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| <i class="fas fa-eye"></i>EyeUnit.ai | |
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| <p class="affiliation"> | |
| sami@eyeunit.ai | |
| </p> | |
| <h1 style="font-size: 2.4em;">FERMED: Advanced Vision-Language Models for Medical Diagnosis</h1> | |
| <p class="authors">Sami Halawa</p> | |
| </div> | |
| <div class="abstract"> | |
| <p> | |
| <strong>Abstract:</strong> This paper introduces FERMED, a novel framework for medical diagnosis leveraging vision-language models (VLMs). We present FERMED-3-VISION-16K, a specialized VLM for glaucoma diagnosis, trained using a detailed two-phase approach. Initially, a pre-trained VLM generates preliminary image descriptions, which are subsequently refined by expert ophthalmologists. The model is then fine-tuned on a dataset of 100,000 eye fundus images using a meticulously crafted Chain-of-Thought (CoT) prompt to encourage structured diagnostic reasoning. Furthermore, we propose the concept of FERMED-PRO-900B, a large-scale multimodal model designed for comprehensive medical diagnosis across numerous specialties. This model, trained on an extensive dataset encompassing images, text, lab results, and patient histories, aims to provide near-human-level diagnostic capabilities. This work outlines the potential of the FERMED framework to significantly enhance diagnostic accuracy, efficiency, and accessibility within the healthcare landscape. | |
| </p> | |
| </div> | |
| <div class="keywords"> | |
| <p><strong>Keywords:</strong> Artificial Intelligence, Vision-Language Models, Medical Diagnosis, Glaucoma, Deep Learning, Chain-of-Thought, Multimodal Learning, Healthcare, Ophthalmology, Diagnostic Imaging, Medical AI, Large Language Models.</p> | |
| </div> | |
| <div class="section"> | |
| <h2>1. Introduction</h2> | |
| <p>The convergence of artificial intelligence (AI) and medical imaging has ushered in a new era of diagnostic possibilities. Vision-Language Models (VLMs), which integrate visual understanding with natural language processing, are at the forefront of this transformation, offering unprecedented capabilities for analyzing and interpreting medical images [1, 2]. This paper details the development of the FERMED framework, beginning with FERMED-3-VISION-16K, a specialized VLM for glaucoma diagnosis, and conceptualizing FERMED-PRO-900B, a large-scale multimodal model for broader medical applications.</p> | |
| <p>Glaucoma, a progressive optic neuropathy, is a leading cause of irreversible blindness worldwide [3]. Early detection and intervention are crucial to prevent vision loss, and diagnosis relies on the integration of structural assessments from Optical Coherence Tomography (OCT) and fundus photography, along with functional evaluations from visual field testing. Traditional diagnostic workflows often require significant expert interpretation, which can be time-consuming and resource-intensive. To address these challenges, the FERMED-3-VISION-16K aims to automate image analysis and provide detailed diagnostic insights by leveraging the power of VLMs and advanced reasoning strategies.</p> | |
| <p>Building upon this foundation, the concept of FERMED-PRO-900B is introduced, a visionary model poised to revolutionize medical diagnosis across various specialties. This model is envisioned as a transformative AI system capable of synthesizing diverse medical data, including images, text reports, laboratory results, and patient histories, to provide near-human-level diagnostic accuracy and reasoning. This paper will explore the methodologies, potential impacts, and challenges associated with both FERMED-3-VISION-16K and FERMED-PRO-900B, illustrating their capabilities and outlining their future implications for healthcare.</p> | |
| </div> | |
| <div class="page-break"></div> | |
| <div class="section"> | |
| <h2>2. FERMED-3-VISION-16K: A Specialized VLM for Glaucoma</h2> | |
| <p>FERMED-3-VISION-16K is designed to automate the analysis of ophthalmological images and provide expert-level assessments of glaucoma. It utilizes a two-phase training approach that combines the strengths of pre-trained VLMs with expert refinement and a rigorous Chain-of-Thought (CoT) reasoning framework.</p> | |
| <h3>2.1. Methodology</h3> | |
