MedAlgnosis: Tumor Teller 💊 💻

Website Link: https://medalgnosis.streamlit.app/

Details :

Team Member 1:

Name : Harshavardhan Bajoria

Country of Residence : India

College Name : Amity University Kolkata

Graduation Year: 2024

Experience Level : Student

Team Member 2:

Name : Tanisha Agarwal

Country of Residence : India

College Name : Manipal Institute of Technology, Manipal

Graduation Year: 2025

Experience Level : Student

Theme :

Creating a brighter future for brain health by leveraging cutting-edge technology and compassionate care. Unleash the potential of AI to drive impactful solutions and foster a healthier tomorrow for all.

Problem Statement:

The existing methods of diagnosing brain tumors through MRI scans often involve time-consuming and error-prone manual analysis by medical professionals. Additionally, there is a need for a secure and privacy-centric approach to handling sensitive patient data during the diagnostic process. The key points of the problem statement are as follows:

• Manual MRI Analysis: Current methods for diagnosing brain tumors using MRI scans rely heavily on manual analysis by medical experts. This process can be time-consuming, leading to delays in diagnosis and treatment planning.
• Accuracy and Efficiency: There is a demand for a more accurate and efficient tumor identification process to enable early detection and timely intervention, which significantly impacts patient outcomes and survival rates.
• Lack of Comprehensive Information: Patients often receive limited information about their specific brain tumor type, its characteristics, potential risks, and treatment options. Providing comprehensive insights is essential for informed decision-making and patient empowerment.
• Data Privacy Concerns: With increasing concerns about data privacy and security in healthcare, there is a critical need to ensure that patient data, including MRI scans, is handled securely and not stored for privacy reasons.
• Streamlining Appointment Booking: The current process of booking appointments with specialized doctors can be cumbersome and inefficient. An integrated system that facilitates seamless appointment scheduling and communication is essential for patient convenience.

Solution: 💡

In light of these challenges, Medalgnosis aims to create an intelligent and privacy-focused solution that revolutionizes brain tumor diagnosis using AI-driven analysis. By leveraging the power of Azure Custom Vision, the application seeks to empower patients with comprehensive tumor insights and personalized treatment recommendations, ultimately contributing to improved patient outcomes and enhanced brain health care.

• Medalgnosis is an innovative medical software application designed to revolutionize brain tumor diagnosis through AI-driven MRI analysis.
• Leveraging Azure Custom Vision's pre-trained machine learning model, the app accurately identifies Meningioma, Glioma, and Pituitary tumors from uploaded MRI scans.
• The platform ensures privacy by not storing any user data or MRI scans, safeguarding sensitive medical information through Streamlit.
• With a user-friendly interface built using Streamlit, patients can easily upload MRI scans and receive rapid and precise tumor identification results.
• Detailed insights about the detected tumor, including its characteristics, causes, effects, and potential treatments, are provided to enable informed decision-making.
• Personalized treatment recommendations based on the tumor type and stage empower patients to make the best healthcare choices.
• Medalgnosis streamlines appointment booking with specialized doctors through its integrated Azure Logic App, facilitating seamless communication.
• The app's focus on data privacy, accuracy, and efficiency contributes to early detection and timely intervention, improving patient outcomes and brain health care.

Tech Stack:

The following tech stacks have been used to create the application and deploy it.

• Python to build the application.
• Streamlit to create a responsive web application along with widgets.
• Streamlit Community Cloud to deploy the web application for anyone across the globe to access it.
• Microsoft Azure AI Custom Vision to get a computer vision model trained using our dataset and use it to predict the tumor type with the patient's MRI scan.
• Microsoft Azure Logic App to send emails to the patient, doctor on appointment booking.
• GitHub to host the source code, use the version control (collaboration history), pull requests and GitHub collaboration features to build efficiently with the teammates. It helps a lot to understand the changes and go back and forth if required to complete the software.

Methodology:

Installation Guide: ⬇️

First, install the following:

• Python

Then, follow this step-by-step process to run this application:

• Get your Azure subscription: https://azure.microsoft.com/en-in/free
• Create an Azure Custom Vision resource and train the model
• Create the Azure Logic App Resource and add HTTP/HTTPS Trigger and send email (V2) action with the body as input for the trigger function.
• Travel to the directory where you wish to store the project files using the cd command.
• Clone the repository in your local system.

git clone https://github.com/HVbajoria/MedAlgnosis

• Go to your project directory where all the files are present.

cd MedAlgnosis

• Install the required dependencies to run the project.

pip install -r requirements.txt

• Replace the endpoint and key with your Azure Custom Vision model resource endpoint and key in predictor.py.
• Replace the logic app URL with the Azure logic app URL of the trigger in predictor.py.
• Run the application

streamlit run about.py

• Enjoy the app!

Demo Video Link 🎥 https://youtu.be/ZwpHzr9rOmI

Website Link 🌐 : https://medalgnosis.streamlit.app/

Social Impact / Novelty:

Medalgnosis is a socially impactful and innovative solution that brings together AI technology, data privacy, and streamlined healthcare services to empower patients, advance medical practices, and improve brain health outcomes. Its emphasis on early detection, privacy, and informed decision-making holds the potential to positively impact the lives of countless individuals and contribute to the broader advancement of healthcare practices.

