generation for retinal images through AI is a prime example of the multidisciplinary nature of multimedia information systems. This field combines aspects of computer science, medical imaging, and artificial intelligence to develop solutions that efficiently handle and process multimedia data, such as images and videos.
Multimedia information systems have evolved to meet the increasing demand for efficient management and analysis of diverse types of data. In the case of retinal images, AI-based systems leverage deep learning techniques to extract relevant features and patterns, enabling accurate diagnosis and automated report generation.
Additionally, the integration of AI with retinal image analysis aligns with developments in the broader fields of artificial reality, augmented reality, and virtual realities. These fields aim to create immersive and interactive experiences by combining virtual and real-world elements.
The application of AI in retinal disease diagnosis can contribute to the development of augmented reality systems, where AI-generated medical reports are overlaid directly onto the retinal images. This would provide ophthalmologists with real-time, context-specific information during diagnosis and treatment, enhancing their decision-making process.
In summary, the use of AI in automating medical report generation for retinal images has the potential to revolutionize retinal disease diagnosis. By addressing key challenges and leveraging multidisciplinary concepts from multimedia information systems, artificial reality, augmented reality, and virtual realities, AI systems can enhance clinical efficiency, diagnostic accuracy, and patient care in ophthalmology.