| <p>The development of FERMED-3-VISION-16K involves a two-phase approach:</p> | |
| <h4>2.1.1. Phase 1: Initial Image Description Generation</h4> | |
| <p>This initial phase utilizes existing pre-trained VLMs to generate descriptive texts for the 100,000 eye fundus images used in the study. Models like <a href="https://deepmind.google/technologies/gemini/#introduction">Gemini-2.0</a>, known for their general image understanding and text generation abilities, are leveraged to provide preliminary annotations. This provides a starting point for further refinement, although their medical accuracy may be limited.</p> | |
| <h4>2.1.2. Phase 2: Expert-Guided Refinement and Fine-Tuning</h4> | |
| <p>In this phase, a base open-source language model, such as <a href="https://huggingface.co/microsoft/phi-3-mini-4k-instruct">Phi-3.5-mini</a>, is fine-tuned on a curated dataset of images and expert-refined descriptions. A key element in this phase is the use of a precisely engineered Chain-of-Thought (CoT) prompt that guides both the expert refinement process and the model's reasoning during inference.</p> | |
| <ul> | |
| <li><strong>Dataset Creation:</strong> A dataset of 100,000 eye fundus images is compiled, each paired with its expert-refined description. The dataset is divided into training, validation, and testing subsets to ensure robust model training and evaluation.</li> | |
| <li><strong>CoT Prompt:</strong> The CoT prompt is designed to elicit a structured diagnostic process from the model (and guide the ophthalmologists during the refinement process). It includes steps for individual image analysis, reasoning based on findings, and providing a possible diagnosis. This prompt is provided verbatim below: | |
| <blockquote> | |
| <p> | |
| "You are an expert ophthalmologist specializing in glaucoma diagnosis and management. You will be provided with one or more medical images, which may include Optical Coherence Tomography (OCT) scans, fundus photographs, and visual field test results. Your task is to analyze these images carefully and provide a step-by-step analysis using the Chain-of-Thought (CoT) method. This includes identifying relevant features, explaining your reasoning, and offering a possible diagnosis or differential diagnosis with an emphasis on accuracy and medical rationale. Follow these instructions exactly: | |
| </p> | |
| <p> | |
| <strong>I. Individual Image Analysis (For each image provided):</strong> | |
| </p> | |
| <p> | |
| <strong>Optical Coherence Tomography (OCT):</strong> | |
| </p> | |
| <ul> | |
| <li>Retinal Nerve Fiber Layer (RNFL): Analyze the RNFL thickness, particularly the TSNIT (Temporal, Superior, Nasal, Inferior, Temporal) profile. Note any localized thinning or deviations from the normative database. Quantify the degree of abnormality (mild, moderate, severe).</li> | |
| <li>Ganglion Cell Layer (GCL) / Ganglion Cell Complex (GCC): Examine the thickness of the GCL/GCC, especially in the macular region. Note any thinning or localized loss. Quantify the degree of abnormality.</li> | |
| <li>Optic Nerve Head (ONH): Evaluate the cup-to-disc ratio, rim area, vertical rim thickness, disc hemorrhages, and Bruch's membrane opening-minimum rim width (BMO-MRW). Identify any abnormalities.</li> | |
| <li>Artifacts: Identify any potential image artifacts (segmentation errors, media opacities, poor scan quality), and state how this may impact the interpretation. If image quality is insufficient, state clearly.</li> | |
| </ul> | |
| <p> | |
| <strong>Fundus Photograph:</strong> | |
| </p> | |
| <ul> | |
| <li>Optic Disc: Describe the optic disc for cupping (size and shape), cup-to-disc ratio, disc size, rim appearance (thinning, notching, pallor), disc hemorrhages, vessel changes, and peripapillary atrophy.</li> | |
| <li>Retinal Nerve Fiber Layer: Describe the visibility of the RNFL, noting any localized defects, vessel changes, or signs of thinning.</li> | |
| </ul> | |
| <p> | |
| <strong>Visual Field:</strong> | |
| </p> | |
| <ul> | |
| <li>Reliability: Assess fixation losses, false positives, and false negatives. Determine if the test is reliable. Note if it is not, and explain why.</li> | |
| <li>Defects: Identify and describe any visual field defects. Include description of their location, pattern (arcuate, nasal step, paracentral), and severity (mild, moderate, severe). Also, consider if there is a generalized depression.</li> | |