• Enhanced Brain Health Care Access: Medalgnosis brings cutting-edge AI technology to the field of brain tumor diagnosis, making it more accessible to a broader population. With rapid and accurate tumor identification, patients from diverse backgrounds can benefit from early detection and timely treatment, improving overall brain health care outcomes.
• Empowering Informed Decision-Making: By providing comprehensive tumor insights and personalized treatment recommendations, Medalgnosis empowers patients to actively participate in their healthcare journey. Informed patients can collaborate more effectively with medical professionals, leading to better treatment adherence and improved patient satisfaction.
• Privacy-Centric Approach: Medalgnosis sets a new standard for data privacy in medical applications. Its commitment to not storing any user data or MRI scans ensures patient information remains secure, addressing concerns about data breaches and confidentiality in healthcare.
• Time-Efficient Diagnosis: The use of AI-driven MRI analysis significantly reduces the time required for tumor identification compared to traditional manual methods. Expedited diagnosis allows medical professionals to make timely treatment decisions, potentially leading to improved patient outcomes and reduced healthcare costs.
• Encouraging Early Detection: Early detection of brain tumors is critical for successful treatment and increased survival rates. Medalgnosis' accurate and efficient identification of tumor types facilitates early intervention, potentially saving lives and improving the long-term prognosis for patients.
• Promoting Medical Advancement: The integration of Azure Custom Vision's pre-trained machine learning model into Medalgnosis represents a novel approach in the field of medical imaging and diagnosis. This innovation showcases the potential for AI technology to revolutionize healthcare practices and contribute to ongoing medical advancements.
• Improved Healthcare Collaboration: The seamless integration of Medalgnosis with Azure Logic App streamlines appointment booking and communication between patients and specialized doctors. This enhanced collaboration fosters better doctor-patient interactions and a smoother healthcare experience for all stakeholders.
• Public Health Awareness: By providing comprehensive insights into brain tumors and their implications, Medalgnosis contributes to public health awareness. Increased understanding of brain health and the importance of early diagnosis may encourage more individuals to undergo regular screenings, promoting overall brain health and well-being.

Future Scope:

Medalgnosis has significant future potential to evolve and improve brain tumor diagnosis and treatment. Through continuous innovation, integration of advanced technologies, and global collaborations, Medalgnosis can impact the lives of millions of individuals by fostering early detection, personalized care, and advancements in brain health research and treatment.

• Expanding Tumor Identification Capabilities: In the future, Medalgnosis can be enhanced to detect and identify a broader range of brain tumors beyond Meningioma, Glioma, and Pituitary tumors. The integration of additional pre-trained machine learning models can extend its capabilities to include other rare or complex tumor types, further improving diagnostic accuracy.
• Integration of Advanced Imaging Techniques: Medalgnosis can explore the incorporation of advanced imaging techniques, such as functional MRI (fMRI) and diffusion tensor imaging (DTI), to provide more detailed insights into tumor characteristics and potential impact on brain function. This would enable a more comprehensive understanding of the tumor's effects on the patient's overall health.
• Multi-Modal Analysis for Holistic Diagnosis: By integrating multiple imaging modalities and clinical data, Medalgnosis can adopt a multi-modal analysis approach for a more holistic and accurate diagnosis. The combination of MRI scans with other patient data, such as genetic profiles and medical history, can lead to a deeper understanding of tumor behavior and personalized treatment recommendations.
• Global Outreach and Collaboration: Medalgnosis can expand its reach to a global scale, collaborating with medical institutions and experts worldwide. This expansion would enable the platform to cater to a more diverse patient population, ensuring its benefits reach individuals from different geographic locations and socioeconomic backgrounds.
• Predictive Analytics for Treatment Outcomes: Leveraging historical treatment data, Medalgnosis can incorporate predictive analytics to assess the likelihood of treatment success for specific tumor types and patient profiles. This feature would enable medical professionals and patients to make well-informed decisions regarding treatment plans and potential outcomes.
• Integration with Electronic Health Records (EHRs): To enhance patient care coordination, Medalgnosis can integrate with existing electronic health record systems. This integration would enable seamless sharing of diagnostic reports and treatment recommendations with healthcare providers, facilitating a collaborative and patient-centric approach to care.
• AI-Driven Research and Clinical Trials: Medalgnosis' vast dataset of anonymized MRI scans and associated information can be leveraged for AI-driven research and clinical trials. The application's anonymized data repository could contribute to advancing brain tumor research, accelerating drug discovery, and facilitating the development of innovative treatment options.
• AI-Driven Radiomics and Prognostics: Medalgnosis can explore radiomics, which involves extracting quantitative features from MRI scans, to develop AI-driven prognostic models. These models could predict tumor growth rates, treatment responses, and patient outcomes, aiding in personalized treatment planning and long-term care.
• Mobile Application and Remote Diagnosis: Developing a mobile application for Medalgnosis would enable users to access tumor analysis and treatment recommendations conveniently on their smartphones. This mobile version could also facilitate remote diagnosis, allowing healthcare providers to reach underserved areas and offer expert consultations without geographical constraints.

Build with ❤️ by Harshavardhan and Tanisha