| <li>Indices: Provide values for Mean Deviation (MD), Pattern Standard Deviation (PSD), and Visual Field Index (VFI).</li> | |
| <li>If applicable: note any evidence of central vision loss.</li> | |
| <li>Explain if the test used was 10-2 or 24-2/30-2 (or other).</li> | |
| </ul> | |
| <p> | |
| <strong>II. Reasoning (Chain-of-Thought):</strong> | |
| </p> | |
| <ul> | |
| <li>Connect Findings: For each modality (OCT, fundus, visual field), explain the reasoning behind each identified feature. Why is each finding normal or abnormal? Do not simply list findings, explain their significance and what they mean in the context of glaucoma.</li> | |
| <li>Glaucoma Patterns: Link identified findings to known glaucomatous patterns of structural and functional loss. Are they typical or atypical for glaucoma?</li> | |
| <li>Structure-Function Correlation: If multiple images are present, explain how they relate to each other. Specifically, address whether structural changes correlate with functional loss. Do the findings from OCT correlate with the visual field defects?</li> | |
| <li>Conflicting Information: If there are contradictory findings, explain them and their potential causes.</li> | |
| </ul> | |
| <p> | |
| <strong>III. Possible Diagnosis and Conclusion:</strong> | |
| </p> | |
| <ul> | |
| <li>Possible Diagnosis: Based on your analysis and reasoning, offer a possible diagnosis or a differential diagnosis, NOT a definitive one.</li> | |
| <li>Glaucoma Classification: If glaucoma is suspected, specify if it appears to be early, moderate, or advanced, and explain your reasoning.</li> | |
| <li>Differential Diagnosis: Clearly identify conditions that may also account for the findings, including other types of glaucoma (normal tension, angle closure, etc.), and other optic neuropathies.</li> | |
| <li>Confidence: Explicitly state your level of confidence in your conclusion based on the available evidence.</li> | |
| <li>Recommendations: Indicate if further testing, a repeat exam, or consultation with a glaucoma specialist are needed.</li> | |
| <li>Medical Rationale: Clearly explain the rationale for your diagnostic conclusion.</li> | |
| </ul> | |
| <p> | |
| <strong>IV. Output Format:</strong> | |
| </p> | |
| <ul> | |
| <li>Present your analysis in a structured format, labeling each image type and the corresponding findings. Use medical terminology.</li> | |
| <li>Keep your language concise, objective, and specific. Prioritize accuracy and precision.</li> | |
| <li>For every quantitative analysis, ensure it is as accurate as possible. Use numerical values.</li> | |
| <li>Present a summary conclusion including the most likely diagnosis and further recommendations.</li> | |
| <li>Do not offer treatment plans.</li> | |
| </ul> | |
| <p> | |
| <strong>Important Notes:</strong> | |
| </p> | |
| <ul> | |
| <li>Do not offer treatment plans, this is outside the scope of this exercise.</li> | |
| <li>Be as specific and precise as possible, do not provide vague answers, focus on medical terminology.</li> | |
| <li>Prioritize accuracy over speed, but be as concise as possible while remaining precise.</li> | |
| <li>If the provided images are not of sufficient quality to perform analysis, please state it clearly.</li> | |
| <li>Your output should be clinically useful and informative for an ophthalmologist.</li> | |
| </ul> | |
| </p> | |
| </blockquote> | |
| </li> | |
| <li><strong>Base Model Selection:</strong> Phi-3.5-mini, known for its strong performance in natural language tasks and its open-source nature, was selected as the base model for fine-tuning. Its compact size is also an advantage.</li> | |
| <li><strong>Fine-tuning Process:</strong> The base model is fine-tuned using the prepared dataset and CoT prompt. The training aims to optimize the model's parameters for accurate image analysis and generation of detailed diagnostic reports, adhering to the specified CoT format.</li> | |
| </ul> | |
| <div class="figure"> | |
| <div class="mermaid"> | |
| graph TD | |
| A[Fundus Image/OCT/Visual Field] --> B(Image Encoder); | |
| B --> C(Image Features); | |
| C --> D(Fusion Module); | |
| E[CoT Prompt] --> F(Text Encoder); | |
| F --> G(Prompt Features); | |
| G --> D; | |
| D --> H(Language Model - Phi-3.5-mini); | |
| H --> I(Diagnostic Report); | |
| </div> | |
| <div class="caption">Figure 1: FERMED-3-VISION-16K Model Architecture</div> | |
| </div> | |
| </div> | |
| <div class="page-break"></div> | |
| <div class="section"> | |
| <h3>2.2. Evaluation Metrics</h3> | |
| <p> | |
| The performance of FERMED-3-VISION-16K is assessed using a comprehensive set of metrics to evaluate diagnostic accuracy, completeness of analysis, reasoning coherence, adherence to formatting, and clinical utility: | |
| </p> | |
| <ul> | |
| <li><strong>Diagnostic Accuracy:</strong> Assessed by comparing the model's diagnoses with ground truth diagnoses from expert ophthalmologists.</li> | |
| <li><strong>Completeness of Analysis:</strong> Evaluates whether all relevant features are identified and thoroughly analyzed by the model.</li> | |
| <li><strong>Coherence and Clarity of Reasoning:</strong> Measures the logical flow and medical validity of the CoT-based reasoning.</li> | |
| <li><strong>Adherence to Output Format:</strong> Ensures the model consistently follows the specified format in its diagnostic reports.</li> | |
| <li><strong>Standard NLP Metrics:</strong> BLEU, ROUGE, and METEOR scores are used to quantify the quality of the generated text descriptions.</li> | |
| <li><strong>Clinical Utility:</strong> Expert ophthalmologists evaluate the clinical usefulness and interpretability of the model's reports in real-world practice. | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h2>3. Expanding the FERMED Framework: Applications Beyond Glaucoma</h2> | |
| <p>The foundational principles of FERMED-3-VISION-16K, which combine specialized image analysis with expert-guided knowledge and structured reasoning, can be effectively extended to other medical specialties and diagnostic tasks. By curating specialized datasets and adapting the CoT prompt, similar models can be developed to analyze various medical images and provide expert-level diagnostic insights in numerous domains. The modular nature of the FERMED framework makes it a versatile solution for diverse medical applications.</p> | |
| <h3>3.1. Potential Applications</h3> | |
| <p> | |
| This section outlines some areas of medicine where FERMED-like models could be transformative: | |
| </p> | |
| <ul> | |
| <li><strong>Diabetic Retinopathy:</strong> Analyzing fundus photographs to detect and classify diabetic retinopathy stages, thus reducing the risk of vision loss due to diabetic complications [4].</li> | |
| <li><strong>Age-related Macular Degeneration (AMD):</strong> Assessing OCT scans and fundus images for signs of AMD, enabling early intervention and reducing the risk of severe vision impairment [5].</li> | |
| <li><strong>Lung Cancer:</strong> Analyzing chest X-rays and CT scans for early detection of lung nodules and other abnormalities, which is crucial for improving survival rates in lung cancer [6].</li> | |
| <li><strong>Skin Cancer:</strong> Examining dermoscopic images to identify and classify skin lesions, aiding in the early detection of melanoma and other skin malignancies [7].</li> | |
| <li><strong>Breast Cancer:</strong> Utilizing mammograms to detect and characterize breast abnormalities, improving early breast cancer diagnosis rates and patient outcomes [8].</li> | |
| </ul> | |
| </div> | |
| <div class="page-break"></div> | |
| <div class="section"> | |
| <h2>4. FERMED-PRO-900B: A Vision for Comprehensive Medical Intelligence</h2> | |
| <p>Moving beyond specialized diagnostic applications, the FERMED framework envisions FERMED-PRO-900B, a large-scale multimodal AI system designed for comprehensive medical intelligence. This conceptual model is designed to integrate diverse medical information streams to offer a holistic view of a patient's health status, thereby transforming the diagnostic process across various specialties.</p> | |
| <h3>4.1. Model Architecture and Training</h3> | |
| <p>FERMED-PRO-900B is conceptualized as a 900-billion parameter model trained on a vast array of medical data. This includes but is not limited to:</p> | |
| <ul> | |
| <li>Millions of medical images from diverse modalities (X-rays, CT scans, MRI scans, fundus photographs, dermoscopic images, etc.) across various specialties.</li> | |
| <li>Comprehensive text-based reports: radiology reports, pathology reports, clinical notes, discharge summaries, and more.</li> | |
| <li>Extensive laboratory results: blood tests, urine tests, genetic tests, and other pertinent lab data.</li> | |
| <li>Detailed patient histories, including electronic health records (EHRs) containing demographics, medical history, family history, and other relevant information.</li> | |
| <li>A wide range of medical literature: research papers, textbooks, clinical guidelines, and diverse sources of medical knowledge.</li> | |
| </ul> | |
| <p>The model would employ advanced multimodal learning techniques to integrate information from these diverse sources, enabling a nuanced understanding of each patient case. Training this model would demand high computational resources and sophisticated algorithms to optimize the model's parameters for accurate and comprehensive diagnoses. | |
| </p> | |
| <h3>4.2. Diagnostic Capabilities</h3> | |
| <p>With its expansive training, FERMED-PRO-900B is envisioned to handle various diagnostic tasks:</p> | |
| <ul> | |
| <li>High-precision image analysis, including the identification and characterization of abnormalities across all image modalities.</li> | |
| <li>Advanced text interpretation, efficiently extracting pertinent information from clinical reports and notes.</li> | |
| <li>Seamless integration of diverse data sources—images, text, lab results, and patient histories—to form a complete diagnostic picture.</li> | |
| <li>Robust differential diagnosis, considering multiple possible diagnoses, each with a ranked probability of occurrence.</li> | |
| <li>Providing detailed explanations for its diagnostic conclusions, using a CoT-like approach to ensure transparency and clinical validation.</li> | |
| <li>Personalized treatment recommendations, suggesting specific tests, consultations, and treatment options based on the unique case profile and medical history of each patient (<em>Note: specific treatment plans are out of scope, but directionality would be provided</em>)</li> | |
| </ul> | |
| <div class="figure"> | |
| <div class="mermaid"> | |
| graph TD | |
| A[Phase 1: Pre-training with Existing VLMs] --> B(Image-to-Text Generation with Gemini-2.0); | |
| B --> C(Expert Refinement of Generated Descriptions); | |
| C --> D[Phase 2: Fine-tuning with Specialized Dataset and CoT Prompting]; | |
| D --> E(Dataset Creation - 100,000 Images with Refined Descriptions); | |
| E --> F(Base Model Selection - Phi-3.5-mini); | |
| F --> G(Prompt Engineering - CoT Prompt); | |
| G --> H(Fine-tuning Process); | |
| H --> I(Model Evaluation); | |
| I --> J(Deployment & Clinical Validation); | |
| </div> | |
| <div class="caption">Figure 2: Project Workflow for FERMED-3-VISION-16K</div> | |
| </div> | |
| </div> | |
| <div class="page-break"></div> | |
| <div class="section"> | |
| <h3>4.3. Anticipated Impact and Vision</h3> | |
| <p> | |
| The realization of FERMED-PRO-900B could transform healthcare delivery and medical practice with several key outcomes: | |
| </p> | |
| <ul> | |
| <li><strong>Enhanced Diagnostic Accuracy:</strong> The model is designed to reduce diagnostic errors and improve patient outcomes through its sophisticated analytical capabilities.</li> | |
| <li><strong>Increased Efficiency:</strong> By streamlining diagnostic workflows, the model would save valuable time for medical professionals, allowing for faster treatment decisions.</li> | |
| <li><strong>Expanded Accessibility:</strong> The system will enhance access to expert-level medical knowledge, especially in remote or underserved areas. This can help bridge healthcare disparities and reduce inequalities in access to quality care.</li> | |
| <li><strong>Acceleration of Medical Research:</strong> The model can analyze large datasets to uncover patterns and insights that would be difficult for humans alone. This could greatly accelerate the progress of medical research and lead to new and innovative treatments.</li> | |
| <li><strong>Personalized Medicine:</strong> The model has the capability to personalize treatment based on each patient’s unique medical history and characteristics, thus maximizing treatment effectiveness.</li> | |
| </ul> | |
| </div> | |
| <div class="section"> | |
| <h3>4.4. Challenges and Ethical Considerations</h3> | |
| <p> | |
| The development of such a complex model presents significant challenges, requiring careful attention to ethical considerations: | |
| </p> | |
| <ul> | |
| <li><strong>Data Acquisition and Curation:</strong> The massive medical dataset would require careful gathering, annotation, and quality control. Data biases must be addressed to ensure fairness.</li> | |
| <li><strong>Computational Resources:</strong> Training a 900-billion parameter model would require immense resources, and efficient, cost-effective solutions must be identified.</li> | |
| <li><strong>Model Interpretability:</strong> Transparency in the decision-making process is crucial to foster trust and facilitate clinical acceptance. Further advancements in explainable AI (XAI) are needed to make the reasoning process transparent to clinicians.</li> | |
| <li><strong>Data Privacy and Security:</strong> Strict measures are needed to protect sensitive patient data, complying with data privacy regulations. Security protocols must be robust and frequently updated to prevent breaches.</li> | |
| <li><strong>Bias and Fairness:</strong> There is an urgent need to address inherent biases in the training data, ensuring equitable performance across different demographics and groups.</li> | |
| <li><strong>Regulatory Approval and Validation:</strong> Regulatory pathways need to be established to ensure the model's safety and efficacy, and rigorous clinical trials are needed before real-world deployment.</li> | |
| </ul> | |
| <p>These challenges will require close collaboration between AI researchers, medical professionals, policymakers, and ethicists. Continuous evaluation and transparent reporting of the model’s performance and limitations are paramount.</p> | |
| </div> | |
| <div class="section"> | |
| <h2>5. Conclusion</h2> | |
| <p> | |
| The FERMED framework, represented by FERMED-3-VISION-16K and envisioned in FERMED-PRO-900B, offers transformative capabilities for the future of medical diagnosis. FERMED-3-VISION-16K demonstrates how specialized VLMs can provide expert-level analysis in specific medical domains, while FERMED-PRO-900B embodies a visionary potential for a comprehensive, multimodal system capable of transforming clinical practice through its unparalleled diagnostic capabilities. While many challenges exist, the pursuit of these advanced AI solutions has the potential to revolutionize healthcare, making expert medical knowledge more accurate, accessible, and efficient, ultimately leading to better patient outcomes and an evolution in the practice of medicine. | |
| </p> | |
| </div> | |
| <div class="page-break"></div> | |
| <div class="section references"> | |
| <h2>6. References</h2> | |
| <ol> | |
| <li><a href="https://arxiv.org/abs/2303.08774">Achiam, J., Adler, S., et al. (2023). GPT-4 Technical Report. <em>arXiv preprint arXiv:2303.08774</em>.</a></li> | |
| <li><a href="https://arxiv.org/abs/2301.12597">Li, J., Li, D., Xiong, C., & Hoi, S. (2023). BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. <em>arXiv preprint arXiv:2301.12597</em>.</a></li> | |
| <li><a href="https://pubmed.ncbi.nlm.nih.gov/25028723/">Weinreb, R. N., Aung, T., & Medeiros, F. A. (2014). The pathophysiology and treatment of glaucoma: a review. <em>JAMA</em>, <em>311</em>(18), 1901-1911.</a></li> | |
| <li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4906449/">Ting, D. S. W., et al. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. <em>JAMA</em>, <em>318</em>(22), 2211-2223.</a></li> | |
| <li><a href="https://www.nature.com/articles/s41591-018-0107-6">De Fauw, J., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. <em>Nature Medicine</em>, <em>24</em>(9), 1342-1350.</a></li> | |
| <li><a href="https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30165-7/fulltext">Ardila, D., et al. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. <em>Nature Medicine</em>, <em>25</em>(6), 954-961.</a></li> | |
| <li><a href="https://www.nature.com/articles/nature21056">Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. <em>Nature</em>, <em>542</em>(7639), 115-118.</a></li> | |
| <li><a href="https://www.nature.com/articles/s41586-019-1758-z">McKinney, S. M., et al. (2020). International evaluation of an AI system for breast cancer screening. <em>Nature</em>, <em>577</em>(7788), 89-94.</a></li> | |
| </ol> | |
| </div> | |
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